A Capital Asset Pricing Model with Time-Varying Covariances
TheCapitalAssetPricingModel
CHAPTER 9: THE CAPITAL ASSET PRICING MODEL1. E(r P ) = r f + βP [E(r M ) – r f ] 18 = 6 + β(14 – 6)βP = 12/8 = 1.52.If the correlation of the security with the market portfolio doubles with all other variables such as variances unchanged, then the beta, and therefore the risk premium, will also double. The current risk premium is 14 – 6 = 8%, so the new risk premium would be 16%, and the new discount rate for the security would be 16 + 6 = 22%.If the stock pays a constant perpetual dividend, then we know from the original data that the dividend, D, must satisfy the equation for the present value of a perpetuity:Price = Dividend / Discount rate50 = D /.14D = 50 × .14 = $7.00At the new discount rate of 22%, the stock would be worth only $7/.22 = $31.82. The increase in stock risk has lowered its value by 36.36%. 3. The appropriate discount rate for the project is:r f + β[E(r M ) – r f ] = 8 + 1.8(16 – 8) = 22.4%Using this discount rate,NPV = –40 +∑t=110151.224t= –40 + 15 × Annuity factor(22.4%, 10 years) = 18.09The internal rate of return (IRR) on the project is 35.73%. Recall from your introductory finance class that NPV is positive if IRR > discount rate (equivalently, hurdle rate). The highest value that beta can take before the hurdle rate exceeds the IRR is determined by35.73 = 8 + β(16 – 8) β = 27.73 / 8 = 3.474. a. False. β = 0 implies E(r) = r f , not zero.b. False. Investors require a risk premium only for bearing systematic (undiversifiable ormarket) risk. Total volatility includes diversifiable risk.c. False. 75% of your portfolio should be in the market, and 25% in bills. Then,βP= .75 ×1 + .25 ×0 = .755. a. Call the aggressive stock A and the defensive stock D. Beta is the sensitivity of thestock's return to the market return, i.e., the change in the stock return per change in the market return. Therefore, we compute each stock's beta by calculating the difference in its return across the two scenarios divided by the difference in the market return.βA =–2 – 385 – 25 = 2.00βD =6 – 125 – 25 = .30b. With the two scenarios equally likely, the expected return is an average of the twopossible outcomes.E(rA) = .5(–2 + 38) = 18%E(rD) = .5(6 + 12) = 9%c. The SML is determined by the market expected return of .5(25 + 5) = 15%, with a beta of1, and the bill return of 6% with a beta of zero. See the following graph.The equation for the security market line is:E(r) = 6 + β(15 – 6).d. Based on its risk, the aggressive stock has a required expected return of: E(rA) = 6 +2.0(15 – 6) = 24%, but the analyst's forecast of expected return is only 18%. Thus itsalpha is 18% – 24% = –6%. Similarly, the required return on the defensive stock is:E(rD) = 6 + .3(15 – 6) = 8.7%, but the analyst’s forecast of expected return for D is 9%, and hence, the stock has a positive alpha:αD= actually expected return – required return (given risk)= 9 – 8.7 = +.3%.The points for each stock plot on the graph as indicated above.e. The hurdle rate is determined by the project beta, .3, not by the firm’s beta. The correctdiscount rate is 8.7%, the fair rate of return on stock D.6. Not possible. Portfolio A has a higher beta than B, but its expected return is lower.Thus, thse two portfolios cannot exist in equilibrium.7. Possible. If the CAPM is valid, the expected rate of return compensates only forsystematic (market) risk represented by beta rather than for the standard deviation which includes nonsystematic risk. Thus, A's lower rate of return can be paired with a higher standard deviation, as long as A's beta is lower than B's.8. Not possible. The reward-to-variability ratio for portfolio A is better than that of themarket, which is impossible according to the CAPM, since the CAPM predicts that the market is the most efficient portfolio. Using the numbers supplied,S A = 16 – 1012 = .5 S M =18 – 1024 = .33The numbers would imply that portfolio A provides a better risk-reward tradeoff than the market portfolio.9. Not possible. Portfolio A clearly dominates the market portfolio. It has a lower standarddeviation with a higher expected return.10. Not possible. The SML for this situation is: E(r) = 10 + β(18 – 10)Portfolios with beta of 1.5 have an expected return of E(r) = 10 + 1.5 ×(18 – 10) = 22%.A's expected return is 16%; that is, A plots below the SML (αA = –6%), and hence, is an overpriced portfolio. This is inconsistent with the CAPM.11. Not possible. The SML is the same as in problem 10. Here, portfolio A’s required returnis: 10 + .9 ×8 = 17.2%, which is still higher than 16%. A is overpriced with a negativealpha: αA = –1.2%.12. Possible. The CML is the same as in problem 8. Portfolio A plots below the CML, asany asset is expected to. This situation is not inconsistent with the CAPM.13. Since the stock's beta is equal to 1.2, its expected rate of return is 6 + 1.2(16 – 6) = 18%E(r) = D1+ P1– PP.18 = 6 + P1– 5050P1= $5314. The series of payments of $1,000 is a perpetuity. If beta is .5, the cash flow should bediscounted at the rate6 + .5 × (16 – 