stochastic process-2

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BS期权定价模型课件

BS期权定价模型课件
第5页,共32页。
(二)普通布朗运动
我们先引入两个概念:漂移率和方差率。
标准布朗运动的漂移率为0,方差率为 1.0。
我们令漂移率的期望值为a,方差率的期
望值为b2,就可得到变量x 的普通布朗运
动: dx adt bdz
(6.4)
其中,a和b均为常数,dz遵循标准布朗
运动。
Copyright©Zhenlong Zheng 2003, Department of Finance, Xiamen University
令 代表该投资组S合的价值,则:
f f S S
(6.15)
Copyright©Zhenlong Zheng 2003, Department of Finance, Xiamen University
第15页,共32页。
在t
时间后:
f
f
S
S
(6.16)
将式(6.12)和(6.14)代入式
随机过程是指某变量的值以某种不确定的 方式随时间变化的过程。可分为离散型的 和连续型的。马尔可夫过程是一种特殊类 型的随机过程。
如果证券价格遵循马尔可夫过程,则其未 来价格的概率分布只取决于该证券现在的 价格。
Copyright©Zhenlong Zheng 2003, Department of Finance, Xiamen University
最后,从金融工程的角度来看,欧式看涨 期权可以分拆成资产或无价值看涨期权 (Asset-or-noting call option)多头和现金 或无价值看涨期权(cash-or-nothing option)空头,SN(d1)是资产或无价值看 涨期权的价值,-e-r(T-t)XN(d2)是X份现金或 无价值看涨期权空头的价值。

Chapter 2(stochastic process)

Chapter 2(stochastic process)

Chapter 2Conditional Expectation2.1 A Binomial Model for StockPrice Dynamicsn Figure:A three coin period binomial model.n Note that the following notation:1. Sample space2. is stock price at time k2.2 Informationn Definition 2.1 (Sets determined by the first k tosses) We say that a set A is determined by the first k coin tosses if, knowing only the outcome of the first k tosses, we can decide whether the outcome of all tosses is in A.n Note thatn1. is the collection of set determined by the first k tossesn2.n3. the random variable is -measurable, for each k=1,2,…nn Example 2.1nn Definition 2.2 (Information carried by a random variable.) Let X be a random variable We say that a set is determined by the random variable X if, knowing only the value of the random variable, we can decide whether or not . Another way of saying this is that forevery , eithern.n Note thatn 1. The collection of subsets of determined by X is a algebra, denote byn 2. If the random variable X takes finitely many different values, then is generated by thecollection of setsn these sets are called the atoms of the algebra .n 3.if X is a random variable then is given byn Example 2.22.3 Conditional Expectationn Definition 2.3 (Expectation.)n Andn We can think of as a partial average of X over the set A.n2.3.1 An examplen Let us estimate , given . Denote the estimate by .n is a random variable Y whose value at is defined byn where .n Properties of n.n.n.n.n We then take a weighted average:n Furthermore,n In conclusion, we can write n Wheren2.3.2 Definition of Conditional Expectationn Existence. There is always a random variable Y satisfying the above properties (provided thati.e., conditional expectations always exist.n Uniqueness. There can be more than one random variable Y satisfying the above properties, but if is another one, then almost surely, i.e.,n Notation 2.1 For random variables X, Y , it is standard notation to writen.n.n2.3.3 Further discussion of Partial Averagingn.n2.3.4 Properties of Conditional Expectationn We computen We can also writen A similar argument shows thatn We can verify the Tower Property,2.4 Martingalesn The ingredients are:n A super martingalen A sub martingalen A Martingalen Example 2.3 (Example from the binomial model.)n For k = 1;2 we already showed thatn The right hand side is , and so we have。

金融随机分析 II Stochastic process

金融随机分析 II Stochastic process

n
i
bn 0
P 1 (ii) bn E X | F ni i1 0, and 2 n
(iii ) b
EX
n i 1
2 ni
E E X ni | Fi 1
2
0
12
Ref: Hall . P, Heyde C., Martingale limit theory and its applicatiom.1980, Academic Press. Inc
M k M a.s and
M
k
M dP 0 as k
10
Corollary 2.2.9 Let X L P ,N k k 1 be an
1

increasing family of σ-algebras, N k F and define N to be the σ-algebra generated by
h X k 1 k h X k 1 X k (b) for every bounded Borel-measurable function h :
(c) (d)
euX k1 k euX k1 X k , u



(Agreement of Laplace transforms)
2.3 Markov Processes
Definition 2.3.1 Let (, , ) be a filtration under . Let {Xk,k=0,1,…}be a stochastic process on (, , ) . This process is said to be Markov if:

