expectations ,risk premia and information spanning in dynamic term structure model estimation
投资学精要(博迪)(第五版)习题答案英文版chapter9&10
Essentials of Investments (BKM 5th Ed.)Answers to Suggested Problems – Lecture 7Bond Pricing Examples for Exam 3:Problem 9(a) in Chapter 9 provides an example of a bond price calculation (answer shown below). As additional examples, page 69 in your course packet provides several bond pricing problems for bonds with various maturity, yield, and coupon characteristics. The bond prices for these examples are as follows (note all bonds pay coupons semi-annually):8% coupon, 8% market yield, 10 years to maturity: B = $1,000.008% coupon, 10% market yield, 10 years to maturity: B = $875.388% coupon, 6% market yield, 10 years to maturity: B = $1,148.778% coupon, 8% market yield, 20 years to maturity: B = $1,000.008% coupon, 10% market yield, 20 years to maturity: B = $828.418% coupon, 6% market yield, 20 years to maturity: B = $1,231.156% coupon, 8% market yield, 10 years to maturity: B = $864.106% coupon, 10% market yield, 10 years to maturity: B = $750.766% coupon, 6% market yield, 10 years to maturity: B = $1000.00Chapter 9:4. Lower. Interest rates have fallen since the bond was issued. Thus, the bond is selling at apremium and the price will decrease (toward par value) as the bond approaches maturity.5. True. Under the Expectations Hypothesis, there are no risk premia built into bond prices.The only reason for an upward sloping yield curve is the expectation of increased short-term rates in the future.7. Uncertain. Liquidity premium will increase long-term yields, but lower inflationexpectations will reduce long-term yields compared to short-term rates. The net effect is uncertain.8. If the yield curve is upward sloping, you cannot conclude that investors expect short-terminterest rates to rise because the rising slope could either be due to expectations of future increases in rates or due to a liquidity premium.9. a) The bond pays $50 every 6 monthsCurrent price = $1052.42Assuming that market interest rates remain at 4% per half year:the price 6 months from now = $1044.52b) Rate of return = [1044.52 - 1052.42 + 50]/1052.42 = .04 or 4% per 6 months14. Zero 8% coupon 10% coupona) Current prices $463.19 $1,000 $1,134.20b) Price in 1 year $500.25 $1,000 $1,124.94change $37.06 $0.00 $-9.26PriceCouponincome $0.00 $80.00 $100.00$37.06 $80.00 $90.74incomeTotalRate of return 8.00% 8.00% 8.00%33. a) The forward rate, f, is the rate that makes rolling over one-year bonds equally attractiveas investing in the two-year maturity bond and holding until maturity:(1.08)(1 + f) = (1.09)2 which implies that f = 0.1001 or 10.01%b) According to the expectations hypothesis, the forward rate equals the expected shortrate next year, so the best guess would be 10.01%.c) According to the liquidity preference (liquidity premium) hypothesis, the forward rateexceeds the expected short-term rate for next year (by the amount of the liquiditypremium), so the best guess would be less than 10.01%.35. a. We obtain forward rates from the following table:Maturity(years)YTM Forward rate Price (for part c)($1000/1.10)1 10.0% $909.09[(1.112/1.10) – 1] $811.62 ($1000/1.112)12.01%2 11.0%[(1.123/1.112) – 1] $711.78 ($1000/1.123)14.03%3 12.0%b. We obtain next year’s prices and yields by discounting each zero’s face value at theforward rates derived in part (a):Maturity(years)Price YTM1 $892.78 [ = 1000/1.1201] 12.01%2 $782.93 [ = 1000/(1.1201 x 1.1403)] 13.02%Note that this year’s upward sloping yield curve implies, according to theexpectations hypothesis, a shift upward in next year’s curve.c.Next year, the two-year zero will be a one-year zero, and it will therefore sell at: ($1000/1.1201) = $892.78Similarly, the current three-year zero will be a two-year zero, and it will sell for $782.93. Expected total rate of return:two-year bond: %00.101000.0162.811$78.892$==− three-year bond: %00.101000.0178.711$93.782$==−37. d) 2e) 3f) 2g) 4Chapter 10:1. ∆∆B B D y y =−⋅+1 -7.194 * (.005/1.10) = -.03272.If YTM=6%, Duration=2.833 years If YTM=10%, Duration=2.824 years6.a) Bond B has a higher yield since it is selling at a discount. Thus, the duration of bond B is lower (it is less sensitive to interest rate changes).b) Bond B has a lower yield and is callable before maturity. Thus, the duration of bond B is lower (it is less sensitive to interest rate changes).9.a) PV = 10,000/(1.08) + 10,000/((1.08)2) = $17,832.65Duration = (9259.26/17832.65)*1 + (8573.39/17832.65)*2 = 1.4808 yearsb) A zero-coupon bond with 1.4808 years to maturity (duration=1.4808) would immunize the obligation against interest rate risk.c) We need a bond position with a present value of $17,832.65. Thus, the face value of thebond position must be:$17,832.65*(1.08)1.4808 = $19,985.26If interest rates increase to 9%, the value of the bond would be:$19,985.26/((1.09)1.4808) = $17,590.92The tuition obligation would be:10,000/1.09 + 10,000/((1.09)2) = $17,591.11or a net position change of only $0.19.If interest rates decrease to 7%, the value of the bond would be:$19,985.26/((1.07)1.4808) = $18,079.99The tuition obligation would be:10,000/(1.07) + 10,000((1.07)2) = $18,080.18or a net position change of $0.19.**The slight differences result from the fact that duration is only a linear approximationof the true convex relationship between fixed-income values and interest rates.11. a) The duration of the perpetuity is 1.05/.05 = 21 years. Let w be the weight of the zero-coupon bond. Then we find w by solving:w × 5 + (1 – w) × 21 = 1021 – 16w = 10w = 11/16 or .6875Therefore, your portfolio would be 11/16 invested in the zero and 5/16 in theperpetuity.b) The zero-coupon bond now will have a duration of 4 years while the perpetuity willstill have a 21-year duration. To get a portfolio duration of 9 years, which is now theduration of the obligation, we again solve for w:w × 4 + (1 – w) × 21 = 921 – 17w = 9w = 12/17 or .7059So the proportion invested in the zero has to increase to 12/17 and the proportion in theperpetuity has to fall to 5/17.12. a) The duration of the perpetuity is 1.1/.1 = 11 years. The present value of the payments is$1 million/.10 = $10 million. Let w be the weight of the 5-year zero-coupon bond andtherefore (1 – w) will be the weight of the 20-year zero-coupon bond. Then we find wby solving:w × 5 + (1 – w) × 20 = 1120 – 15w = 11w = 9/15 = .60Therefore, 60% of the portfolio will be invested in the 5-year zero-coupon bond and 40%in the 20-year zero-coupon bond.Therefore, the market value of the 5-year zero must be×.60 = $6 million.$10millionSimilarly, the market value of the 20-year zero must be$10× .40 = $4 millionmillionb) Face value of the 5-year zero-coupon bond will be× (1.10)5 = $9.66 million.$6millionFace value of the 20-year zero-coupon bond will be$4 million × (1.10)20 = $26.91 million.18. a) 4b) 4c)42d)21. Note that we did not discuss swaps in detail. For that reason, I would not expect you to beable to answer this type of question on the exam. The question is meant to provide youwith a brief summary of some potential motivations for swaps.a) a. This swap would have been made if the investor anticipated a decline in long-terminterest rates and an increase in long-term bond prices. The deeper discount, lowercoupon 6 3/8% bond would provide more opportunity for capital gains, greater callprotection, and greater protection against declining reinvestment rates at a cost of only amodest drop in yield.b. This swap was probably done by an investor who believed the 24 basis point yield spreadbetween the two bonds was too narrow. The investor anticipated that, if the spreadwidened to a more normal level, either a capital gain would be experienced on theTreasury note or a capital loss would be avoided on the Phone bond, or both. Also, thisswap might have been done by an investor who anticipated a decline in interest rates, andwho also wanted to maintain high current coupon income and have the better callprotection of the Treasury note. The Treasury note would have unlimited potential forprice appreciation, in contrast to the Phone bond which would be restricted by its callprice. Furthermore, if intermediate-term interest rates were to rise, the price decline ofthe higher quality, higher coupon Treasury note would likely be “cushioned” and thereinvestment return from the higher coupons would likely be greater.c. This swap would have been made if the investor were bearish on the bond market. Thezero coupon note would be extremely vulnerable to an increase in interest rates since theyield to maturity, determined by the discount at the time of purchase, is locked in. This isin contrast to the floating rate note, for which interest is adjusted periodically to reflectcurrent returns on debt instruments. The funds received in interest income on the floatingrate notes could be used at a later time to purchase long-term bonds at more attractiveyields.d. These two bonds are similar in most respects other than quality and yield. An investorwho believed the yield spread between Government and Al bonds was too narrow wouldhave made the swap either to take a capital gain on the Government bond or to avoid acapital loss on the Al bond. The increase in call protection after the swap would not be afactor except under the most bullish interest rate scenarios. The swap does, however,extend maturity another 8 years and yield to maturity sacrifice is 169 basis points.e. The principal differences between these two bonds are the convertible feature of the Zmart bond and the yield and coupon advantage, and the longer maturity of the LuckyDucks debentures. The swap would have been made if the investor believed somecombination of the following: First, that the appreciation potential of the Z martconvertible, based primarily on the intrinsic value of Z mart common stock, was nolonger as attractive as it had been. Second, that the yields on long-term bonds were at acyclical high, causing bond portfolio managers who could take A2-risk bonds to reach forhigh yields and long maturities either to lock them in or take a capital gain when ratessubsequently declined. Third, while waiting for rates to decline, the investor will enjoyan increase in coupon income. Basically, the investor is swapping an equity-equivalentfor a long- term corporate bond.23. Choose the longer-duration bond to benefit from a rate decrease.a) The Aaa-rated bond will have the lower yield to maturity and the longer duration.b) The lower-coupon bond will have the longer duration and more de facto call protection.c) Choose the lower coupon bond for its longer duration.30. The price of the 7% bond in 5 years is:PVA(C=$70, N=25, r=8%) + PV($1000, N=25, r=8%) = $893.25You also get five $70 coupon payments four of which can be reinvested at 6% for a total of $394.59 in coupon income.HPR = ($893.25 - 867.42 + 394.59)/867.42 = 48.47%The price of the 6.5% bond in 5 years is:PVA(C=$65, N=15, r=7.5%) + PV($1000, N=15, r=7.5%) = $911.73You also get five $65 coupon payments four of which can be reinvested at 6% for a total of $366.41 in coupon income.HPR = ($911.73 - 879.50 + 366.41)/879.50 = 45.33%**The 7% bond has a higher 5-year holding period return.。