6) = 11%PV = 1000/.11 = $9,090.91If, however, beta is equal to 1, the investment should yield 16%, and the price paid for the firm should be:PV = 1000/.16 = $6,250The difference, $2,840.91, is the amount you will overpay if you erroneously assumed that beta is .5 rather than 1.15. Using the SML: 4 = 6 + β(16 – 6)β = –2/10 = –.216. r1 = 19%; r2= 16%; β1= 1.5; β2= 1a. To tell which investor was a better selector of individual stocks we look at their abnormalreturn, which is the ex-post alpha, that is, the abnormal return is the difference betweenthe actual return and that predicted by the SML. Without information about theparameters of this equation (risk-free rate and market rate of return) we cannot tell which investor was more accurate.b. If r f = 6% and r M = 14%, then (using the notation of alpha for the abnormal return)α1 = 19 – [6 + 1.5(14 – 6)] = 19 – 18 = 1%α2 = 16 – [6 + 1(14 – 6)] =16 – 14 = 2%Here, the second investor has the larger abnormal return and thus he appears to be thesuperior stock selector. By making better predictions the second investor appears to have tilted his portfolio toward underpriced stocks.c. If r f = 3% and r M = 15%, thenα1 =19 – [3 + 1.5(15 – 3)] = 19 – 21 = –2%α2 = 16 – [3+ 1(15 – 3)] = 16 – 15 = 1%Here, not only does the second investor appear to be the superior stock selector, but thefirst investor's predictions appear valueless (or worse).17. a. Since the market portfolio by definition has a beta of 1, its expected rate of return is 12%.b. β = 0 means no systematic risk. Hence, the portfolio's expected rate of return in marketequilibrium is the risk-free rate, 5%.c. Using the SML, the fair expected rate of return of a stock with β = –0.5 is:E(r) = 5 + (–.5)(12 – 5) = 1.5%The actually expected rate of return, using the expected price and dividend for next year is:E(r) = 44/40 – 1 = .10 or 10%Because the actually expected return exceeds the fair return, the stock is underpriced.18. a.The risky portfolio selected by all defensive investors is at the tangency point betweenthe minimum-variance frontier and the ray originating at r f, depicted by point R on thegraph. Point Q represents the risky portfolio selected by all aggressive investors. It is the tangency point between the minimum-variance frontier and the ray originating at r B f .b. Investors who do not wish to borrow or lend will each have a unique risky portfolio at thetangency of their own individual indifference curves with the minimum-variance frontier in the section between R and Q.c. The market portfolio is clearly defined (in all circumstances) as the portfolio of all riskysecurities, with weights in proportion to their market value. Thus, by design, the average investor holds the market portfolio. The average investor, in turn, neither borrows norlends. Hence, the market portfolio is on the efficient frontier between R and Q.d. Yes, the zero-beta CAPM is valid in this scenario as shown in the following graph:19. Assume that stocks pay no dividends and hence the rate of return on stocks is essentiallytax-free. Thus, both taxed and untaxed investors compute identical efficient frontiers.The situation is analogous to that with different lending and borrowing rates as depicted in the graph of Problem 18. Taxed investors are analogous to lenders with a lending rate of r f(1 – t). Their relevant CML is drawn from r f(1 – t) to the efficient frontier withtangency at point R on the graph. Untaxed investors are analogous to borrowers whomust use the (now higher) rate of r f to get a tangency at Q. Between them, both classes of investors hold the market portfolio which is a weighted average of R and Q, withweights proportional to the aggregate wealth of the investors in each class.Since any combination of two efficient frontier portfolios is also efficient, the average(market) portfolio will also be efficient here, as depicted by point M. Moreover, the Zero Beta model must now apply, because the market portfolio is efficient and all investorschoose risky portfolios that lie on the efficient frontier. As a result, the ray from theexpected return on the efficient portfolio with zero correlation with M (and hence zerobeta), to the efficient frontier, will be tangent at M. This can only happen ifr f(1 – t) < E(r Z) < r f.More generally, consider the case of any number of classes of investors with individualrisk-free borrowing and lending rates. As long as the same efficient frontier of riskyassets applies to all of them, the Zero-Beta model will apply, and the equilibrium zero-beta rate will be a weighted average of each individual's risk-free borrowing and lending rates.20. In the zero-beta CAPM the zero-beta portfolio replaces the risk-free rate, thus,E(r) = 8 + .6(17 – 8) = 13.4%21. a.22. d. From CAPM, the fair expected return = 8 + 1.25(15 − 8) = 16.75%Actually expected return = 17%.α = 17 − 16.75 = .25%23. d.24. c.25. d.26. d. [You need to know the risk-free rate]27. d. [You need to know the risk-free rate]28. Under the CAPM, the only risk that investors are compensated for bearing is the risk thatcannot be diversified away (systematic risk). Because systematic risk (measured by beta) is equal to 1.0 for both portfolios, an investor would expect the same rate of return from both portfolios A and B. Moreover, since both portfolios are well diversified, it doesn’t matter if the specific risk of the individual securities is high or low. The firm-specificrisk has been diversified away for both portfolios.。