StochasticProcess

StochasticProcess

2
Discrete Time and Continuous Time Processes
•X(t) is a discrete time process if X(t) is defined only for a set of time instants tn=nT where T is a constant and n is an integer
According to Theorem 1 p( j)= [ P(0, 1)P(1,2) … P( j-1, j)]T p(0) In the Stationary case, P(0, 1)P(1,2) … P( j-1, j) = P j
Example: Random Walk With Barriers
Markov Diagram
•In the stationary form
–the Chapman-Kolmogorov equations has a graphical interpretation in terms of one-step probabilities p1,1 p1 p2 p2,2 p2,1 2 1 p1,2 p2,3 p3,2 p1,3 p3,1 3
t ,t ' pm P[ X (t ' ) n | X (t ) m] ,n
•For discrete-time process, define
i, j pm P[ X ( j ) n | X (i ) m] ,n
Markov Chain
•A stochastic process is a Markov chain if
•X(t) is a discrete value process if the set of all possible values of X(t) at all times t is a countable set SX

Stochastic Processes SolutionbookII 随机过程习题解答

Stochastic Processes SolutionbookII  随机过程习题解答
k i−1 ,ti ]
,
Xti − Xti−1
i=1
2
=
i=1 n k=1 ti
k σs
k dWs
+
ti−1
µs ds
n ti ti−1 ti 2 k k σs dWs ti ti−1 l l σs dWs
m
=
i=1
+2
k,l=1,k=l k k σs dWs
+2
ti−1
µs ds
k=1
+
ti−1
µs ds
As thus
Π
→ 0, we have Var Wtik − Wtik−1 E [W i , W j ]t
Wtik − Wtik−1
Wtjk − Wtjk−1
= 0 and
n k=1 i j [W , W ]t =
Wtjk − Wtjk−1 = 0.
converges to its expectation, and thus
t
f (t, x) = eαx− 2 α t ,
1
2
α ∈ R.
f (t, Bt ) = f (0, B0 ) −
0 t
1 2 α f (s, Bs ) ds + 2
t
αf (s, Bs ) dBs +
0
1 2
t
α2 f (s, Bs ) ds
0
=1+
0
αf (s, Bs ) dBs
t 0 αf (s, Bs ) dBs t≥0
S 1 2 E(YS YS ) = E 0 S 2 1 Yu dYu + 0 S S 1 2 Yu dYu + 0 S

AdventuresInStochasticProcessesSolutionManual

AdventuresInStochasticProcessesSolutionManual

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I Stochastic process

I Stochastic process

Springer, 2000
2
Introduction
PROBLEM Consider the simple stock price model
dS t dt
t S t ,
S 0 x
(1.1)
Where S(t) is the stock price at time t, and μ(t) is the relative rate of growth at time t. It might happen that μ(t) is not completely known, but subject to some random environmental effects, so that we have
n W
0 X1 ( ) X 2 ( ) X 3 ( )
4. General: X X X maxX ( ),0 max X ( ),0
Ak ( Ak ) k 1 k 1
(W, , P) is called a probability triple;
a measurable subset of W is called an event.
6
Example 1.1.3
( A B) ( A)( B), A , B
Definition 1.2.4: We say that two s-algebras G , H , are independent if
( A B) ( A)( B), A G , B H
n
The integral of X, is defined as
W
X ( )dP( ) c P( F )

【1】Stochastic Processes-Lecture-One

【1】Stochastic Processes-Lecture-One

We define as follows : A sequence of events {En , n ≥ 1} is said to be an increasing sequence if En ⊂ En +1 , n ≥ 1 and is said to be decreasing if En ⊃ En +1 , n ≥ 1 .
If {En , n ≥ 1} is an increasing sequence of events , then we define a new event , denoted by lim En by
n →∞
lim En = ∪ Ei , when En ⊂ En +1 , n ≥ 1 .
n →∞ i =1
backslash, sometimes escape 反斜线转义符,有时表 示转义符或续行符 tilde 波浪符
~
'
apostrophe hyphen
撇号
-...
‘’
连字号
dash 破折号 dots/ ellipsis 省略号
single quotation marks 单引号 double quotation marks 双引号 parallel 双线号 arrow 箭号;参见号
PROPOSITION 1.1.1
If {En , n ≥ 1} is either an increasing or decreasing sequence of events , then lim P ( En ) = P(lim En ).
n →∞ n →∞
proof Suppose , first , that {En , n ≥ 1} is an increasing sequence , and define events Fn , n ≥ 1 by F1 = E1 ,