金融建模动量因子
您对动量因子的理解是什么呢？请大家积极回帖，一起来探讨！自从Jegadeesh和Titman首先在1993年Journal of Finance上发表了动量因子（Momentum Factor）的研究成果之后(Jegadeeshand Titman, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, 1993，简而言之，动量因子就是采取逢高买进，逢低卖出的策略所取得的回报)，由于它显著的超额回报率(market excess return)，数十年来一直是学术界经久不衰的研究课题之一，研究范围包括各个资本市场，各种资产类型，各种时间跨度。
例如：•关于S&P的: Style Momentum Within the S&P 500 Index (Chen and De Bondt, 2004)和Cross-Asset StyleMomentum (Kim,2010)•美国行业/板块: Do Industries ExplainMomentum? (Moskowitz and Grinblatt,1999),Understanding the Nature of the Risks andSources of Rewards to Momentum Investing (GrundyandMartin, 1998)•美国小盘股: Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of MomentumStrategies (Hong et al, 1999)•欧洲股票市场: International MomentumStrategies,(Rouwenhorst, 1997)•英国股票市场: The Profitability of Momentum Investing, (Lui et al, 1999),Momentum in the UK Stock Market (Hon and Tonks,2001)•中国股票市场: Contrarian and Momentum Strategiesin the China Stock Market: 1993-2000 (Kang et al,2002), The “Value” Effect and the Market for ChineseStocks (Malkiel and Jun, 2009), Momentum andSeasonality in Chinese Stock Markets (Li, Qiu, and Wu, 2010) 和Momentum Phenomenon in the Chinese Class A and B Share Markets (Choudhry and Wu, 2009)•日本股票市场: Eureka! A Momentum Strategy that Also Works in Japan (Chaves , 2012)•澳洲股票市场: Do Momentum Strategies Work?: Australia Evidence, (Drew, Veeraraghavan, and Ye, 2004)•瑞士股票市场: Momentum and IndustryDependence (Herberger, Kohlert, and Oehler, 2009)•新兴股票市场: Local Return Factors and Turnover in Emerging Stock Markets, (Rouwenhorst, 1999)•前沿新兴股票市场: The Cross-Section of Stock Returns in Frontier Emerging Markets (Groot, Pang, and Swinkels,2012)•全球股票市场: Momentum Investing and Business Cycle Risk: Evidence from Pole to Pole, (Griffin et al,2002), International Momentum Strategies (Rouwenhoust, 1998), The Case for Momentum (Berger, Isael, Moskowitz, 2009)•外汇市场: Do Momentum Based Strategies Still Work In Foreign Currency Markets? (Okunev and White,2003), Interaction between Technical Currency Trading and Exchange Rate Fluctuations (Schulmeister,2006), Momentum in Stock Market Returns: Implications for Risk Premia on Foreign Currencies (Nitschka,2010), 和Currency Momentum Strategies (Menkhoff et al, 2011)•大宗商品市场: Momentum Strategies in Commodity Futures Markets (Miffre and Rallis, 2007), The Strategicand Tactical Value of Commodity Futures (Erb and Harvey, 2006)•技术分析: 52-Week High and MomentumInvesting (Georgeand Hwang, 2004).•公司盈利: Momentum Strategies (Chan et al,2006), Firm-specific Attributes and the Cross-section ofMomentum (Sagi and Seasholes, 2007)•在时间维度上: Market States and Momentum (Cooper, Gutierrezand Hameed, 2003),Time-Varying MomentumProfitability (Wang and Xu, 2010), Time SeriesMomentum (Moskowitz et al, 2011), 212 Years of PriceMomentum (Gezcy, 2013), A Century of Evidence onTrend Following (Hurst, Ooi, Pedersen, 2012), TwoCenturies of Trend Following (Lempérière, 2014).还有各种从价格动量 (price momentum)衍生出的变体，例如：•“新鲜”动量：Fresh Momentum (Chen, Kadan and Kose, 2009)•“残余”动量：Residual Momentum (Blitz, Huij and Martens, 2011)•CAPM/Fama-French“残余”动量：Some Tricks to Momentum (SocGen, 2012)•“双重”动量：Risk Premia Harvesting Through Dual Momentum (Antonacci,2013)•“共同”动量: Comomentum: Inferring Arbitrage Activity from Return Correlations (Lou and Polk, 2012)•趋势因子: Trend Factor: A New Determinant ofCross-Section Stock Returns (Han and Zhou, 2013)在跨多种资产的研究中，人们通常把动量因子（Momentum Factor）和价值因子（Value Factor）放在一起研究，例如: Global Tactical Cross-AssetAllocation: Applying Value and Momentum Across Asset Classes (Blitz and VanVliet, 2007), Value and Momentum Everywhere (Asness, Moskowitz, and Pedersen,2009), Using aZ-score Approach to Combine Value and Momentum in Tactical Asset Allocation (Wang and Kohard, 2012), 和Size, Value, and Momentum in International Stock Returns (Fama and French, 2011)也有和反转（Reversal/Mean Reversion）一起研究，例如：Momentum–Reversal Strategy (Yu and Chen, 2011), An Institutional Theory of Momentumand Reversal (Vayanos and Woolley,2010), Momentum and Mean Reversion across National Equity Markets (Balvers and Wu, 2006), Macromomentum: Returns Predictability in International Equity Indices (Bhojraj, 2001)至于动量因子产生的原因至今没有定论，投资者的行为偏差（behavior bias）算是其中一个，主要体现在投资者对于自己掌握的信息过于自信，从而导致资产价格对于新信息反应不足（underreaction）: Investor Psychology and Security Market Under-andOver-Reactions(Daniel, Hirshleifer, Subrahmanyam,1998), Overconfidence, Arbitrage, and Equilibrium Asset Pricing (Daniel, Hirshleifer, Subrahmanyam,2001)其他类似的解释例如：When are Contrarian Profits Due to Stock Market Overreaction? (Lo and Mackinlay, 1990), A Model of Investor Sentiment (Barberis,Shleifer, Vishny, 1997), A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets (Hong and Stein, 1997), Price Momentum andTrading Volume (Lee and Swaminathan, 1998),Underreactions and Overreactions:The Influence of Information Reliability and Portfolio Formation Rules (Bloomfieldet al, 1998), Rational Momentum Effects (Johnson, 2002)除此之外，还有从其他不同角度进行解释的，例如:•交易成本(Trading Cost): The Illusory Nature of Momentum Profits (Lesmond, Schill, and Zhou,2004), Trading Cost of Asset Pricing Anomalies (Frazzini, Israel and Moskowitz,2012)•横截面预期收益(Cross-sectional ExpectedReturns):Momentum is Not an Anomaly(Dittmar et al, 2007)•知情交易(Informed Trading): Momentum and Informed Trading (Hameed et al, 2008)•市场情绪(Sentiment): Sentiment and Momentum(Doukas et al, 2010)•经济周期 (Business Cycle): Momentum, Business Cycle, and Expected Returns(Chordia and Shivakumar,2002)•文化差异 (Cultural Difference): Individualism and Momentum around the World (Chui, Titman and Wei,2009)•过度协方差(Excess Covariance): Momentum and Autocorrelationin Stock Returns(Lewellen, 2002)•避税 (Tax Loss Harvesting): PredictingStock Price Movements from Past Returns: The Role of Consistency and Tax-LossSelling (Grinblatt and Moskowitz, 2004)•宏观风险溢价(Macroeconomic Risk Premium): Momentum Profits, Factor Pricing and Macroeconomic RiskFactor (Zhang, 2008)•前景理论(Prospect Theory ): Prospect Theory, Mental Accounting, and Momentum(Grinblatt and Han,2004)•处置效应(Disposition Effect): The Disposition Effect and Underreaction to News(Frazzini, 2006)，其中前景理论与处置效应均指投资者在处理股票时，倾向卖出赚钱的股票、继续持有赔钱的股票。
收入操纵、舞弊审计准则与审计报告谨慎性
ACCOUNTING LEARNING151收入操纵、舞弊审计准则与审计报告谨慎性文/陈达华摘要：本文分析了收入操纵、舞弊会计准则和注册会计师审计报告谨慎程度之间的关系。
关键词：收入操纵、舞弊审计准则；审计报告谨慎性一、引言在注册会计师审计中，被审计单位制作虚假财务报告、引起重大错报风险大多是通过多计收入或者少计收入来实现的。
因此，注册会计师在审计中应该秉承收入方面存在舞弊风险原则，并以此为原则去判断哪些类型收入容易发生舞弊风险、容易导致交易或者认定层次的重大错报。
收入在企业财务舞弊中是非常常用的一种手段和方法，对财务报告的影响是广泛和重大的，并且收入舞弊往往具有不易察觉性。
通常来说审计目标和审计准则项目中不对具体的某项财务报表项目做出细致的规范和要求，但是鉴于收入确认在财务报表的重要性和舞弊手段的多样性与不易察觉性，国际审计准则专门对收入可能伴随的风险进行了强调，美国公众公司会计监督委员会也将收入作为注册会计师执业过程中必须关注的高风险领域。
我国审计准则近年来秉承和国际会计准则趋同的原则，因此引入了相同原则，引导注册会计师高度重视与收入相关的舞弊风险。
在这里有一个问题值得我们重点关注：在审计准则中特别强调某类风险会产生什么样的效果？注册会计师是否关注到了审计准则中专门强调的收入舞弊风险。
为此，我们应该从如下两点思考：第一，作为一个舞弊频发、通常引起重大错报风险的领域，收入审计值得注册会计师投入更多的资源。
目前尚没有明确的证据证明注册会计师在执业时针对收入确认做出特别风险应对措施。
而在对舞弊未来方向的研究中，我们有必要去思考注册会计师如何去思考评估和应对收入确认和其他特别风险，这有利于其在审计过程中提升应对舞弊风险的能力。
第二，审计准则对注册会计师实务具有理论上的直接影响。
但是笔者在调查中发现这种影响效果难以具体识别出来，因为审计准则中的要求大部分是原则性的，实证检验中难以将注册会计师的某项具体行为归到特定条款之中去，进而具体某一项审计准则的效用是难以体现出来的。
英语判断题
“三大模块”题库（英语判断题）1. The capital ratios expected to be maintained by all banks are 4% for Tier 1 and 8% for Total capital. False2. In all G-10 countries, the minimum ratios for Tier 1 and Total capital are 6% and 10%, respectively. False3. The Basel I minimum Tier 1 and Tier 2 capital ratios are each 4%. False4. The Basel I minimum Tier 1 ratio is 4% and the minimum Total capital ratio is 8%. True8. According to the corporation law, the company structure includes the shareholders, the board of directors and the supervisory board.（模块三电子版P357）答：错10. Huge loss of bank deposits can be considered as the internal signal of early-warning signals of liquidity risk. (Wrong)（错）参考来源：（模块三P269）13. Economic capital is an economic concept that is used to describe a bank's ability to cover the expected loss.Answer : False 《商业银行主要业务》P453 判断第一题17. Low-value consumption goods a corporation has can not be used as debt assets. Wrong18. The necessary condition for a bank to issue financial bond is core capital adequacy ratio more than 4%.Right19. The bank business of bill acceptance and discount should have its real trade background.Right20. The efficiency standard regarding public finance in terms of resource replacement is the maximization the government investment return.Wrong21. Economic capital can be allocated to a specific transaction.Right22. Economic capital is the minimum amount of money an institution can lose over a specified time horizon.Wrong25. True or false: options give buyers the obligation to buy or sell an asset at a present price over a specific period.答案：False出处：《商业银行主要业务》P 40726. Futures contracts usually end in the delivery of the underlying commodity.答案：False出处：《商业银行主要业务》P 39429. When the aggregate demand is beyond the aggregate supply, the economy is in balance. （Answer: False）30. At the present time，supervise method of Chinese banking is Risk-based Supervision. （Answer: False）31. The purpose of foreign exchange futures hedging is to avoid or reduce funds’ risk，to ensure the value of foreign currency assets。