资本资产定价模型的基本原理。
资本资产定价模型的基本原理。
The Capital Asset Pricing Model (CAPM) is a widely used model in finance that describes the relationship between risk and expected return for assets. 资本资产定价模型（Capital Asset Pricing Model，CAPM）是金融领域中广泛使用的一个模型，它描述了资产风险与预期回报之间的关系。
The basic principle of the CAPM is that investors need to be compensated for both the time value of money and the risk they are taking on a particular investment. 资本资产定价模型的基本原理是，投资者需要得到对资金时间价值和他们在特定投资中承担的风险所需的补偿。
In the CAPM, the expected return on an asset is calculated as the risk-free rate plus a premium based on the asset's beta, which measures its volatility relative to the overall market. 在资本资产定价模型中，资产的预期回报被计算为无风险利率加上基于资产的贝塔值的溢价，贝塔值衡量了该资产相对于整个市场的波动性。
The risk-free rate represents the return an investor can achieve without taking on any risk, such as investing in government bonds.无风险利率代表了投资者在不承担任何风险的情况下可以获得的回报，比如投资政府债券。
an intertemporal capital asset pricing model
an intertemporal capital asset pricing model1. 引言1.1 概述本文旨在介绍和探讨一种名为“Intertemporal Capital Asset Pricing Model (ICAPM)”的经济学模型。
ICAPM是一种资产定价模型，用于解释资本市场中资产价格与预期收益之间的关系。
它通过考虑投资者对跨期消费和风险厌恶程度的影响，试图更全面地解释资产回报率。
1.2 文章结构本文将按照以下结构进行介绍和分析：- 第2部分：介绍ICAPM的基本概念、假设条件以及相关公式推导；- 第3部分：探讨ICAPM在实际应用中的领域，并讨论其带来的影响和批评；- 第4部分：基于实证研究和案例分析，探讨ICAPM在现实中的有效性和可行性；- 最后，第5部分将对整篇文章进行总结，并展望未来研究方向以及ICAPM所带来的启示或意义。
1.3 目的本文的目的是深入了解并全面介绍ICAPM这一重要经济学模型。
通过详细阐述其背后的理论基础、假设条件和数学推导，读者将对ICAPM有更清晰的认识。
同时，本文还将通过案例分析和实证研究来验证ICAPM的应用效果，并探讨其在实际投资决策中的适用性和局限性。
最后，文章还将总结研究发现，并展望未来关于ICAPM的进一步研究方向，以及该模型对金融市场和投资决策的启示或意义。
2. Intertemporal Capital Asset Pricing Model (ICAPM):2.1 简介：Intertemporal Capital Asset Pricing Model (ICAPM)是一种金融经济学模型，旨在解释资产定价与风险之间的关系。
ICAPM是根据时间序列分析和跨期经济学理论开发出来的，并堪称资本资产定价模型（CAPM）的扩展。
2.2 假设条件：ICAPM基于以下假设条件进行建模：1）投资者都是理性的，趋向于追求最大化预期效用；2）投资者在不同时间段中对于风险具有相同的感知；3）市场参与者可以随意借贷或贷款实现跨期消费平滑；4）风险溢价依赖于个体和整体经济因素；5）投资者对未来收益和风险能够作出准确预测。
资产资本定价模型理解
资产资本定价模型（Capital Asset Pricing Model，简称CAPM）是一种研究风险资产在市场中的均衡价格的模型，由威廉·夏普在马科维兹的投资组合理论的基础上提出。
以下是关于资产资本定价模型的详细解释：1.资产资本定价模型主要研究的是风险与要求的收益率之间的关系。
具体来说，它研究的是投资者在面对不同风险水平时所要求的预期收益率。
2.资产资本定价模型认为，投资者对风险的态度可以用其对风险的厌恶程度来衡量。
风险厌恶程度越高，投资者对风险的容忍度越低，要求的预期收益率也就越高。
3.资产资本定价模型的核心公式为Ri=Rf+β×(Rm-Rf)，其中Ri表示资产的预期收益率，Rf表示无风险利率，Rm表示市场组合的收益率，β表示资产的贝塔系数，反映了资产相对于市场的波动性。
4.资产资本定价模型中，市场组合的收益率与无风险利率的差值被称为市场风险溢价。
这个溢价反映了市场整体对风险的偏好。
如果风险厌恶程度高，则市场风险溢价的值就大。
5.资产的贝塔系数是衡量该资产相对于市场的波动性的指标。
贝塔系数大于1，说明该资产的波动性大于市场平均水平，其预期收益率也会相应地高于市场平均水平；反之，贝塔系数小于1，说明该资产的波动性小于市场平均水平，其预期收益率也会相应地低于市场平均水平。
6.资产资本定价模型是一种线性回归模型，其成立需要一系列的假设前提，如没有交易成本、资产可以无限分割、存在大量的投资者等等。
然而，这些假设在现实中较为苛刻，难以全部实现。
总的来说，资产资本定价模型是一种理论工具，它可以帮助投资者理解和预测不同风险水平下的预期收益率。
然而，它也具有一定的局限性，实际应用中需要考虑多种因素。
资本资产定价模型
(一)资本市场线（CML）在建立了上述假设后，现在我们考虑所有投资者的投资行为。
显然，当所有投资者对风险资产（证券）的预期一致，而且每个投资者都可以不受限制地以固定的无风险利率借入或贷出资金时，根据我们上面的分析，每个投资者投资组合的有效界面都表现为从无风险资产出发、并与风险资产有效界面相切的同一条射线；每个投资者最优投资组合（最优证券组合）中所包含的对风险证券的投资部分都可以归结为对同一个风险资产组合M（在上一节我们称之为“切点处的资产组合”）的投资，即在每个投资者的最优证券组合中，对各种风险证券投资的相对比重均与M相同；不同投资者的最优证券组合的唯一区别仅在于，由于每个投资者的风险偏好不同，每个投资者投资于无风险资产和风险资产组合M的比例不同。
资本资产定价模型的这一特征常被称为“分离定理”。
换句话说，投资者对风险和收益的偏好状况与其应当持有的风险资产组合无关。
实际上，根据分离定理，我们还可以得到另一个重要的结论：在均衡状态下，每种证券在切点处的风险资产组合M中都有一个非零的比例，而且这个比例就等于该种证券在整个资本市场的相对市值。
这是因为，根据分离定理，每个投资者都持有相同的风险资产组合M。
如果某种证券在组合M中的比例为零，那么就没有人购买该证券，该证券的价格就会下降，从而使该证券的预期收益率上升，一直到在最终的切点处的风险资产组合M中该证券的比例非零为止。
反之，如果投资者对某种证券的需要量超过其供给量，则该证券的价格将上升，导致其预期收益率下降，从而降低其吸引力，它在切点处的风险资产组合M中的比例也将下降，直至对其需要量等于其供给量为止。
当所有证券的供求达到均衡时，整个市场就被带入一种均衡状态：（1）每个投资者对每一种证券都愿意持有一定的数量；（2）市场上每种证券的价格都处在使得需求与供给相等的水平上；（3）无风险利率的水平正好使得借入资金的总量等于贷出资金的总量。
结果，在均衡状态下，切点处的风险资产组合M中每种证券的比例就等于该种证券的相对市值，也就是每种证券的总市值在所有证券的市值总和中所占的比重。
经典资产定价理论综述
Financial View | 金融视线MODERN BUSINESS现代商业156经典资产定价理论综述肖琨小 中央财经大学金融学院 北京 100081摘要:本文从威廉·夏普提出的CAPM模型出发,指出其在理论与实证中的不足,从而从三个不同发展方向出发,全面梳理资产定价深化研究,逐步引入CAPM模型的各种拓展模型,从而较为全面的介绍经典的资本资产定价相关理论。
关键词:资本资产定价;APT模型;CCAPM模型;行为金融理论一、引言资本资产的定价问题一直深受金融市场领域乃至整个金融领域的关注。
研究最早起源于20世界50年代,随着经济、金融的不断发展,如今,如何有效的确定金融资产的价格仍是很多经济学家所面临的重大问题。
马科威茨通过把收益、风险分别定义为均值和方差,第一次从数量上解决了收益与风险的关系问题,资本资产定价模型就是在这一理论的基础之上提出的。
1970年,威廉·夏普率先提出资本资产定价模型:CAPM模型,成为资本资产定价的基础。
它的结论非常简单:投资的收益只与风险有关。
虽然,CAPM模型的提出非常成功,但还是存在着很多理论上、实践上的局限性。
首先,C A P M 的假设前提难以实现;其次,CAPM中的β值难以确定;最后,与之相关的实证结果令人失望。
因此,金融市场学家不断探求比CAPM更为有效的资本市场理论。
经济学家们大致从三个方面进行了改进:第一、将单因素CAPM拓展为多因素模型,如APT套利定价理论,Fama-French 三因素模型(提出SMB和 HML因素);第二、提出基于消费的CCAPM模型,将资产回报率与宏观经济变量联系起来;第三,由行为金融学理论对资产定价问题进行解释。
二、资本资产定价的多因素模型(一)套利定价理论APT该模型由斯蒂芬·罗斯于1976年提出,与CAMP模型相比,其最大的特点是利用套利概念定义均衡,并且该模型的假设更加合理。
套利定价理论的基本机制是:在均衡市场中,两种相同的商品必定以相同价格出售。
资本资产定价模型(CAPM专题)
资本资产定价模型（Capital Asset PricingModel）摘要：本文目的是对目前资本资产定价模型的研究状况进行一个详细的评述，内容分以下几个部分：第一部分是概述，介绍CAPM 的基本理论框架；第二部分则对国内外相关文献进行一个比较详细的评述。
一、概述资本资产定价模型是一种纯交换经济中的实证性均衡定价模型，核心思想是在一个竞争均衡中对有价证券定价。
其最早是由夏普（William Sharpe）、林特尔（John Lintner）、特里诺（Jack Treynor）和莫森（Jan Mossin）等人在资产组合理论的基础上提出的，被认为是金融市场现代价格理论的支柱，广泛应用于投资决策和公司理财领域。
（一）基本原理1、有效集（Efficient Set）当风险水平（标准差）相同时，理性投资者将选择具有较高收益率的投资组合；当预期收益率相同时，他们将选择风险水平（标准差）较小的投资组合。