Stochastic Process_1 随机过程

Stochastic Process_1 随机过程
1 n 即P X i mx 1 n i 1
Dy D
中心极限定理
• 令X1, …,Xn为一系列独立随机变量, 均值为 ,方差为 ,则当
的分布趋于正态分布。 大量独立的随机变量之和的极限分布为正态 分布。
多次抛掷硬币实验中出现正面的平均比率 每次实验均抛掷了大量硬币
x mx f ( x )dx
2
x mx
x mx f ( x )dx
2
由于f(x)>0,因此
Dx 2
x mx
f ( x )dx 2 P[ x mx ]
Dx
P[ x m x ]
2
Chebyshev定理
当独立试验次数足够大,随机变量X的观察值的算 术平均值以概率收敛于数学期望。 “以概率收敛”即当实验次数n足够大时,对任意 小的正数 和 有 1 n
例:掷一个色子的期望E(X)
练习:试求前面所讲几个典型随机变量的期望
• 定理:X是一随机变量,F(x)为分布函数, y=g(x)是连续函数,若 g ( x)dF ( x存在,则 )
EY E[ g ( X )] g ( x)dF( x)

• 推论:如果a,b为常数,则

(7) Continuity for above: 若 En 单调递减,则
lim P( En ) P( En )
n n 1
• 条件概率 • 乘法公式 • 全概率公式
P( EF ) P( E F ) P( F )
P( E1 E2 En ) P( E1 ) P( E2 E1 ) P( E3 E1 E2 ) P( En E1 E2 En 1 )

Stochastic Process Assignment 1

Stochastic Process Assignment 1

Suppose we know that the number of items produced by in a fatory during aweek is a random variable with mean 500.What can be said about the probability that this week’s production will be at least 1000?



S OLUTION
Solution:From the Markov’s Inequality,we have P {P roduction ≥ 1000} ≤ E [P rodution]/1000 = 0.5 . 2

E XERCISE 1.4
There are n types of coupons.Each newly obtained coupon is,independently,type i with probability pi , i = 1, 2, . . . , n.Find the expected number and the variance of the number of distinct types obtained in a collection of k coupons.
Page 4 of 11
= 1 − ((1 − pi )k + (1 − pj )k − (1 − pi − pj )k ) − (1 − (1 − pi )k )(1 − (1 − pj )k ) = (1 − pi − pj )k − (1 − pi )k (1 − pj )k . Thus,the Varriance of N can be obtained. 2

StochasticProcessesRossSolutionsManual-…

StochasticProcessesRossSolutionsManual-…

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II_Stochastic_process-金融随机分析

II_Stochastic_process-金融随机分析

11(1)()1,...,1,...(1)(...)()k k t t t t k v F F F F σσσσν--⨯⨯=⨯⨯ ()K1for all permutations σon {1,2,...k}K2)()(1,,,,,1,111n n k t t t t k t t R R F F v F F v m k k k k ⨯⨯⨯⨯=⨯⨯++ for all m ∈N ,where (of course ) the right hand side has a total of k+m factors.Then there exists a probability space (Ω,F , P) and a stochastic process {X t } on Ω, s.t.,:n t R X →Ω],,,[)(11,,11k t t k t t F X F X P F F v kk ∈∈=⨯⨯ for all t i ∈T and all Borel sets F i .6DEFINTION 2.2.1An n-dimensional stochastic process {M t }t ≥0on ( , F ,P)is called a martingale (resp.submartingale, supermartingale) with respect to a filtration {F t }t ≥0(and with respect to P 0) if(Ⅰ) {M t } is F t -adapted(Ⅱ) E[| M t |]<∞for all t, and(III) E[M t | F s ]= M s (resp. ≥,≤), a.s. , for all s ≤t .(Note:If t ∈T={0,1,2,….},then {M t } is a martingale (resp. submartingale, supermartingale) if and only ifE[M k+1| F k ]= M k (resp. ≥,≤), a.s.It is clear that any martingale must be both a sub-and auper-martingale.2.2 martingales7 Some examples of martingale Example2.2.2 Let {ξn ,n ≥1} be a random process on (Ω, F ,P), F n =σ(ξ0,…, ξn ), if E(ξn+1| F n )=0, Let∑==nk k n X 0ξExample 2.2.3Let ξn be an independent random process with mean 1,then {X n ,n ∈N} is a martingale.∏==ni in X 1ξExample 2.2.4Let ξbe a random variable on (Ω, F ,P), F n be a filtration on (Ω,F ),then{X n =E(ξ| F n ), n ∈N} is a martingale.,then, {X n ,n ∈N} is a martingale.if ξn is nonnegative then {X n ,n ∈N} is a submartingale.8PROPERTIES OF MARTINGALETHEOREM 2.2.5Let M t be a submartingale (resp. martingale). Then E(M t ), as a function of t, is nondereasing.(resp. a constant)In particular, when X t is a martingale and E[ |X t |p ]<∞for some p ≥1. Then {|X t |p } is a submartingale.THEOREM 2.2.6Let X t ,Y t be F t -submartingales (resp. martingales). Theni)for all a ≥0,b ≥0, aX t +bY t is F t -submartingale (resp. martingale).ii){ X t ∨Y t } is F t -submartingale.iii)Let ϕ: R →R a nondereasing convex function ( resp. convex function) such that E[ϕ(X t )] exists for all t ≥0. Then ϕ( X t ) is a submartingale.{}{},t t P t B x P B x τ≤<=>with probability 1. The expected time to为X 的矩母函数.(moment generating function)定义* 对随机变量X 及其分布函数F(x),若积分在某一区间上存在且有限,则定义区间上的函数()x e dF x α∞--∞⎰()12,αα()12,t t ()()()x X m e dF x E e ααα∞---∞==⎰。