糖尿病护理英文
Managing blood pressure and cholesterol levels to reduce the risk of cardiovascular diseases
Lifestyle modification
Encouraging healthy eating habits, regular physical activity, smoking process, and stress management
Self monitoring of blood glucose
Training patients to use glucose and interpret their results to adjust their treatment plan accordingly
Insulin administration
Epidemiology and Risk Factors
Risk Factors Family history of diamonds
Obesity and physical inactivity
Epidemiology and Risk Factors
Advanced age Ethnicity (cervical radial and ethical groups have higher risks)
For patients requiring insulin therapy, teaching property injection techniques, storage methods, and dosage adjustment
Preventing complications
Risk%20Premia%20II
3
Mean-Variance Bounds
• In terms of expected returns, we obtain:
Et {Rt +1} < R f t ,t +1 + R f t ,t +1 ⋅ σ R ⋅ σ m
Et {Rt +1} > R f t ,t +1 − R f t ,t +1 ⋅ σ R ⋅ σ m
• From the reasonably nice expression for the variance, take root to find the volatility of xt+1-γ:
σ (xt +1 ) = exp − 2γµ x + γ 2σ x 2 ⋅ [exp{ γ 2σ 2 x }− 1]
{ E {x
− 2γ
t +1
} }= exp − 2γµ
where
ln xt +1 ~ N µ x , σ 2 x
(
4γ 2 2 σ x x + 2
2
)
9
Taking the Model to the Data
• Variance of the pricing kernel σ2(mt+1):
1 !!! R f t ,t +1
γ2
{
}
Æ Somewhat nicer expression for the volatility of the pricing kernel:
σ (m t +1 ) =
1 2 exp γ 2σ x − 1 3 R f t , t +1 1 442 4 4
Stakeholder Identification
Stakeholder Expectations Definition Process
Stakeholder Expectation Definition Purpose and Importance • The Stakeholder Expectation Definition Process is used to:
– Identify who Stakeholders are (for all phases of the project) – Identify how the product is intended to be used by the stakeholders – Elicit and define stakeholder expectations (including use cases, scenarios, operational concepts) in ways we can measure (validate) the appropriateness of our system when completed.
– Other interested parties who provide broad overarching constraints within which the customers’ needs must be achieved, or who have influence on success of the system. Examples:
Stakeholder Expectations Definition Best Practice Process Flow Diagram
Activities Input Output
What are the Benefits of the Stakeholder Expectations Process?
风险术语的英文对照
风险术语的英文对照1. Risk Assessment - 风险评估2. Risk Management - 风险管理3. Risk Mitigation - 风险缓解4. Risk Identification - 风险识别5. Risk Analysis - 风险分析6. Risk Control - 风险控制7. Risk Response - 风险应对8. Risk Avoidance - 风险避免9. Risk Transfer - 风险转移10. Risk Tolerance - 风险容忍度11. Risk Probability - 风险概率12. Risk Impact - 风险影响13. Risk Assessment Matrix - 风险评估矩阵14. Risk Register - 风险登记册15. Risk Treatment Plan - 风险处理计划16. Risk Exposure - 风险暴露度17. Risk Control Measures - 风险控制措施18. Risk Indicator - 风险指标19. Risk Communication - 风险沟通20. Risk Event - 风险事件请注意，上述术语仅提供参考，具体的风险管理术语可能根据行业和上下文有所不同。
Risk management is an essential component of any organization, as it involves the identification, assessment, and mitigation of potential risks that could impact the achievement of objectives. In order to effectively manage risks, itis crucial to have a clear understanding of various risk terminologies and their corresponding translations in English.Risk assessment, or 风险评估, is the process of identifying and evaluating potential risks to determine their likelihood and potential impact. This involves analyzing the probability of a risk occurring and assessing the potential consequences it could have on the organization. Risk assessments are typically conducted using various tools and techniques such as risk matrices, scenario analysis, and historical data.Once risks have been identified and assessed, the organization can proceed with risk management, or 风险管理. This involves developing strategies and action plans to minimize or eliminate the identified risks. Risk management aims to reduce the likelihood of a risk occurring or its potential impact if it does occur. It includes risk mitigation, or 风险缓解, which involves implementing measures to reduce the probability and/or severity of a risk.Risk identification, or 风险识别, is the process of identifying potential risks that could impact the organization's objectives. This includes analyzing internal and external factors that could lead to risks, such as changes in regulations, market volatility, or operational vulnerabilities. Risk analysis, or 风险分析, is the process of evaluating the identified risks to determine their potential impact and prioritize their treatment.Risk control, or 风险控制, involves implementing measures to reduce or manage the identified risks. This includes developingand implementing risk control measures, such as implementing safety protocols, conducting regular inspections, or implementing redundancy measures. Risk response, or 风险应对, refers to the actions taken by the organization to address identified risks. This could include accepting the risk, avoiding the risk, transferring the risk to a third party, or implementing measures to mitigate the risk.Risk avoidance, or 风险避免, refers to the strategy of completely eliminating the exposure to a particular risk. This could involve making changes to business processes, discontinuing certain activities, or avoiding certain markets or investments. Risk transfer, or 风险转移, involves transferring the responsibility and financial implications of a risk to another party, such as purchasing insurance coverage.Risk tolerance, or 风险容忍度, refers to the level of risk that an organization is willing to accept in order to achieve its objectives. This involves striking a balance between maximizing opportunities and minimizing potential risks. Risk probability, or 风险概率, refers to the likelihood or chance of a risk occurring. Risk impact, or 风险影响, refers to the magnitude of the consequences that would result if a risk were to occur.A risk assessment matrix, or 风险评估矩阵, is a tool used to evaluate and prioritize risks based on their likelihood and impact. It provides a visual representation of risks and helps in determining appropriate risk management strategies. A risk register, or 风险登记册, is a document that records all identified risks, along with their likelihood, potential impact, and mitigation measures.To implement effective risk management, organizations develop risk treatment plans, or 风险处理计划, which outline the specific actions to be taken to manage identified risks. These plans include a clear description of the risk, its potential impact, the desired risk treatment strategy, and the individuals responsible for its implementation.Risk exposure, or 风险暴露度, refers to the level of vulnerability or susceptibility of the organization to a particular risk. It considers the organization's potential financial, operational, and reputational losses resulting from a risk event. Risk control measures, or 风险控制措施, are actions implemented to mitigate or prevent identified risks. These measures may include implementing internal controls, conducting training programs, or investing in technologies to mitigate risks.Risk indicators, or 风险指标, are quantitative or qualitative measures used to monitor and assess risks. These indicators help in identifying early warning signs of emerging risks, enabling timely and proactive risk management. Risk communication, or 风险沟通, refers to the process of sharing information about risks within the organization or with external stakeholders. Effective risk communication is crucial for ensuring that everyone understands the risks, their potential impact, and the organization's strategiesfor managing them.Overall, understanding and utilizing risk terminologies in both English and their native language is vital for effective riskmanagement. It ensures clear communication, facilitates collaboration, and enhances the organization's ability to identify, assess, and mitigate risks. By effectively managing risks, organizations can safeguard their interests, minimize losses, and enhance their overall performance and resilience.。
it项目管理课后作业参考答案
Chapter-11. What are the advantages of using formal project management?Answer: Using project management provides advantages, such as:a) Better control of financial, physical, and human resourcesb) Improved customer relationsc) Shorter development timesd) Lower costse) Higher quality and increased reliabilityf) Higher profit marginsg) Improved productivityh) Better internal coordinationi) Higher worker morale2. What are the triple constraints of project management?Answer: The three constraints of project management are Scope, Time, Cost.Scope: What work will be done?Time: How long should it take to complete? [Schedule]Cost: What should it cost? [Budget]In order to meet the high Quality, the project manager should balance these three often-competing goals.3. What is Project Management?Answer: Project management is “the application of knowledge, skills, tools and techniques to project activities to meet project requirement.”4. Define Project stakeholders?Answer:Stakeholders are the people involved in or affected by project activities.Stakeholders include:●Project sponsor●Project manager●Project team●Support staff●Customers●Users●Suppliers●Opponents to the project5. Explain the Nine Project Management Knowledge Areas?Answer:●Project scope management to identify and manage the successfulcompletion of the project have to do all the work。
4-06市场效率
Copyright © 2009 Pearson Prentice Hall. All rights reserved.