同时满足这两个条件的投资组合的集合就是有效集。
2、分离定理投资者对风险和收益的偏好状况与该投资者风险资产组合的最优构成是无关的。
最优风险资产组合即为使夏普比率（Sharpe ratio）最大的投资组合。
3、投资分散化定理（Investment Diversification）在均衡状态下，每种证券在均衡点处投资组合中都有一个非零的比例。
4、共同基金定理（Mutual Fund Theorem）投资者的最优风险性资产组合（切点处投资组合）即为市场组合，其中各证券的构成比例等于该证券的相对市值。
5、风险－报酬均衡定理（Risk－Return Tradeoff Theorem）给定上述假设，在均衡的资产市场中，有( ( )) ( ) ( ( ) ( )) ( ( )) ( ( ( )) ( )) 0 0 , E R m R x Var R m Cov R m R x E R x R x j j = + - ，其中m 为最优风险资产组合。
国外知名财务学家（中文）-2-斯蒂芬罗斯
斯蒂芬·罗斯（Stephen A. Ross）教授斯蒂芬·罗斯(Stephen A. Ross)，因其创立了套利定价理论(Arbitrage Pricing Theory，简称APT)而举世闻名,是当今世界上最具影响力的金融学家之一。
斯蒂芬·罗斯生于1944年，1965年获加州理工学院物理学学士学位，1970年获哈佛大学经济学博士学位。
罗斯担任过许多投资银行的顾问，其中包括摩根保证信托银行、所罗门兄弟公司和高盛公司，并曾在许多大公司担任高级顾问，诸如AT＆T和通用汽车公司等；罗斯还曾被聘为案件的专业顾问，诸如AT＆T公司拆分案、邦克-赫伯特公司(Bunker and Herbert)陷入白银市场的诉讼案等；另外，罗斯担任过一些政府部门的顾问，其中包括美国财政部、商业部、国家税务局和进出口银行等；罗斯还曾任美国金融学会主席(1988年)、计量经济学会会员、宾夕法尼亚大学沃顿商学院经济与金融学教授、耶鲁大学经济与金融学Sterling讲座教授。
由于对金融理论的杰出贡献，罗斯获得了许多学术荣誉，包括国际金融工程学会(IAFE)最佳金融工程师奖、金融分析师联合会葛拉汉与杜德奖(Graham and Dodd Award)、芝加哥大学商学院给最优秀学者颁发的利奥·梅内姆奖(Leo Melamed Award)、期权研究领域的Pomerance奖；投资管理与研究学会(AIMR)授予罗斯的尼古拉斯-摩罗德乌斯基奖(Nicholas Molodvsky Awar d)，是一个奖励“改变了某专业的方向并使之达到更高领域所作出的杰出贡献”的奖项；1999年，罗斯在《金融与数量分析杂志》(JFQA)第三期发表的论文“额外风险：再论萨缪尔森的大数谬论”获得JFQA1999年度威廉·夏普奖(William Sharpe Award)(该奖项用于奖励那些在《金融与数量分析杂志》上发表文章、为金融理论作出杰出贡献的研究者)。
Chapter 13 The Capital Asset Pricing Model (CAPM)
件要求
w [z
k k
0
rk ( z1 z0 )] z0 wk wk rk ( z1 z0 ) vM zM ， wk vM
k k k
13-13
由此得出
z0 [ ( wk / vM )rk ]( z1 z0 )] zM
k
这正是我们要表明的（即市场组合也可以由两个 MVF 组合—— z0 和 z1 组合而成） 。 ，我们有 由定理 12.10（ rq rzcp q , p (rp rzcp ) ） 定理 13.3 对于任意组合 q，我们有
2 P N m 1
n , r P ) P Cov(r zn P
N N N N n , r P ) Cov r n , (1 zm )rF zm r m zmCov(r n , r m ) zm mn 。 注意： Cov(r m 1 m 1 m 1 m 1
n n rF n n , M (r M rF ) r
（13.4）
n ] 0 且 Cov[r n ] 0 。 M , 其中， E[
n , r n , r M ) cov(rF + n n , M (r M rF ) M ) cov(r n , r M , r M ) cov( M ) = cov( n , M r n , r M , r M ) cov( M ) n , M cov(r
定理 13.1 对于参与者的投资行为和资产的定价关系给出了非常具体的推论。第一， 它指出市场组合 zM 是一个 MVE 组合。市场组合是每个参与者能够直接观察到的，不依 赖于任何关于资产收益的信息，这有极大的实用价值。第二，所有投资者的最优投资选 择就是无风险资产和市场组合的组合。第三，它确定了一个简单的资产定价关系。
资本资产定价模型理论研究
资本资产定价模型理论研究资本资产定价模型理论研究一、引言资本资产定价模型（Capital Asset Pricing Model, 简称CAPM）是金融学中的重要理论，用于解释和预测资本市场的资产定价问题。
该模型是根据资产收益率与市场的关系来进行资产估值的模型，其应用广泛，被广泛认可和应用于金融市场。
本文将介绍CAPM的基本原理、假设和模型推导，同时探讨其在实证研究中的应用和局限性。
二、CAPM的基本原理与假设1. 基本原理资本资产定价模型的基本原理是，资产的预期收益率与市场组合的风险有关。
市场组合即包含所有可能投资资产的投资组合，如证券、股票等。
CAPM认为，资产的风险是由一种称为系统性风险（Systematic Risk）的不可分散风险决定的，而非系统性风险（Unsystematic Risk）是可以通过资产组合来消除的。
2. 假设CAPM建立在一些基本假设之上，包括：（1）投资者是理性的、风险厌恶的：投资者追求最大化预期回报同时最小化风险，且会适当的考虑时间价值。
（2）无风险利率存在：市场上存在无风险利率可以用来度量风险资产的风险溢价。
（3）投资者只关心市场组合的收益：投资者只关注市场组合的预期收益，忽略其他因素。
（4）市场是完全竞争的：投资者可以自由买卖，并可以借入和贷出无风险资产。
三、CAPM模型推导CAPM模型推导的核心是资产的预期收益率与市场组合的风险之间的关系。
假设市场组合的预期收益率为Rm，资产的预期收益率为Ri，无风险利率为Rf，资产与市场组合的协方差为cov(Ri, Rm)，资产的风险溢价为Ri - Rf。
根据CAPM模型的推导，可以得到以下等式：R i = Rf + βi * (Rm - Rf)其中，βi是资产的系统性风险系数，代表了资产相对于市场组合的相对风险敏感性。
四、CAPM模型实证研究CAPM模型的实证研究主要包括两方面：一是研究CAPM模型的有效性，即预测市场收益的能力；二是研究CAPM模型的解释性，即资产收益率的变动是否与模型中的因素一致。
资本资产定价模型(capm)的基本原理
资本资产定价模型（Capital Asset Pricing Model, CAPM）是现代金融理论中的一种重要的资产定价模型，它是由沃尔夫勒姆·舒维茨在1964年提出的。
CAPM模型基于投资组合的平均预期收益率与组合的风险之间的关系来对资产的预期回报进行估计。
这个模型可以用来评估股票、债券和其他资产的合理价格，也可以帮助投资者优化投资组合，分散风险。
这个模型的基本原理包括以下几点：1. 市场风险溢价：CAPM模型认为，投资者应该获得与市场风险成正比的回报。
市场风险溢价是指超过无风险利率的部分收益率。
投资者所要求的预期收益率由无风险利率和市场风险溢价共同决定。
2. 个体资产与市场的关系：CAPM模型通过计算资产的β值来度量个体资产与市场的关联程度。
β值的计算公式为：β=ρ*(σa/σm)，其中ρ为资产收益率与市场收益率之间的相关系数，σa为资产的收益率标准差，σm为市场收益率标准差。
3. 无风险资产的存在：CAPM模型假设存在无风险资产，投资者可以放弃风险获得无风险收益。
在CAPM模型中，无风险利率被视为投资者可以获得的最低预期收益。
4. 投资者的理性行为：CAPM模型假设投资者是理性的，他们在资产配置时会充分考虑风险和收益的权衡。
5. 单一期模型：CAPM模型是一个单期模型，即只对一期的投资收益进行评估，不考虑多期的投资情况。
CAPM模型的基本原理构成了现代金融理论的基础之一，它为资本市场的参与者提供了一个理性的框架，有助于他们进行有效的投资决策。
然而，CAPM模型也存在一些局限性，这包括对市场投资者行为的理性假设和对资产收益率的预测不确定性等。
CAPM模型的基本原理对于理解资本市场的风险与收益关系、评估资产的合理价格以及优化投资组合都具有重要意义。
随着金融市场的不断发展和变化，CAPM模型也在不断完善和拓展，为投资者提供更多更准确的参考信息。
CAPM模型作为资产定价的重要模型，在实践中有着广泛的应用。
资本资产定价模型(CAPM)概述
CAPM & Liquidity
流动性[Liquidity]是指资产出售时所需的费用与便捷程度。投资学非 常注重流动性，有人强调认为“缺乏流动性的资产其投资价值等于0”。 一些研究和大量事实表明，缺乏流动性将大大降低资产的市场售价水 平。如，一项研究表明，股权高度集中的企业其市场价值的折扣超过 了30%。在中国，非流通的国有股售价很低就是明证。 非流动性溢价[Illiquidity Premium]：每种资产的价格中包含了非流动 性溢价。即投资者愿意选择那些流动性强并且交易费用低的资产，也 就是愿意为流动性强的资产支付高价。一般而言，流动性差的资产折 价交易[收益率高]而流动性高的资产往往高价交易[收益率低]。 Amihud and Mendelson等人的研究支持了这一判断。他们运用买卖差 价占全部股价的百分比来衡量流动性。在20年的周期内，流动性最差 的股票收益与流动性最好的股票相比，前者每年平均要高出8.5%。
Z(Q)
Z(P)
Zero Beta Market Model
E (ri ) E (rZ ( M ) ) E (rM ) E (rZ ( M ) )
Cov(ri , rM )
2 M
上式就是CAPM的另一种表达式，其中，E(rz (m))取代了rf。
重要性与局限
零贝塔模型描述了不存在无风险资产时，预期收益率与风 险之间的关系。 与传统CAPM模型相比，零贝塔模型不受无风险资产存在 性的限制，具有更广阔的适用范围，但其局限性在于模型 无法限制卖空行为。 罗斯[1977]的研究表明，同时考虑不存在无风险资产和有 卖空限制条件时，CAPM模型的线性关系将不存在。
资本资产定价模型
资本资产定价模型目录CAPM模型的提出 (2)一. 资本资产定价模型公式 (5)二. 资本资产定价模型的假设 (6)三. 资本资产定价模型的优缺点 (7)四. Beta系数 (9)五. 资本资产定价模型之性质 (10)六. CAPM 的意义 (10)七. 资本资产订价模式模型之应用——证券定价 (12)八. 资本资产定价模型之限制 (13)CAPM模型的提出马科维茨(Markowitz，1952)的分散投资与效率组合投资理论第一次以严谨的数理工具为手段向人们展示了一个风险厌恶的投资者在众多风险资产中如何构建最优资产组合的方法。
应该说，这一理论带有很强的规范(normative）意味，告诉了投资者应该如何进行投资选择。
但问题是,在20世纪50年代，即便有了当时刚刚诞生的电脑的帮助，在实践中应用马科维茨的理论仍然是一项烦琐、令人生厌的高难度工作；或者说，与投资的现实世界脱节得过于严重，进而很难完全被投资者采用——美国普林斯顿大学的鲍莫尔(william Baumol)在其1966年一篇探讨马科维茨一托宾体系的论文中就谈到,按照马科维茨的理论，即使以较简化的模式出发，要从1500只证券中挑选出有效率的投资组合,当时每运行一次电脑需要耗费150~300美元,而如果要执行完整的马科维茨运算，所需的成本至少是前述金额的50倍；而且所有这些还必须有一个前提，就是分析师必须能够持续且精确地估计标的证券的预期报酬、风险及相关系数，否则整个运算过程将变得毫无意义。