精品文档-随机过程——计算与应用(研究生)(冯海林)-随机过程引论课件1

精品文档-随机过程——计算与应用(研究生)(冯海林)-随机过程引论课件1

思考:若令Xt表示t时刻该生物群体的个数,
则这个随机变量Xt是否可以较为全面 反映生物群体增长情况?
一般需要每隔一定时间,即在 t=0,1, 2 , …. 时观察 相应的群体个数Xt,
即需要一族随机变量,记为{Xt ,t=0,1, 2 , ….}
西安电子科技大学 —数学与统计学院 冯海林
School of Mathematics and Statistics Xidian University
2
2015/9-2016/1
随机过程引论
Introduction to Stochastic Process
➢ 随机过程应用广泛
随机过程在自然科学、社会科学以及工程 技术的各领域均有应用.
——在我校的一些专业:雷达、通信、无线电 技术、自动控制、生物工程、经济管理等领 域有极为广泛的应用.
西安电子科技大学 —数学与统计学院 冯海林
所以该地区的最高气温需要用一族随机变量 Xt ,t=0,1,2,…,方可表达之
记为{Xt , t=0,1,2,…}
西安电子科技大学 —数学与统计学院 冯海林
School of Mathematics and Statistics Xidian University
13
2015/9-2016/1
随机过程引论
5
2015/9-2016/1
随机过程引论
Introduction to Stochastic Process
➢ 本课程的教学内容
随机过程的基本知识
பைடு நூலகம்
布朗运动及其相关的随机过程
跳跃随机过程
二阶矩过程与平稳过程
离散时间马尔可夫链
西安电子科技大学 —数学与统计学院 冯海林

Stochastic Process_平稳过程

Stochastic Process_平稳过程

设 定义: X { X (t ), t }为平稳过程, (1)若P{ X (t ) m X (t )} 1, 则称过程X 的均值具有 遍历性; (2)若P{ X (t ) X (t ) RX ( )} 1, 则称为X (t )的时 间协方差函数具有遍历性。
定义: X 设
{ X (t ), t }为平稳过程,
1 T (1)若均方极限 lim T X (t )dt存在,则称X (t )在 T 2T ( , )上的时间平均,记为 X (t ) ; 1 T (2)对固定的,若均方极限 lim T ( X (t ) m ) T 2T ( X (t ) m )dt存在,则称为X (t )的时间协方差函数。
联合平稳过程
设{X (t ),t T},{Y (t ),t T}为两个平稳过程,若对任意 , R XY (s+ , t )=R XY (s, t )=R XY (t s)仅于t s有关,则称 X (t ),Y (t )为联合平稳过程。
遍历性(ergodic)定理
针对平稳过程,对时间的平均值 均值。
其状态E {e1 , e2 , , eb }, 则m EX n是各个状态的加权平均。
所有样本,当N 很大时,AN中的元素历经E中的各个状态,
定理(均值遍历性定理)
(i)设X { X n , n 0, 1, }为平稳序列,其协方差R( ), 则X 有 遍历性的充分必要条件是 1 lim N N
随机过程
Stochastic Process
主讲人:范瑾
Email: jinfan@ 2010 Autumn
平稳过程
Review
Stationary process:for all the random vectors and have the same joint distribution. Weakly stationary process: E[X(t)]=c, R(s)=Cov[X(t),X(t+s)] does not depend on t.