6-12
Evidence on Efficient Market Hypothesis
• Favorable Evidence
1. Investment analysts and mutual funds don't beat the market
– The Efficient Market Hypothesis – Evidence on the Efficient Market Hypothesis – Behavioral Finance
Copyright © 2009 Pearson Prentice Hall. All rights reserved.
1. It implies that in an efficient capital market, one investment is as good as any other because the securities’ prices are correct.
2. It implies that a security’s price reflects all available information about the intrinsic value of the security.
4. Technical analysis does not outperform market
Copyright © 2009 Pearson Prentice Hall. All rights reserved.
6-13
Evidence in Favor of Market Efficiency
国外关于风险计算的书籍
国外关于风险计算的书籍以下是一些关于风险计算方面的国外书籍，每本书籍都提供了有关内容的简要介绍：1. "Risk Analysis: A Quantitative Guide" by David Vose2. "Quantitative Risk Management: Concepts, Techniques and Tools" by Alexander J. McNeil, Rudiger Frey, and Paul Embrechts 这本书提供了风险管理的定量方法和工具的综合指南。
它介绍了各种风险建模技术，包括随机过程、概率论和统计方法，并向读者展示如何使用这些工具来评估和管理金融和非金融风险。
3. "Risk Assessment: Theory, Methods, and Applications" by Marvin Rausand and Arnljot Høyland这本教材是的综合指南，旨在帮助读者理解和应用风险评估的理论、方法和工具。
它介绍了常用的风险分析方法，包括事件树和故障树分析、可靠性分析和风险矩阵方法，并提供了一些实际的应用案例。
4. "Economic Risk in Hydrocarbon Exploration" by Ian Lerche5. "Risk Assessment and Decision Analysis with Bayesian Networks" by Norman Fenton and Martin Neil6. "Monte Carlo Methods in Financial Engineering" by Paul Glasserman该书专注于金融工程领域的风险计算方法。
它介绍了蒙特卡罗模拟的基本原理，并探讨了如何使用这一方法来评估和管理金融市场的风险。
金融学期刊(JF)创刊以来50篇最经典论文
金融学期刊（JF）创刊以来50篇最经典论文1) Portfolio Selection证券组合选择Harry Markowitz Volume 7, Issue 1March 19522) Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk资本资产价格：风险条件下的市场均衡理论William F. SharpeVolume 19, Issue 3September 19643) Efficient Capital Markets: Review Of Theory And Empirical Work有效市场：理论与经验研究的评论Eugene F. FamaVolume 25, Issue 2May 19704) The Cross-Section Of Expected Stock Returns股票预期收益的横截面（分析）Eugene F. Fama, Kenneth R. FrenchVolume 47, Issue 2June 19925) Counterspeculation, Auctions, And Competitive Sealed Tenders反投机、拍卖和竞争性密封投标William VickreyVolume 16, Issue 1March 19616) A Survey Of Corporate Governance关于公司治理的调查Andrei Shleifer, Robert W. VishnyVolume 52, Issue 2June 19977) Legal Determinants Of External Finance外部融资的法律层面决定因素Rafael La Porta, Florencio Lopez-De-Silanes, Andrei Shleifer, Robert W. VishnyVolume 52, Issue 3July 19978) Corporate Ownership Around The World世界各地的企业所有权Rafael La Porta, Florencio Lopez-De-Silanes, Andrei ShleiferVolume 54, Issue 2April 19999) On the Pricing Of Corporate Debt: The Risk Structure Of Interest Rates 企业债务的定价：利率的风险结构Robert C. MertonVolume 29, Issue 2May 197410) Financial Ratios, Discriminant Analysis And Prediction Of Corporate Bankruptcy财务比率，判别分析和企业破产预测Edward I. AltmanVolume 23, Issue 4September 196811) The Modern Industrial Revolution, Exit, And The Failure Of Internal Control-Systems近代工业革命，退出，和内部控制系统的失灵Michael C. JensenVolume 48, Issue 3July 199312) On Persistence In Mutual Fund Performance共同基金绩效的持久性Mark M. CarhartVolume 52, Issue 1March 199713) On The Relation Between The Expected Value And The Volatility Of The Nominal期望值和名义股票超额回报波动性的关系Lawrence R. Glosten, Ravi Jagannathan, David E. RunkleVolume 48, Issue 5December 199314) Returns To Buying Winners And Selling Losers: Implications For Stock Market Efficiency购买赢家和卖掉输家的回报：对股市效率的启示Narasimhan Jegadeesh, Sheridan TitmanVolume 48, Issue 1March 199315) Informational Asymmetries, Financial Structure, And Financial Intermediation信息不对称、金融结构和金融中介Hayne E. Leland, David H. PyleVolume 32, Issue 2May 197716) The Pricing Of Options On Assets With Stochastic Volatilities具有随机波动性资产期权定价John Hull, Alan WhiteVolume 42, Issue 2June 198717) Efficient Capital Markets: II有效资本市场IIEugene F. FamaVolume 46, Issue 5December 199118) Does The Stock Market Overreact?股市反应过度了吗？Werner F. M. De Bondt, Richard ThalerVolume 40, Issue 3July 198519) Multifactor Explanations Of Asset Pricing Anomalies对资产定价异常现象的多因素解释Eugene F. Fama, Kenneth R. FrenchVolume 51, Issue 1March 199620) The Capital Structure Puzzle资本结构之谜Stewart C. MyersVolume 39, Issue 3July 198421) The Performance Of Mutual Funds In Period 1945-1964共同基金在1945年-1964年的绩效Michael C. JensenVolume 23, Issue 2May 196822) Debt And Taxes负债和税收Merton H. MillerVolume 32, Issue 2May 197723) What Do We Know About Capital Structure? Some Evidence From International Data我们对资本结构了解多少呢？来自国际数据的一些证据Raghuram G. Rajan, Luigi ZingalesDecember 199524) The Benefits Of Lending Relationships: Evidence From Small Business Data借贷关系的好处：来自小企业数据的证据Mitchell A. Petersen, Raghuram G. RajanVolume 49, Issue 1March 199425) Measuring And Testing The Impact Of News On Volatility衡量和检验新闻对波动性的影响Robert F. Engle, Victor K. NgVolume 48, Issue 5December 199326) Investor Psychology And Security Market Under- And Overreactions投资者心理和证券市场的过度反应/反应迟钝Kent Daniel, David Hirshleifer, Avanidhar SubrahmanyamVolume 53, Issue 6December 199827) Contrarian Investment, Extrapolation, And Risk逆向投资、外推法和风险Josef Lakonishok, Andrei Shleifer, Robert W. VishnyVolume 49, Issue 5December 199428) A Simple Model Of Capital Market Equilibrium With Incomplete Information 一个简单的信息不完全的资本市场均衡模型Robert C. MertonVolume 42, Issue 3July 198729) Insiders And Outsiders: The Choice Between Informed And Arms-Length DebtRaghuram G. RajanSeptember 199230) Why Does Stock Market Volatility Change Over Time? 为什么随着时间推移股市波动性会发生变化？G. William SchwertVolume 44, Issue 5September 198931) The Determinants Of Capital Structure Choice资本结构决策的决定因素Sheridan Titman, Roberto WesselsVolume 43, Issue 1March 198832) Inferring Trade Direction From Intraday Data从当日数据推断交易方向Charles M.C. Lee, Mark J. ReadyVolume 46, Issue 2June 199133) The New Issues Puzzle新股发行之谜Tim Loughran, Jay R. RitterVolume 50, Issue 1March 199534) The Limits Of Arbitrage套利限制Andrei Shleifer, Robert W. VishnyVolume 52, Issue 1March 199735) Noise噪声Fischer BlackVolume 41, Issue 3July 198636) Investor Protection And Corporate Valuation投资者保护和公司估值Rafael La Porta, Florencio Lopez-De-Silanes, Andrei Shleifer, Robert Vishny Volume 57. Issue 3June 200237) The Theory Of Capital Structure资本结构理论Milton Harris, Artur RavivVolume 46, Issue 1March 199138) The Long-Run Performance Of Initial Public Offerings首次公开发行股票的长期绩效Jay R. RitterVolume 46, Issue 1March 199139) Initial Public Offerings And Underwriter Reputation首次公开发行和承销商商誉Richard Carter, Steven ManasterVolume 45, Issue 4September 199040) Dividend Policy Under Asymmetric Information信息不对称下的股利政策Merton H. Miller, Kevin RockVolume 40, Issue 4September 198541) A Simple Implicit Measure Of The Effective Bid-Ask Spread In An Efficient Market买卖价差(Bid-Ask Spread)有效市场下有效买卖差价的简单内隐测量Richard RollSeptember 198442) Empirical Performance Of Alternative Option Pricing Models备选的期权定价模型的实证绩效Gurdip Bakshi, Charles Cao, Zhiwu ChenVolume 52, Issue 5December 199743) Compensation And Incentives: Practice vs. Theory薪酬和激励：实践和理论George P. Baker, Michael C. Jensen, Kevin J. MurphyVolume 43, Issue 3July 198844) Are Investors Reluctant To Realize Their Losses?投资者不愿意意识到他们的损失？Terrance OdeanVolume 53, Issue 5October 199845) Size And Book-To-Market Factors In Earnings And Returns盈余和收益的规模和账面-市值因素Eugene F. Fama, Kenneth R. FrenchVolume 50, Issue 1March 199546) Security Prices, Risk, And Maximal Gains From Diversification证券价格、风险和来自多元化的最大收益John LintnerVolume 20, Issue 4December 196547) An Empirical Comparison Of Alternative Models Of The Short-Term Interest-Rate对短期利率备选模型的实证比较K. C. Chan, G. Andrew Karolyi, Francis A. Longstaff, Anthony B. SandersJuly 199248) Risk Management Coordinating Corporate Investment And Financing Policies风险管理协调企业投融资政策Kenneth A. Froot, David S. Scharfstein, Jeremy C. SteinVolume 48, Issue 5December 199349) Disentangling The Incentive And Entrenchment Effects Of Large Shareholdings解析大股东激励和壕沟防御效应Stijn Claessens, Simeon Djankov, Joseph P. H. Fan, Larry H. P. Lang Volume 57, Issue 6December 200250) Valuing Corporate Securities: Some Effects Of Bond Indenture Provisions 公司证券估值：债券契约条款的一些作用Fischer Black, John C. CoxVolume 31, Issue 2May 1976。
环境风险评估的原则
managers) 对风险管理者增加风险评估的透明度 provide standardized tools & techniques 提供统一标准的工具和技术 dispel perception that ecological risk assessment is impossible 推翻认为环境风险评估不可能的错误观点
Data Acquisition, Verification, & Monitoring 数据的获得, 证明, 和监控
Problem Formulation
问题定义
Risk Assessment 风险评估
Exposure & effects Characterization 暴露和结果描述
Risk Characterization
a road map 一个总体的计划
Goals of ERA Framework 环境风险评估结构的目标
(Bartenhouse, 2006)
develop a unified conceptual approach to environmental assessment 发展一个统一概念性方法来评估环境