正是由于这一问题的存在，从20世纪60年代初开始，以夏普（w．Sharpe，1964）,林特纳（J．Lintner，1965)和莫辛(J．Mossin，1966)为代表的一些经济学家开始从实证的角度出发，探索证券投资的现实，即马科维茨的理论在现实中的应用能否得到简化？如果投资者都采用马科维茨资产组合理论选择最优资产组合，那么资产的均衡价格将如何在收益与风险的权衡中形成？或者说,在市场均衡状态下,资产的价格如何依风险而确定？这些学者的研究直接导致了资本资产定价模型（capital asset pricing model，CAPM）的产生。
金融计量学
FINANCIAL ECONOMETRICS – A NEW DISCIPLINE WITH NEW METHODSRobert EngleUCSD and NYUApril 2000Financial Econometrics is simply the application of econometric tools to financial data. For many years, least squares techniques provided satisfactory tools. Stock market forecasts, efficient market tests, and even tests of portfolio models such as the CAPM and APT were essentially implemented with least squares on cleverly manipulated data sets.More recently, however, the field has developed its individual character as new statistical tools have been invented to analyze new questions.In this short overview, I would like to suggest a framework that includes much of the recent literature and important tools of Financial Econometrics. Let t P be a vector of asset prices observed at time t, and let t F be the information set known to theeconometrician at time t which automatically must include these prices. Corresponding to the price change and dividend payment from t to t+k is a return vector t ,k t R +. A central concern of financial econometrics is to discover the joint conditional density ()t k t F P f +. Estimates of the conditional mean, ()k t t t /k t P E ++=µ of this density were used to test the efficient markets hypothesis, which, in its simplest form, supposed that expected excess returns should be zero. New econometric methods were then introduced to estimate the conditional variances and covariances,()()’P P E t /k t k t t /k t k t t t ,k t +++++−−=µµΩ of these prices and returns. The first models were the ARCH and GARCH models of Engle(1982) and Bollerslev(1986) and then the stochastic volatility models of Taylor(1986) and Harvey, Ruiz and Shepherd(1994).Multivariate GARCH methods were implemented by Bollerslev, Engle andWooldridge(1988), Bollerslev(1990), and Engle and Kroner(1996). Engle andGonzales-Rivera(1991) allowed general non-normal errors but a paper by Hansen(1994)is one of the few successful efforts to estimate time varying higher moments of this density. In response to the needs of regulators and risk managers for calculations of Value at Risk, new methods now are being designed to examine the tails of thisdistribution. It is not clear whether the tails have the same dynamic behavior as the rest of the distribution as would be assumed by GARCH style models. The new models include the Hybrid model of Boudoukh, Richardson and Whitelaw(1998), the CAViaR model of Engle and Manganelli(1999), and extreme value theory estimation of tail shapes as in Embrechts, Kluppelberg and Mikosch (1997), and McNiel and Frey(2000).This collection of methods has generally been successful in parameterizing and estimating conditional densities. The most significant unsolved problem is the multivariate extension of many of these methods. Although various multivariateGARCH models have been proposed, there is no consensus on simple models that are satisfactory for big problems. There has been little work on multivariate tail properties or other conditional moments. There is intriguing evidence of interesting non-linearities in correlation. A second important extension that is receiving a great amount of attention, is the development of methods to use intra-daily data and ultimately transactions data,called by Engle(2000) ultra-high-frequency data, to improve estimation of conditional densities.All these methods are focussed on moments of the empirical conditional density,f . However another object of interest is the risk neutral conditional density, f*, which is the set of probabilities of returns or prices under which an agent would value assets by their discounted expected value. This density is known to exist and to be given by(1) ()()()t k t k t 1t t k t F P f P m b F P *f ++−+=if and only if there are no arbitrage opportunities. The positive function m is the pricing kernel, interpreted as price per unit probability, which is unique if markets are complete.The scalar ()m E b t t = ensures that f* is a density. The risk neutral density has the property that assets with payoff ()k t P g + should have a price(2) ()()()()()()()k t t t t t t t P g *E b du F u *f u g b du F u f u g u m S +===∫∫Since this formula applies to all assets including ones with a sure payment, it is clear that t b is the price of a pure discount bond. When g is the identity, it gives an expression for the risk premium for the underlying asset. When the payoff function is(3) ()}0,K P max{P g k t k t −=++,this expression prices European Call Options.Two different strands of financial econometrics have approached the option-pricing problem. The first observes options data and infers f* ; the second specifies m, estimates f , and computes the option price. The estimation of f* is sometimes called arbitrage