Guidelines for Ecological Risk Assessment Risk Assessment Forum, USEPA (1998) EPA/630/R95/002F
预期与展望 英语作文
预期与展望英语作文Title: Expectations and Prospects。
In life, expectations and prospects serve as guiding lights, shaping our actions, decisions, and aspirations. They are the fuel that propels us forward, driving us towards our goals and dreams. Whether in personal endeavors or professional pursuits, understanding our expectations and prospects enables us to navigate through life with purpose and determination.First and foremost, expectations play a crucial role in defining our path. They are the envisioned outcomes we anticipate from our efforts and endeavors. These expectations may stem from various sources, including societal norms, familial values, personal aspirations, or professional ambitions. For instance, a student may expect to excel academically, aiming for top grades and recognition. Similarly, an entrepreneur may envision building a successful business empire, driven by theexpectation of financial prosperity and societal impact.However, expectations alone are insufficient without a clear understanding of the prospects they entail. Prospects encompass the potential opportunities, challenges, and possibilities that lie ahead. They provide a realistic assessment of what can be achieved and the obstacles that may need to be overcome. For instance, while a student may expect academic excellence, the prospects may include rigorous study schedules, challenges in grasping complex concepts, and competition from peers. Similarly, an entrepreneur may anticipate business success, but the prospects may involve market fluctuations, resource constraints, and evolving consumer demands.Despite the inherent challenges, acknowledging both expectations and prospects empowers individuals to chart their course effectively. It fosters resilience, adaptability, and strategic thinking, essential qualities for navigating the complexities of life. By aligning expectations with realistic prospects, individuals can set achievable goals, develop robust plans, and persevere inthe face of adversity.Furthermore, expectations and prospects are not static but evolve over time, influenced by experiences, circumstances, and external factors. As individualsprogress through different stages of life, their expectations may undergo refinement, and new prospects may emerge. For instance, a student transitioning to the professional realm may recalibrate expectations from academic success to career advancement, accompanied by prospects of skill development, networking, andprofessional growth.Moreover, societal changes, technological advancements, and global dynamics continuously reshape expectations and prospects across various domains. In the realm of technology, the rapid pace of innovation generates new expectations for convenience, efficiency, and connectivity, while presenting prospects for groundbreaking discoveries and transformative solutions. Similarly, in the socio-economic sphere, shifting demographics, geopolitical shifts, and environmental concerns redefine expectations forsustainable development, social equity, and global cooperation, alongside prospects for inclusive growth, environmental stewardship, and peacebuilding.In conclusion, expectations and prospects are intrinsic to the human experience, guiding our journey through life's myriad challenges and opportunities. By understanding and embracing them, individuals can cultivate resilience, adaptability, and purposeful action. Whether in personal aspirations or societal endeavors, the synergy between expectations and prospects fuels progress, fosters innovation, and paves the way for a brighter future. As we navigate the complexities of an ever-changing world, let us harness the power of expectations and prospects to chart a course towards fulfillment, success, and collective prosperity.。
积极应对风险 英语
积极应对风险英语In the face of risks, it is important to adopt a proactive approach to mitigate potential negative impacts. There are several strategies that can be employed to effectively manage risks and ensure a positive outcome.Firstly, it is essential to conduct a thorough risk assessment. This involves identifying potential risks and evaluating their likelihood and potential impact. By understanding the nature of the risks, appropriate measures can be implemented to address them. This may involve creating contingency plans, setting up early warning systems, or establishing risk management protocols.Furthermore, it is crucial to maintain open communication and transparency within the organization. By fostering a culture of risk awareness and responsibility, employees are more likely to identify and report potential risks in a timely manner. This can help to prevent small issues from escalating into major problems.In addition, it is important to stay informed about external factors that may pose risks to the organization.This includes staying updated on industry trends,regulatory changes, and geopolitical developments. Bystaying ahead of potential risks, organizations can adapt their strategies and operations to minimize negative impacts.Moreover, it is beneficial to diversify and spread out risks. This can be achieved through various means, such as investing in different markets, maintaining a balanced portfolio, or establishing partnerships with multiple suppliers. By spreading out risks, organizations can avoid being heavily impacted by a single event or factor.Lastly, it is important to continuously monitor and evaluate the effectiveness of risk management strategies. This involves regularly reviewing risk assessments,updating contingency plans, and learning from past experiences. By continuously improving risk management practices, organizations can adapt to changing circumstances and ensure long-term success.面对风险，积极应对至关重要，以下是一些有效的风险管理策略。
风险预测的一般过程和基本方法
风险预测的一般过程和基本方法As we all know, risk prediction is a critical process in various fields such as finance, insurance, and healthcare. It involves analyzing data to anticipate potential risks and taking proactive measures to mitigate them. 风险预测是金融、保险和医疗保健等领域的关键过程。
它涉及分析数据来预测潜在风险，并采取积极措施来减轻风险。
The general process of risk prediction typically begins with data collection and preprocessing. This involves gathering relevant data from various sources and organizing it in a format that is suitable for analysis. 风险预测的一般过程通常从数据收集和预处理开始。
这涉及从各种来源收集相关数据，并以适合分析的格式组织数据。
Once the data is collected and prepared, the next step is to choose a suitable prediction model. This model can vary depending on the type of risk being predicted and the available data. 一旦数据收集和准备完毕，下一步就是选择适当的预测模型。
这个模型可以根据预测的风险类型和可用数据而变化。
After selecting a prediction model, the data is fed into the model,and predictions are made based on the analysis of the data. These predictions can provide insights into potential risks and help decision-makers plan for the future. 选择预测模型后，数据被输入模型，并根据数据分析进行预测。
寻找股票市场中的预期差
寻找股票市场中的预期差摘要科学衡量基本面价值，找到市场和基本面之间的预期差，获取超额收益是值得努力的方向。
1 引言以 P/B 为代表的价值因子在美股上长盛不衰。
在 A 股上，其经济效益虽然不如美股显著，但价值因子在 empirical asset pricing 以及因子选股上的作用也不容忽视。
以中证500 成分股为例，下图展示了依靠做多低P/B（价值股）、做空高P/B（成长股）的对冲组合的净值曲线（每月调仓、不考虑任何交易成本）。
长期来看，价值股跑赢了成长股。
然而，这背后的解释是什么呢？风险补偿还是错误定价？2012 年，一篇发表于顶刊 Review of Financial Studies 上的文章（Piotroski and So 2012）回答了这个问题。
两位作者提出了预期差（expectation errors）的概念，指出价值股战胜成长股背后的原因是错误定价。
本文就来聊聊这个预期差。
下文的大部分篇幅将用于介绍预期差在中证500 上的实证结果（第三、四节）。
在那之前，我们首先来说明 Piotroski and So (2012) 的研究框架。
2 研究框架按照错误定价的解释，价值股跑赢成长股的原因是市场参与者低估了前者、高估了后者。
这里的高估和低估都是价格相对于期内在价值而言的。
价格反映了投资者对股票的市场预期，而内在价值反映了股票本身的基本面预期。
高、低估说明这两个预期之间存在差异，Piotroski and So (2012) 把这个差异定义为预期差。
更进一步，Piotroski and So (2012) 认为，价值股跑赢成长股的内在逻辑是预期差的修正。
具体来说，他们使用一个F-score 模型（出自 Piotroski 2000）通过 9 个指标给股票的基本面打分，以此作为股票的基本面预期。
这9 个指标从盈利能力、资本结构及偿债能力、运营效率三个维度衡量一个公司的内在价值。
这些指标以及它们的打分方式如下。
高三英语长难句分析单选题60题
高三英语长难句分析单选题60题1. The new artificial intelligence system, which is designed to process vast amounts of data at an incredible speed, ______ expected to revolutionize the way we analyze information.A. areB. isC. wasD. were答案：B。
解析：句子的主干是The new artificial intelligence system...is expected to revolutionize the way...，其中which引导的是一个非限定性定语从句，用来修饰system。
句子的主语system是单数，并且这里表达的是一般现在时的概念，所以要用is。
A选项are用于复数主语；C选项was是一般过去时，不符合句子语境；D选项were 是复数主语的一般过去时形式，均不符合要求。