free pricing, since the existence of a density which prices all assets, insures no arbitrage opportunities. A variety of methods have been proposed which involve some interpolation of options prices to all strikes and then inversion of (2) to get f*. See for example Shimko(1993) and Rubinstein(1994). These methods have no implication for risk management as the density f* is simply estimated at each point in time and may have any sort of time series behavior. For a dramatic demonstration, see Dumas Fleming and Whaley(1998).The second approach is closer to conventional econometric research. It is typical to appeal to the Black and Scholes continuous hedging argument and Girsanov’s Theorem to infer the pricing kernel, however much recent research which is aimed directly at parameterizing and estimating m , has shown that this may be too simple a specification to replicate the properties of options prices. See for example Jackwerth and Rubinstein(1996) and Rosenberg and Engle(1999).An important class of models assumes that the true data generating process is a continuous time diffusion process. In this case both the empirical and risk neutral density can be computed. Black and Scholes derived their options pricing model based upon geometric Brownian motion but many other models have been proposed and have their own options prices. The econometric problem is to estimate the parameters of the diffusion process based upon the discretely observed data. It has generally been found that simple diffusion processes do not fit observed data so interest has focussed on mean reverting processes such as Ornstein-Uhlenbeck, jump diffusions, and affine family models such as the square root diffusion . The econometrics of these models is difficult, often requiring simulated method of moments or characteristic function estimation. There is a challenge here in finding a satisfactory diffusion model that also supports option pricing. However, models that are most convenient for option pricing incorporate the assumption that options are redundant assets and perhaps this is not the case.The use of higher and higher frequency data potentially could provide information on the appropriate class of diffusion models to use for pricing both underlying andderivative assets. To analyze uhf data, it is necessary to model not only the characteristics of each trade, but also the timing. Engle and Russell(1998) introduce the Autoregressive Conditional Duration model that estimates the distribution of arrival times for the next event conditional on all past information. Dufour and Engle(2000) following Hasbrouck’s (1993) vector autoregression, show that the more frequent the transactions, the greater the volatility and price response to trades and transaction arrivals are predictable based upon economic variables such as the bid ask spread. Econometric models of transactions, quotes, prices and volumes support many of the implications of market microstructure theory. Potentially these models should yield valuable information for market designers and risk managers. These models may also serve as underlying data generating processes for calculating empirical and risk neutral density forecasts and consequently for options pricing. This is an important challenge for models of empirical microstructure.ReferencesBollerslev, Tim (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31,pp307-327Bollerslev, Tim (1990), “Modelling the Choerence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model”, Review of Economics andStatistics, 72:498-505Bollerslev, Tim, Robert Engle, and J.M. Wooldridge (1988) "A Capital Asset Pricing Model with Time Varying Covariances," Journal of Political Economy 96, 116-131.Boudoukh, J., M. Richardson and R.F. Whitelaw (1998), The best of both worlds, Risk, 11: 64-67.Dufour, Alfonso and Robert Engle,(2000) “Time and the Price Impact of a Trade”, Journal of Finance, forthcomingDumas B, Fleming J, Whaley RE (1998) “Implied volatility functions: Empirical tests”Journal of Finance 53: (6) 2059-2106 DECEmbrechts, Paul, Claudia Kluppelberg and Thomas Mikosch (1997), Modelling Extremal Events, Springer.Engle, Robert(1982), "Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation," Econometrica 50, 987-1008.Engle, Robert, (2000) "The Econometrics of Ultra High Frequency Data", Econometrica68 : 1-22.Engle, Robert and G. Gonzalez-Rivera(1991)"Semi-Parametric ARCH Models," Journal of Business and Economic Statistics 9: 345-359.Engle, Robert and with Simone Manganelli (1999)"CAViaR: Conditional Value At Risk By Regression Quantiles," manuscript, UCSDEngle, Robert and K. Kroner (1995) "Multivariate Simultaneous GARCH," Econometric Theory 11, 122-150.Engle, Robert and Jeffrey R. Russell,(1998) "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica 66: 1127-1162.Hansen, Bruce (1994), "Autoregressive conditional density estimation," International Economic Review 35, 705-730.Hasbrouck, Joel, (1991),” Measuring the information content of stock trades”, Journal of Finance 46, 179-207.Jackwerth JC, Rubinstein M, (1996) “Recovering probability distributions from option prices”, Journal of Finance,51: (5) 1611-1631McNeil, Alexander and Rudiger Frey (2000), Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach, Journalof Empirical Finance, forthcoming.Rosenberg, Joshua, and Robert Engle, (1999) "Empirical Pricing Kernels," manuscript UCSD, and NYURubinstein, M. (1994), “IMPLIED BINOMIAL TREES”, Journal of Finance, 49: (3) 771-818Shimko,D.C.,(1993) “Bounds of Probability”, RISK, 6,33-37Taylor, Stephen J, (1986), Modelling Financial Time Series, John Wiley and Sons Ltd。