2. In the field of aerospace technology, the satellite, whose mission is to observe the Earth's climate changes, has a complex structure, with many components ______ are highly sensitive to temperature changes.A. thatB. whichC. whatD. they答案：A。
解析：句子主干是the satellite...has a complex structure。
whose引导的是定语从句修饰satellite，后半句中with结构里又包含一个从句，从句的先行词是components，在从句中作主语，当先行词是物且在从句中作主语时，可以用that或者which引导定语从句，但这里为了避免和前面的which重复，所以用that。
风险预判 英语
风险预判英语Risk Prediction in EnglishRisk prediction is a process of identifying potential problems that may occur in the future and taking steps to mitigate or avoid them. It is an essential skill in many professions, including finance, healthcare, and engineering.In the following paragraphs, we will discuss the importanceof risk prediction, how it is done, and its benefits.1. Importance of Risk PredictionRisk prediction is important because it allows us to be proactive about problems that may occur in the future. By identifying potential risks, we can take steps to preventthem from occurring or mitigate their impact if they do occur. This, in turn, can save time, money, and even lives.2. How Risk Prediction is DoneRisk prediction is done by analyzing data andidentifying patterns that suggest a potential risk. This can be done through a variety of methods, including statistical analysis, data mining, and machine learning. Once a potential risk is identified, steps can be taken to mitigate or avoidit.3. Benefits of Risk PredictionThe benefits of risk prediction are numerous. For example, in the financial industry, risk prediction can help prevent fraud and detect potential market crashes. In healthcare, risk prediction can help identify patients whoare at risk of developing certain conditions or diseases, allowing for earlier treatment and better outcomes. Inengineering, risk prediction can help prevent accidents and ensure that products are safe and reliable.In conclusion, risk prediction is an important skill that can benefit many professions. By identifying potential risks and taking steps to mitigate or avoid them, we can save time, money, and even lives. Using data analysis, we can accurately predict potential threats to our organizations and minimize their impact. Therefore, it is essential for professionals to develop risk prediction skills and incorporate them into their decision-making processes.。
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Expectations, risk premia and information spanning in dynamic term structure model estimationRodrigo GuimarãesMarch 2014Working papers describe research in progress by the author(s) and are published to elicit comments and to further debate.Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the Bank of England or to state Bank of England policy. This paper should therefore not be reported as representing the views of the Bank of England or members of the Monetary Policy Committee or Financial Policy Committee.AppendixA Model EstimatesTables A1and A2show the model…t statistics for the nominal models and for the UK joint model,respectvely.Table A3shows the estimates of the P-dynamics unconditional mean of nominal interest rates for both US and UK.Table A4shows the estimates of the P-dynamics half-life(of the largest eigenvalue) of the pricing factors for both US and UK.Tables A5and A6show the sensitivity of excluding surveys from the nominal and joint models,respectively.Table A5is an expanded version(more percentiles)of thetable shown in the paper.The time series of the normalized range of term premia estimates as a percentage of yields are shown in Figure A1,for the US,and in Figure A2,for the UK.Because estimates with shorter samples are particularly problematic,we show the ranges for all subsamples and excluding2002and2007subsamples.For each model size N and estimation strategy we take the parameters estimated with starting sample year Y Y, ^ NY Y,and obtain the Kalman Filter estimates of the latent factors for the entiresample(since1972)and calculate the n-year spot nominal term premium t^p N;Y Yt;n.We then compute the time series of the range of term premia estimates by calculating for each date t:rtp N t;n=maxi2f72:5:07g t^p N;i t;n mini2f72:5:07gt^p N;i t;n(1)Figure A3shows the estimates of10year real term premia for the UK.1Table A1:Mean absolute errors of nominal yield curve models-US and UK Nominal Yields3m1y2y3y4y5y7y10yUnconstrained3factors19.48.5 4.5 4.6 5.9 5.0 3.0 5.920126687384factors22.2 3.0 4.2 3.5 3.9 4.0 2.4 2.22246565335factors23.0 1.8 2.9 3.2 3.6 3.7 2.2 1.8234656533With Surveys3factors19.58.5 4.6 4.6 5.9 5.0 3.1 5.820126687384factors21.6 3.0 4.2 3.5 3.9 4.0 2.4 2.22246565335factors23.5 3.1 4.0 3.4 3.8 3.8 2.3 1.9234656533 Nominal Yields6m1y2y3y4y5y7y10y3factors 6.47.3 5.8 2.7 3.4 4.5 4.4 4.69108456674factors 1.1 2.5 1.7 2.1 1.1 1.4 3.2 1.9243322535factors0.30.8 1.10.30.70.50.90.301201111With Surveys3factors 6.47.3 5.8 2.7 3.4 4.5 4.4 4.69108456674factors 1.3 2.7 1.8 2.1 1.2 1.5 3.2 2.0243322535factors0.30.8 1.10.40.60.50.90.40.4 1.3 1.70.5 1.10.8 1.50.6Notes:The table shows mean absolute errors across for each maturity for the US(top panel)and UK(bottom panel) nominal yields estimated with the entire sample(Jan1972-November2010).All…gures are expressed in annualized basis points.For each maturity(column)and each number of factors(rows)the absolute error for the model estimated for the full sample is shown for that maturity.The standard deviation of errors for each maturity and model is shown in the line below.2Table A2:Joint nominal-real model…t1972 Nominal Yields6m1y2y3y4y5y7y10yUnconstrained3factors13.97.610.28.47.0 6.5 5.811.817101311998154factors 3.7 5.4 3.1 2.8 2.8 3.4 4.3 4.6574345665factors0.8 1.8 1.6 1.20.8 1.0 1.8 1.113221132With Surveys3factors13.97.510.28.37.0 6.5 5.711.617101311998154factors 3.7 5.3 3.2 2.8 2.8 3.5 4.4 4.7574345665factors 2.9 4.2 2.8 2.1 2.4 2.8 3.1 4.146433445 Real Yields1y2y3y4y5y7y10yUnconstrained3factors24.619.17.97.510.415.821.2322411101420264factors24.620.28.47.09.915.421.1312611101420265factors24.921.28.97.59.714.620.130271210131926With Surveys3factors24.919.38.47.810.616.021.4322512111521274factors24.020.48.77.310.015.521.4312612101420265factors 6.9 4.9 3.6 3.3 3.3 2.1 6.07.9 6.2 4.7 4.2 4.3 2.97.8Notes:The table shows mean absolute errors across models for each maturity for the nominal(top panel)and real(bottom panel)yields.All…gures are expressed in annualized basis points.For each maturity(column)and each number of factors (rows)the mean of the mean absolute error for the model estimated for the full sample is shown for that maturity.The standard deviation of errors for each maturity and model is shown in the line below.3Table A3:Asymptotic mean under P-dynamicsFactors Estimation sample(starting year))19721977198219871992199720022007Unrestricted3 4.5 3.7 2.3 1.6 2.1 1.5 1.6-5.84 4.0 3.0 2.00.8 1.40.6 1.3-5.25 4.3 5.7 3.2 2.2 3.10.0 4.435.8With Surveys3 5.0 4.9 4.0 3.4 4.2 4.3 4.1 6.34 4.9 4.9 4.3 4.2 4.5 4.5 4.4 5.65 4.4 4.4 4.4 4.0 4.4 4.3 4.29.5Sharpe ratio0.53 4.6 4.7 6.5 4.1 3.6 2.9 1.4 2.74 4.6 4.59.3 6.0 4.2 4.0 2.6 2.65 5.6 5.810.5 5.6 5.2 3.8 3.1 3.9Sharpe ratio0.33 4.7 5.38.3 5.3 4.0 3.3 2.5 3.647.67.410.1 6.6 5.5 4.9 3.7 3.558.68.415.5 5.6 5.6 5.5 4.90.4Unrestricted3 6.37.48.2 5.9 5.3 2.6 2.6 2.44 6.37.08.1 6.5 5.6 3.2 3.2 3.45 6.57.58.0 6.5 5.7 2.8 3.1 3.2With Surveys37.38.18.77.2 6.9 5.4 5.0 5.14 6.5 6.98.07.27.2 5.3 5.1 5.15 5.8 6.0 6.5 6.5 6.3 5.4 5.0 5.0Sharpe ratio0.53 6.37.48.2 5.9 5.3 2.68.9 2.54 6.37.08.1 6.0 5.6 3.2 3.5 3.35 6.67.47.8 6.5 5.9 3.7 4.1 3.5Sharpe ratio0.33 6.37.48.2 5.9 5.3 2.9 6.7 3.54 6.87.58.9 5.4 5.8 3.8 5.1 4.25 6.97.98.2 5.0 6.0 4.8 4.7 4.0Notes:The table shows the estimated unconditional mean under the P-dynamics of nominal interest rates for US(Panel A)and UK (Panel B).For each number of factors and estimation method(blocks of rows),and each sample estimation period(columns).All …gures are expressed in annualized percentage points.4Table A4:Persistence under P-dynamicsFactors Estimation sample(starting year))19721977198219871992199720022007Unrestricted3 6.98.5 4.0 5.7 2.9 1.7 3.1 2.447.910.1 4.47.5 4.4 3.4 2.4 2.257.07.5 2.6 4.40.9 2.90.90.9With Surveys37.0 6.5 4.3 4.9 1.8 1.30.9 4.84 6.7 6.1 4.2 3.7 2.0 2.0 1.8 3.85 2.6 3.2 1.8 1.9 2.2 2.1 1.88.8Sharpe ratio0.53 5.5 5.923.38.2 6.0 2.6 1.6 5.64 6.3 6.519.410.8 2.7 2.8 2.3 2.857.58.510.87.3 6.0 2.7 1.1 2.0Sharpe ratio0.33 6.37.011.07.3 2.7 1.9 3.3 5.04 6.5 6.0 6.9 5.5 3.2 2.3 1.5 3.25 6.9 5.913.5 3.8 2.7 2.1 2.80.1Unrestricted3 6.911.119.510.6 5.5 2.9 2.9 4.04 6.39.519.010.5 4.2 2.6 1.7 1.25 6.89.816.69.7 3.5 4.6 2.0 1.6With Surveys37.210.616.17.78.1 1.9 1.2 1.34 6.110.99.8 6.1 5.6 1.7 1.4 1.65 5.1 5.5 4.2 5.6 6.0 1.3 1.4 1.7Sharpe ratio0.53 6.911.119.510.6 5.5 2.9expl. 4.54 6.39.519.010.8 4.2 2.6 1.9 1.75 5.910.416.211.3 4.0 3.0 3.0 2.0Sharpe ratio0.33 6.911.119.510.6 5.5 2.4expl. 2.147.67.316.928.6 5.5 1.69.5 1.858.510.918.6 2.5 5.8 2.3 3.7 2.0Notes:The table shows the estimated half-life of the largest eigenvalue under the P-dynamics for US(Panel A)and UK(Panel B). For each number of factors and estimation method(blocks of rows),and each sample estimation period(columns).All…gures are expressed in years.5Table A5:Sensitivity of term premia estimates to the inclusion of surveys in…ltering Percentile Estimation sample(starting year))19721977198219871992199720022007 Nominal Yields3factors0.50.00.00.00.10.10.10.1 1.10.90.80.90.80.70.50.5 1.4 6.00.95 1.2 1.4 1.2 1.10.70.8 2.310.00.99 2.3 2.6 2.2 2.0 1.3 1.2 3.321.10.999 5.0 5.4 4.7 3.3 2.2 1.7 3.838.34factors0.50.00.00.00.00.00.00.00.50.90.20.30.20.30.10.40.4 5.40.950.40.50.40.40.10.50.67.50.990.9 1.10.80.80.30.90.912.60.999 1.9 2.3 1.6 1.20.5 1.00.916.05factors0.513.77.19.87.7 6.37.0 5.60.10.949.520.726.028.622.225.625.5 1.70.9563.227.233.139.132.038.036.3 2.20.9995.544.553.263.953.360.858.6 2.70.999129.964.386.092.372.681.481.1 3.2 Nominal Yields3factors0.50.00.00.00.00.00.00.00.00.90.00.10.00.10.10.10.10.20.950.20.20.10.10.10.20.10.30.990.60.40.20.20.10.30.20.40.9990.90.60.30.30.20.40.30.44factors0.50.00.00.00.00.00.00.00.00.9 2.5 3.1 3.80.6 1.10.40.00.10.9511.912.312.4 1.3 1.70.50.10.10.9929.828.225.3 2.1 2.60.80.10.20.99958.555.342.0 2.9 3.60.90.10.25factors0.50.00.00.00.00.00.00.00.10.9 1.7 2.3 2.20.70.40.30.30.70.958.59.5 5.1 1.40.70.40.5 1.10.9922.120.210.3 2.5 1.30.80.9 2.20.99940.534.415.3 3.4 1.7 1.1 1.0 2.2Notes:The table shows the percentiles of the absolute di¤erences between the estimates of spot term premia for the models estimated using survey forecasts when the surveys are not used in…ltering the states.For each number of factors(blocks of rows), and each sample estimation period(columns),the percentiles of the absolute di¤erence between the term premia estimates for maturities from10years to20years,with and without surveys used in…ltering,are shown along the rows for each block.All…gures are expressed in annualized basis points.6Table A6:Sensitivity of joint model term premia estimates to the inclusion of surveys in …lteringPercentile Estimation sample(starting year))19721977198219871992199720022007 Nominal Yields3factors0.50.00.00.00.00.00.00.00.00.90.50.50.80.10.10.30.60.10.95 1.1 1.2 2.00.10.20.5 1.10.10.99 2.9 2.6 4.30.30.5 1.6 2.20.20.99910.18.9 5.50.60.8 2.5 3.40.24factors0.50.00.00.00.00.00.00.00.30.90.30.40.40.60.60.30.5 1.50.950.60.70.6 1.60.90.7 1.0 1.70.99 1.5 1.2 1.4 2.9 1.5 1.6 1.7 3.00.999 4.9 5.0 5.8 4.9 2.3 3.0 2.0 3.15factors0.50.00.00.00.00.00.00.10.10.90.20.30.30.60.60.50.60.70.950.60.7 1.0 1.20.90.90.9 1.40.99 1.8 1.9 3.8 4.9 1.5 2.1 1.4 2.20.999 4.1 3.97.110.1 2.0 3.8 1.7 2.5 Real Yields3factors0.50.00.00.00.00.00.00.00.00.90.20.20.40.10.10.10.10.30.950.50.50.90.40.20.20.20.30.99 1.4 1.4 2.00.70.30.60.40.50.999 2.3 2.3 2.5 1.30.4 1.10.70.54factors0.50.00.00.00.00.00.00.00.20.90.10.10.10.20.30.10.6 1.60.950.20.20.20.40.40.20.9 1.80.990.70.80.70.90.70.4 1.6 3.10.999 2.7 2.9 2.8 1.7 1.10.9 2.0 3.15factors0.50.00.00.00.00.00.10.00.00.90.30.40.7 1.60.30.70.40.30.950.60.8 1.1 4.50.4 1.10.70.50.99 1.7 2.0 2.515.40.7 2.2 1.00.90.999 3.2 3.3 5.027.00.9 3.4 1.3 1.1Notes:The table shows the percentiles of the absolute di¤erences between the estimates of spot nominal(top panel)and real (bottom panel)term premia for the models estimated using survey forecasts when the surveys are not used in…ltering the states. For each number of factors(blocks of rows),and each sample estimation period(columns),the percentiles of the absolute di¤erence between the term premia estimates for maturities from10years to20years,with and without surveys used in…ltering,are shown along the rows for each block.All…gures are expressed in annualized basis points.7Figure A1:Range of 10year nominal spot term premia as a proportion of …tted yields for US20406080100 3 factor models including 200220406080100 3 factor models20406080100 4 factor models including 200220406080100 4 factor models20406080100 5 factor models including 200220406080100 5 factor modelsNotes:The …gure shows the range of the 10year spot term premium estimates for the US nominal government bond yields for the entire sample as a proportion of the …tted yield (see Equation (1)and the description in page 1).Each chart shows the range of estimates for the four di¤erent estimation strategies (unrestricted,using surveys and with a 0.5average constraint on the maximum Sharpe ratio)for a given number of factors.In each chart,the range for each estimation strategy is calculated across the 7di¤erent estimation samples (with starting dates 1972:5:2002)with that strategy.8Figure A2:Range of 10year nominal spot term premia as a proportion of …tted yields for UK80900010204060801003 factor models including 2002 and 20078090001020406080100 3 factor models204060801004 factor models including 2002 and 200720406080100 4 factor models809000100204060801005 factor models including 2002 and 20078090001020406080100 5 factor modelsNotes:The …gure shows the range of the 10year spot term premium estimates for the UK nominal government bond yields for the entire sample as a proportion of the …tted yield (see Equation (1)and the description in page 1).Each chart shows the range of estimates for the three of the di¤erent estimation strategies (unrestricted,using surveys and with a 0.5average constraint on the maximum Sharpe ratio)for a given number of factors.In each chart of the right column,the range for each estimation strategy is calculated across the 6di¤erent starting dates (1972:5:1997)with that strategy,while the charts on the left column also include the estimates with samples starting in 2002and 2007.9Figure A3:UK 10year real term premium with and without surveys-4-20246 3 factors with surv eys-4-20246 3 factors Unrestricted80900010-4-20246 4 factors with surv eys80900010-4-20246 4 factors Unrestricted-4-20246 5 factors with surv eys-4-20246 5 factors UnrestrictedNotes:The …gure shows the 10year spot real term premium estimates for the UK government bond yields for a total of 48estimated models.All …gures are in percentage points per annum.The models vary by sample,with 8di¤erent samples shown in each chart.The samples vary by start date,starting every 5years from 1972to 2007,with all samples ending in Dec 2010.The 3models varying by number of factors (3to 5)are displayed along the rows.The models using surveys are displayed in the left column and the unrestricted models in the right column.The forecasts for 1,2and 3years ahead Bank Rate from the Bank of England’s Survey of External Forecasters and the forecasts for in‡ation from Consensus Forecasts for 1,2,3,4,5years ahead and the average between 6and 10years ahead were used for estimation of the models with surveys.B Monte CarloThe description of the design of these Monte Carlo experiments is in the Appendix of the paper.Cramér-von Mises test P-values:Tables B1through B3show the same median p-values for the Cramér-von Mises test for the Monte Carlo experiments MC2-MC4, respectively,as the table for MC1in the paper.Bias:Tables B4through B6show the same statistics for the bias in unconditional mean and half-lifes of the largest eigenvalue of for the Monte Carlo experiments MC2-MC4,respectively,as the table for MC1in the paper.Table B1:Monte Carlo P-values for Alternative Time Series and Cross-Section Estimates (MC2)A.Estimates from Time Series40507010020030050070010000.50%0%1%24%55%76%89%98%99%10%0%0%2%11%26%42%70%88%20%0%0%0%0%1%5%19%36%30%0%0%0%0%0%1%3%10%50%0%0%0%0%0%0%0%0%70%0%0%0%0%0%0%0%0%100%0%0%0%0%0%0%0%0%150%0%0%0%0%0%0%0%0%200%0%0%0%0%0%0%0%0%300%0%0%0%0%0%0%0%0%500%0%0%0%0%0%0%0%0%B.Estimates with Cross-Section ForecastsLike Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey Yields0.51%100%100%0%53%100%0%1%16%10%99%100%0%42%100%0%0%10%20%99%100%0%7%85%0%0%1%30%99%100%0%0%32%0%0%0%50%97%100%0%0%2%0%0%0%70%42%100%0%0%2%0%0%0%100%10%100%0%0%100%0%0%0%150%0%100%0%0%0%0%0%0%200%0%99%0%0%0%0%0%0%300%0%71%0%0%0%0%0%0%500%0%13%0%0%0%0%0%0%Notes:The