{财务管理财务知识}财务管理英语中英文对照
{财务管理财务知识}财务管理英语中英文对照ACCA--财务管理英语solepropsietorship独资企业partnership合伙企业corporatefinance公司财务corporate公司closelyheld私下公司publicpany公众公司GoldmanSachs高盛银行pensionfund养老基金insurancepany保险公司boardofdirector董事会separationofownershipandmanagement所有权与管理权的分离limitedliability有限责任articlesofincorporation公司章程realasset实物资产financialasset金融资产security证券financialmarket金融市场capitalmarket资本市场moneymarket货币市场investmentdecision投资决策capitalbudgetingdecision资本预算决策financingdecision融资决策financialmanager财务经理treasurer司库controller总会计师CFO首席财务官principal-agentproblem委托代理问题principal委托人agent代理人agencycost代理成本informationasymmetry信息不对称signal信号efficientmarketshypothesis有效市场假说presentvalue现值discountfactor贴现因子rateofreturn收益率discountrate贴现率hurdlerate门坎比率opportunitycostofcapital资本机会成本netpresentvalue净现值cashoutflow现金支出netpresentvaluerule净现值法则rate-of-returnrule收益率法则profitmaximization利润最大化doingwell经营盈利doinggood经营造益collateral抵押品warrant认股权证convertiblebond可转换债券primaryissue一级发行prinarymarket一级市场secondarytansaction二级交易secondarymarket二级市场over-the-counter(OTC)场外交易financialintermediary金融中介zero-stage启动阶段businessplan创业计划书first-stagefinancing第一阶段融资after-the-moneyvaluation注资后的价值papergain账面利润mezzaninefinancing引渡融资angelinvestor天使投资者venturecapitalfund创业投资基金limitedprivatepartnership有限合伙企业generalpartner普通合伙人limitedpartner有限合伙人small-businessinvestmentpanies（SBIC）小企业投资公司initialpublicoffering(IPO)首次公开发行primaryoffering首次发行secondaryoffering二次发行underwriter承销商syndicateofunderwriter承销辛迪加registrationstatement注册说明书prospectus招股说明书roadshow路演greenshoeoption绿鞋期权spread差价offeringprice发售价格underpricing抑价winner’scurse成功者灾难bookbuilding标书登记fixedpriceoffer定价发售auction拍卖discriminationauction差价拍卖uniform-priceauction一价拍卖generalcashoffer一般现款发行rightissue附权发行shelfregistration上架注册priorapproval事前许可boughtdeal买方交易seasonedissue新增发行knowledgeableinvertor成熟的投资者qualifiedinstitutionalbuyer有资格的机构买者recorddate登记日withdividend附有红利cumdividend附息exdividend除息legalcapital法定资本regularcashdividend正常现金红利extra额外的specialdividend特别红利stockdividend股票红利automaticdividendreinvestmentplans(DRIP)红利自动转投计划transferofvalue价值转移capitalstructure资本结构MM’spropositionMM定理weighted-averagecostofcapital(WACC)加权平均资本成本margindebt保证金借款floating-ratenote浮动利率票据money-marketfund货币市场基金interest-rateceiling利率上限trade-offtheory权衡理论righttodefault违约权leveragebuy-out(LBO)杠杆收购asymmetricinformation信息不对称financialslack银根宽松costofdebt负债成本costofequity权益成本terminalvalue清算价值cashflowtoequity权益现金流rebalancing重整projectfinancing项目融资adjustedcostofcapital调整资本成本equivalentloan等值贷款offsettingtransaction反向交易depreciablebasis折旧基数adjustedpresentvalue(APV)调整现值calloption看涨期权exerciseprice执行价格strikeprice敲定价格exercisedate到期日Europeancall欧式看涨期权Americancall美式看涨期权positiondiagram头寸图putoption看跌期权SalomonBrothers所罗门兄弟公司termstuctureofinterestrate利率期限结构annuity年金perpetuity永久年金annuityfactor年金因子annuitydue即期年金futurevalue终值conmpoundinterest复利simpleinterest单利continuouslypoundedrate连续复利率ConsumerPriceIndex(CPI)消费物价指数currentdollar当期货币nominaldollar名义货币constantdollar不变货币realdollar实际货币realrateofreturn实际收益率inflationrate通货膨胀率principal本金deflation滞胀yieldtomaturity到期收益率marketcapitalizationrate市场资本化率dividendyield红利收益率costofequitycapital权益资本成本payoutratio红利发放率earningspershare(EPS)每股收益returnonequity(ROE)权益收益率discountcashflow贴现现金流growthstock成长股inestock绩优股presentvalueofgrowthopportunity(PVGO)成长机会的现值price-earningsratio(P/E)市盈率freecashflow(FCF)自由现金流量bookrateofreturn账面收益率capitalinvestment资本投资operatingexpense经营费用paybackperiod回收期discounted-paybackrule贴现回收期法则discounted-cash-flowrateofreturn贴现现金流量的收益率internalrateofreturn(IRR)内部收益率lending贷出borrowing借入profitabilitymeasure盈利指标standardofpfofitability赢利标准modifiedinternalrateofreturn修正内部收益率mutuallyexclusiveprojects互相排斥的项目capitalrationing资本约束profitabilityindex盈利指标softrationing软约束hardrationing硬约束incrementalpayoff增量收入networkingcapital净营运资本sunkcost沉没成本sunk-costfallacy沉没成本悖论overheadcost间接费用salvagevalue残值straight-linedepreciation直线法折旧InternalRevenueService国内税收署taxshield税盾acceleratedcostrecoverysystem加速成本回收折旧法alternativeminimumtax另类最低税taxpreference税收优惠accelerateddepreciation加速折旧projectanalysis项目分析equivalentannualcashflow等价年度现金流marginalinvestment边际投资defaultrisk违约风险riskpremium风险溢酬standarderror标准误差standarddeviation标准差marketportfolio市场组合marketreturm(rm)市场收益率variance方差thelossofadgreeoffreedom自由度损失Delphic德尔菲uniquerisk独特风险unsystematicrisk非系统风险residualrisk剩余风险specificrisk特定风险diversifiablerisk可分散风险marketrisk市场风险systematicrisk系统风险undiversifiablerisk不可分散风险convariance协方差well-diversified有效分散valueadditivity价值可加性efficientportfolio有效投资组合quadraticprogramming二次规划bestefficientportfolio最佳有效投资组合capitalassetpricingmodel(CAPM)资本资产定价模型separationtheorem分离定理securitymarketline证券市场线marketcapitalization市场资本总额small-capstocks小盘股book-to-marketration账面-市值比datamining数据挖掘datasnooping数据侦察consumptionbeta消费贝塔consumptionCAPM消费型资本资产定价模型riskaversion风险厌恶arbitragepricingtheory套利定价理论sensitivityofeachstocktothesefactors每种股票对这些因素的敏感度three-factormodel三因素模型panycostofcapital公司资本成本industrybeta行业贝塔firmvalue公司价值debtvalue负债价值assetvalue资产价值blue-chipfirm蓝筹股financialleverage财务杠杆gearing举债经营financialrisk财务风险unlever消除杠杆relativemarketvaluesofdebt(E/V)负债的相对市场价值taxableine应税利润after-taxcost税后成本marginalcorporatetaxrate公司边际税率after-taxweighted-averagecostofcapital税后加权平均资本成本cyclicality周期性cyclicalfirms周期性公司operatingleverage经营杠杆revenue收入fixedcosts固定成本variablecosts可变成本rateofoutput产出率certaintyequivalent确定性等价值certainty-equivalentcashflow确定性等价现金流risk-adjusteddiscountrate风险相应贴现率underlyingvariables基础变量sample抽样realoption实物期权decisiontree决策树timingqption安排期权productionoption生产性期权optiontobailout清算选择权economicrent经济租金futuremarket期货市场capitalbudget资本预算strategicplanning战略规划appropriationrequest拨款申请postaudit事后审计stockoptions股票期权privatebenefit私下利益perks特权享受perquisite特权享受overinvestment过度投资generallyacceptedaccountingprinciple(GAAP)公认会计原则qualifieddoption保留意见delegated委托economicvalueadded(EVA)经济附加值tradeloading贸易超载economicdepreciation经济折旧nationalineandproductaccounts国民收入与产出账户cap上限合约strip本息剥离债券swap互换bookrunner股份登记员efficientcapitalmarket有效资本市场randomwalk随机游走positivedrift正漂移autocorelationcoefficient自相关系数weakformofefficiency弱有效性semistrongformofefficiency半强有效性strongformofefficiency强有效性marketmodel市场模型acquiringfirm兼并公司acquiredfirm被兼并公司superiorprofit超常利润overreaction反应过度underreaction反应不足indexarbitrageur指数套利者portfolioinsurancescheme证券组合保险策略superiorratesofreturn超常收益exchange-ratepolicy汇率政策applicationdate申购日long-termasset长期资产retainedearnings留存收益financialdeficit资金缺口internalfund内部资金debtpolicy负债政策dividendpolicy红利政策long-termfinancing长期融资totalcapitalization资本总额reserve准备金issuedandoutsanding已发行流通股份issuedbutnotoutsanding已发行未流通股份authorizedsharecapital法定股本总额financialinstitution金融机构cash-flowright现金流要求权controlright控制劝dominantstockholder控股股东majorityvotingsystem多数投票制cumulativevoting累计投票制supermajority绝对多数制proxycontest投票代理权角逐minoritystockholder小股东reversestocksplit逆股票拆细masterlimitedpartnership业主有限责任合伙企业realestateinvestmenttrust(REIT)房地产信托投资基金preferredstock优先股cumulativepreferredstock红利累积优先股lineofcredit授信额度fixed-rate固定利率floating-rate浮动利率coupon息票LondonInterbankOfferedRate(LIBOR)伦敦银行间拆借利率eurobond欧洲债券eurocurrency欧洲货币euro欧元senior优级junior次级subordinated从属secured有担保的collateral抵押品warrant认股权证convertiblebond可转换债券primaryissue一级发行prinarymarket一级市场secondarytansaction二级交易secondarymarket二级市场over-the-counter(OTC)场外交易financialintermediary金融中介zero-stage启动阶段businessplan创业计划书first-stagefinancing第一阶段融资after-the-moneyvaluation注资后的价值papergain账面利润mezzaninefinancing引渡融资angelinvestor天使投资者venturecapitalfund创业投资基金limitedprivatepartnership有限合伙企业generalpartner普通合伙人limitedpartner有限合伙人small-businessinvestmentpanies（SBIC）小企业投资公司initialpublicoffering(IPO)首次公开发行primaryoffering首次发行secondaryoffering二次发行underwriter承销商syndicateofunderwriter承销辛迪加registrationstatement注册说明书prospectus招股说明书roadshow路演greenshoeoption绿鞋期权spread差价offeringprice发售价格underpricing抑价winner’scurse成功者灾难bookbuilding标书登记fixedpriceoffer定价发售auction拍卖discriminationauction差价拍卖uniform-priceauction一价拍卖generalcashoffer一般现款发行rightissue附权发行shelfregistration上架注册priorapproval事前许可boughtdeal买方交易seasonedissue新增发行knowledgeableinvertor成熟的投资者qualifiedinstitutionalbuyer有资格的机构买者recorddate登记日withdividend附有红利cumdividend附息exdividend除息legalcapital法定资本regularcashdividend正常现金红利extra额外的specialdividend特别红利stockdividend股票红利automaticdividendreinvestmentplans(DRIP)红利自动转投计划transferofvalue价值转移capitalstructure资本结构MM’spropositionMM定理weighted-averagecostofcapital(WACC)加权平均资本成本margindebt保证金借款floating-ratenote浮动利率票据money-marketfund货币市场基金interest-rateceiling利率上限trade-offtheory权衡理论righttodefault违约权leveragebuy-out(LBO)杠杆收购asymmetricinformation信息不对称financialslack银根宽松costofdebt负债成本costofequity权益成本terminalvalue清算价值cashflowtoequity权益现金流rebalancing重整projectfinancing项目融资adjustedcostofcapital调整资本成本equivalentloan等值贷款offsettingtransaction反向交易depreciablebasis折旧基数adjustedpresentvalue(APV)调整现值calloption看涨期权exerciseprice执行价格strikeprice敲定价格exercisedate到期日Europeancall欧式看涨期权Americancall美式看涨期权positiondiagram头寸图putoption看跌期权感谢阅读多年企业管理咨询经验，专注为企业和个人提供精品管理方案，企业诊断方案，制度参考模板等欢迎您下载，均可自由编辑。
资本资产定价模型
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“技术”部分 技术”
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“个性”部分 个性”
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10.2资本市场线（续） 资本市场线（ 二、均衡与市场组合 证券市场均衡时每一种证券在切 点组合的构成中具有一个非零的 比例。 比例。 如果紫光的股票没有进入最优风 险资产组合中， 险资产组合中，所有的投资者的 风险资产组合中也没有它。 风险资产组合中也没有它。
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CML
CML的截距、斜率各代表什么经济 的截距、 的截距 含义？ 含义？ CML和CAL有何关系？区别？ 有何关系？ 和 有何关系 区别？ CML是CAL的一个有趣特例。 的一个有趣特例。 是 的一个有趣特例
Copyright © 2005 by Zengqf,Swun
Copyright © 2005 by Zengqf,Swun
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10.2深入探讨：市场风险溢价（续） 深入探讨：市场风险溢价（
在简化的CAPM模型中，无风险投资包括 模型中， 在简化的 模型中 投资者之间的借入和贷出。 投资者之间的借入和贷出。任何借入头寸 必须有债权人的贷出头寸作为抵偿。 必须有债权人的贷出头寸作为抵偿。 因此,全部投资者之间的净借入与净贷出总 因此 全部投资者之间的净借入与净贷出总 和为零。那么， 和为零。那么，全部投资者风险资产组合 上投资比例总和为100%，即y*=1。因此 上投资比例总和为 ， 。因此, 就市场整体