table shows the median of the p-values from the pairwise Cramér-von Mises test for common estimated dynamics from the Monte Carlo experiment model‘MC2’(described in Appendix of the paper).This is a3factor VAR, with largest eigenvalue of0.9997.The test is applied to all pairwise combinations of forecasts from the1000estimates.The forecasts are generated using actual estimated factors from UK data(the same from which the true parameters were taken) for di¤erent forecast horizons(rows).Table B2:Monte Carlo P-values for Alternative Time Series and Cross-Section Estimates (MC3)A.Estimates from Time Series40507010020030050070010000.53%7%40%74%86%95%97%99%99%10%0%7%32%49%67%78%89%95%20%0%0%4%11%22%33%49%64%30%0%0%0%1%5%10%20%32%50%0%0%0%0%0%1%2%5%70%0%0%0%0%0%0%0%1%100%0%0%0%0%0%0%0%0%150%0%0%0%0%0%0%0%0%200%0%0%0%0%0%0%0%0%300%0%0%0%0%0%0%0%0%500%0%0%0%0%0%0%0%0%B.Estimates with Cross-Section ForecastsLike Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey Yields0.569%99%100%29%87%99%23%74%98%133%99%100%3%61%99%2%40%97%26%98%100%0%26%88%0%7%96%31%98%100%0%14%75%0%1%67%50%98%100%0%4%66%0%0%3%70%85%100%0%1%70%0%0%0%100%13%99%0%0%54%0%0%0%150%0%47%0%0%1%0%0%0%200%0%4%0%0%0%0%0%0%300%0%0%0%0%0%0%0%0%500%0%0%0%0%0%0%0%0%Notes:The table shows the median of the p-values from the pairwise Cramér-von Mises test for common estimated dynamics from the Monte Carlo experiment model‘MC3’(described in Appendix of the paper).This is a4factor VAR, with largest eigenvalue of0.9914.The test is applied to all pairwise combinations of forecasts from the1000estimates.The forecasts are generated using actual estimated factors from US data(the same from which the true parameters were taken) for di¤erent forecast horizons(rows).Table B3:Monte Carlo P-values for Alternative Time Series and Cross-Section Estimates (MC2)A.Estimates from Time Series40507010020030050070010000.53%9%53%92%99%99%99%100%100%10%0%13%55%79%90%96%99%99%20%0%0%11%27%41%54%73%87%30%0%0%1%5%11%19%35%54%50%0%0%0%0%0%0%1%5%70%0%0%0%0%0%0%0%0%100%0%0%0%0%0%0%0%0%150%0%0%0%0%0%0%0%0%200%0%0%0%0%0%0%0%0%300%0%0%0%0%0%0%0%0%500%0%0%0%0%0%0%0%0%B.Estimates with Cross-Section ForecastsLike Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey Yields0.538%99%100%17%81%100%15%31%60%116%99%100%2%53%100%1%11%51%22%99%100%0%9%78%0%1%29%30%99%100%0%1%31%0%0%10%50%85%100%0%0%1%0%0%0%70%67%100%0%0%1%0%0%0%100%48%100%0%0%99%0%0%0%150%0%96%0%0%0%0%0%0%200%0%51%0%0%0%0%0%0%300%0%3%0%0%0%0%0%0%500%0%0%0%0%0%0%0%0%Notes:The table shows the median of the p-values from the pairwise Cramér-von Mises test for common estimated dynamics from the Monte Carlo experiment model‘MC4’(described in Appendix of the paper).This is a4factor VAR, with largest eigenvalue of0.9998.The test is applied to all pairwise combinations of forecasts from the1000estimates.The forecasts are generated using actual estimated factors from UK data(the same from which the true parameters were taken) for di¤erent forecast horizons(rows).Table B4:Monte Carlo(MC2)Percentiles of the Bias for Estimates of Unconditional Mean and Half-lifeA.Estimates from Time SeriesModel40501002003004005007001000 Asymptotic Mean in percentage points(DGP=-24.4)0.01-109.2-127.6-144.8-300.5-111.0-72.1-56.1-48.9-51.40.05-23.5-22.2-32.7-37.0-37.9-33.2-30.8-30.5-27.80.10-14.7-15.8-20.3-27.6-25.0-24.3-22.3-21.5-20.70.500.6 1.2 1.20.7 1.0 1.1 1.3 2.3 1.50.9016.518.321.725.326.428.327.227.223.80.9526.325.729.035.437.337.636.537.030.30.99152.3216.8113.9100.6107.188.083.9102.948.6Half-life in years(DGP=208)0.01-207-206-205-202-201-200-198-191-1850.05-206-205-203-201-198-196-192-184-1750.10-205-205-203-199-195-191-186-179-1670.50-202-201-196-183-174-166-156-140-1210.90-164-167-152-97-85-66-65-2270.95expl.411-219846352186920.99expl.expl.expl.expl.expl.expl.42162413597B.Estimates with Cross-Section ForecastsModel Like Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey YieldsAsymptotic Mean in percentage points(DGP=-24.4)0.01-57.2-51.5-7.0-87.1-130.2-155.2-96.2-151.3-81.60.05-19.7-28.2-3.9-22.8-39.2-95.4-26.9-25.2-23.30.10-14.3-22.8-3.0-14.8-20.9-68.6-15.5-15.9-14.90.500.4-1.50.30.50.8 4.20.60.30.70.9017.015.1 4.316.225.088.616.616.315.90.9527.821.3 5.624.658.5111.025.024.824.60.99140.857.78.396.6146.2161.8163.752.851.1Half-life in years(DGP=208)0.01-205-187-54-206-201-181-206-206-2040.05-202-174-30-205-199-149-205-204-2020.10-201-162-17-203-199-115-204-203-2010.50-197-20-197-189-2-198-198-1980.90expl.27900expl.expl.3expl.expl.expl.0.95expl.expl.8expl.expl.expl.expl.expl.expl.0.99expl.expl.146expl.expl.expl.expl.expl.expl. Notes:The table shows the percentiles of the bias in the estimated unconditional mean of interest rates( + (I ) 1 )and half-life of the largest eigenvalue of (1ln(0:5))from the Monte Carlo experimentTable B5:Monte Carlo(MC3)Percentiles of the Bias for Estimates of Unconditional Mean and Half-lifeA.Estimates from Time SeriesModel40501002003004005007001000 Asymptotic Mean in percentage points(DGP=4.94)0.01-4.7-4.1-2.7-2.0-1.6-1.4-1.2-1.0-0.80.05-3.2-3.0-2.0-1.4-1.1-0.9-0.8-0.7-0.60.10-2.5-2.2-1.6-1.1-0.9-0.7-0.6-0.5-0.50.500.0-0.1-0.1-0.1-0.10.00.00.00.00.90 2.2 2.1 1.4 1.00.80.70.70.50.50.95 2.7 2.7 1.9 1.3 1.2 1.00.80.70.60.99 4.8 4.0 2.5 1.9 1.7 1.3 1.2 1.00.9Half-life in years(DGP=6.7)0.01-5.7-5.6-4.9-3.9-3.5-3.1-2.7-2.3-2.00.05-5.5-5.3-4.3-3.4-2.8-2.5-2.2-1.8-1.50.10-5.2-5.0-3.9-2.9-2.4-2.1-1.8-1.5-1.30.50-3.6-3.2-1.8-1.0-0.8-0.5-0.4-0.3-0.20.90 1.1 1.4 1.9 1.8 1.7 1.6 1.3 1.1 1.00.95 3.5 3.9 3.7 2.8 2.4 2.2 1.9 1.7 1.50.9913.910.97.2 5.0 4.2 3.9 3.3 2.5 2.2B.Estimates with Cross-Section ForecastsModel Like Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey YieldsAsymptotic Mean in percentage points(DGP=4.94)0.01-2.0-0.5-0.2-3.6-0.9-0.4-4.1-2.5-1.70.05-1.2-0.3-0.1-2.6-0.5-0.3-3.1-1.6-1.10.10-1.0-0.2-0.1-2.0-0.4-0.2-2.3-1.2-0.90.500.00.00.00.00.00.00.00.00.00.900.80.20.1 1.80.40.2 2.2 1.10.90.95 1.10.30.1 2.40.50.3 2.9 1.5 1.10.99 1.70.40.1 3.70.80.4 4.8 2.3 1.5Half-life in years(DGP=6.7)0.01-5.2-2.8-0.9-5.3-4.8-2.5-5.3-5.0-4.40.05-4.2-2.1-0.5-4.6-3.6-1.7-4.6-4.2-3.60.10-3.7-1.6-0.3-4.1-3.0-1.3-4.1-3.7-3.10.50-1.50.00.0-1.8-1.10.0-1.8-1.5-1.20.90 1.20.90.2 1.40.90.7 1.7 1.3 2.20.95 2.4 1.60.5 4.0 1.7 1.3 4.3 3.4 3.90.99 6.8 2.9 1.114.5 3.4 3.221.97.98.4Notes:The table shows the percentiles of the the bias in estimated unconditional mean of interest rates( + (I ) 1 )and half-life of the largest eigenvalue of (1ln(0:5))from the Monte Carlo experimentTable B6:Monte Carlo(MC4)Percentiles of the Bias for Estimates of Unconditional Mean and Half-lifeA.Estimates from Time SeriesModel40501002003004005007001000 Asymptotic Mean in percentage points(DGP=14.35)0.01-139.1-97.6-105.6-50.8-32.6-31.9-24.8-19.3-16.60.05-27.9-30.6-29.2-25.4-20.3-19.2-15.7-13.3-11.70.10-18.3-21.0-18.7-17.4-15.5-15.1-12.3-10.8-9.00.500.70.8 1.30.40.00.00.2-0.3-0.40.9019.521.122.419.217.014.312.510.39.10.9529.833.730.926.821.318.816.613.311.40.99131.8165.365.269.243.130.924.920.317.5Half-life in years(DGP=51.4)0.01-49.5-49.1-47.6-45.7-44.5-43.6-43.1-42.2-39.40.05-48.7-48.2-46.5-44.2-43.0-41.9-41.1-39.1-33.00.10-48.2-47.5-45.7-43.1-41.9-40.3-39.0-34.7-29.70.50-44.3-43.0-40.6-35.1-29.3-24.9-20.0-15.2-11.60.90-3.8 3.4-13.3 3.59.614.919.521.517.70.95expl.expl.42.339.234.742.139.040.632.60.99expl.expl.expl.4111.3273.6193.0103.889.963.1B.Estimates with Cross-Section ForecastsModel Like Q Data US Data UKNoise Survey Yields Noise Survey Yields Noise Survey YieldsAsymptotic Mean in percentage points(DGP=14.35)0.01-87.0-44.9-5.5-105.7-98.6-183.3-122.0-103.5-87.20.05-28.2-15.1-2.9-27.0-31.2-38.0-27.8-28.4-27.50.10-19.3-11.2-2.1-18.9-19.5-24.7-18.4-19.3-18.30.500.70.70.10.70.50.70.50.40.90.9020.411.7 2.219.920.231.619.820.220.10.9534.115.6 2.831.529.541.429.330.832.90.99132.387.6 4.8153.2119.768.5155.4129.8256.5Half-life in years(DGP=51.4)0.01-48.1-41.8-22.4-49.0-46.5-41.9-49.0-48.8-48.30.05-46.7-41.0-15.0-47.9-44.4-40.8-48.1-47.9-47.20.10-46.0-40.6-10.4-47.3-43.0-39.9-47.4-47.2-46.20.50-38.7-15.3-0.1-41.4-37.3-0.4-42.2-41.4-40.00.90expl.281.20.3expl.expl.0.7302.9expl.expl.0.95expl.expl.10.3expl.expl.expl.expl.expl.expl.0.99expl.expl.52.2expl.expl.expl.expl.expl.expl. Notes:The table shows the percentiles of the the bias in estimated unconditional mean of interest rates( + (I ) 1 )and half-life of the largest eigenvalue of (1ln(0:5))from the Monte Carlo experiment。