Probability and Stochastic Processes

Probability and Stochastic Processes

Probability and Stochastic Processes Probability and stochastic processes are two closely related concepts that are extensively used in various fields, including mathematics, physics, engineering, and computer science. Probability is the measure of the likelihood of an event occurring, while stochastic processes are mathematical models used to describe the evolution of random variables over time. In this essay, we will explore the significance of probability and stochastic processes, their applications, and the challenges encountered in their study. Probability theory is a fundamental concept in mathematics that provides a framework for analyzing and predicting the outcomes of random events. It is used extensively in various fields, including finance, engineering, physics, and computer science, to name a few. In finance, probability theory is used to evaluate the risk associated with investments and to determine the expected returns. In engineering, it is used to analyze thereliability of systems and to design experiments. In physics, it is used to describe the behavior of particles at the quantum level. Stochastic processes are mathematical models used to describe the evolution of random variables over time. They are widely used in various fields, including finance, economics, biology, and physics. In finance, stochastic processes are used to model the behavior of stock prices and interest rates. In economics, they are used to model the behavior of markets and to predict future trends. In biology, they are used to model the spread of diseases and to study population dynamics. In physics, they are used to model the behavior of particles and to describe the evolution of complex systems. One of the significant challenges encountered in the study of probability and stochastic processes is the complexity of the models used to describe them. These models often involve complex mathematical equations and require sophisticated statistical techniques to analyze. The use of computer simulations and numerical methods has helped to overcome some of these challenges, but the complexity of the models remains a significant barrier to their widespread use. Another challenge encountered in the study of probability and stochastic processes is the interpretation of the results obtained. The outcomes of these models are often probabilistic, which means that they are subject to uncertainty and variability. This uncertainty can make it difficult to draw definitive conclusions from theresults obtained. Additionally, the results obtained from these models may be sensitive to the assumptions made during the modeling process, which can lead to inaccuracies in the predictions made. Despite these challenges, the study of probability and stochastic processes remains essential in various fields. These concepts provide a powerful framework for analyzing complex systems and predicting future outcomes. They are also essential for understanding the behavior of random events and for making informed decisions in the face of uncertainty. As such, the study of probability and stochastic processes is likely to remain a vital area of research for many years to come. In conclusion, probability and stochastic processes are two closely related concepts that are widely used in various fields. They provide a powerful framework for analyzing complex systems and predicting future outcomes. However, the complexity of the models used to describe them and the uncertainty associated with their results present significant challenges to their study. Despite these challenges, the study of probability and stochastic processes remains essential in various fields and is likely to remain a vital area of research for many years to come.。

two-stage stochastic programming

two-stage stochastic programming

two-stage stochastic programming
两阶段随机规划是一种用于解决决策问题的数学优化方法,其中决策变量在第一阶段被确定,然后在第二阶段面临不确定性。

两阶段随机规划可以应用于各种场景,如生产计划、资源配置、物流和供应链管理等。

在第一阶段,决策者需要做出一系列决策,这些决策基于对未来的预测和期望。

这些决策通常涉及资源的分配、产品的生产计划等。

在第二阶段,决策者面临不确定性,例如市场需求的变化、资源供应的波动等。

这些不确定性可能导致第一阶段的决策无法实现预期的目标,因此决策者需要在第二阶段调整决策以适应这些变化。

两阶段随机规划的目标是在第一阶段做出最优决策,并在第二阶段面对不确定性时保持灵活性。

通过将问题分解为两个阶段,两阶段随机规划可以更好地处理不确定性和风险,并提供更稳健和可靠的解决方案。

在实际应用中,两阶段随机规划可以通过各种优化算法进行求解,如线性规划、整数规划、混合整数规划等。

此外,可以通过引入不同的假设和约束条件来扩展两阶段随机规划的应用范围,例如考虑不同的成本和收益函数、时间限制、资源限制等。

总之,两阶段随机规划是一种强大的数学优化方法,可以帮助决策者做出更稳健和可靠的决策,并在面对不确定性和风险时保持灵活性。

QuantLib金融计算——随机过程之概述

QuantLib金融计算——随机过程之概述

QuantLib ⾦融计算——随机过程之概述⽬录如果未做特别说明,⽂中的程序都是 Python3 代码。

QuantLib ⾦融计算——随机过程之概述载⼊模块import QuantLib as qlprint(ql.__version__)1.12框架随机过程是⾦融⼯程中的⼀个核⼼概念,是沟通理论分析和计算实践的枢纽。

quantlib-python 提供了⼀组成体系的类架构⽤于描述实际中最常见到的⼏种随机过程,以 1.12 版本为例:C++ 版本的实现提供了更多具体的随机过程。

其中最根本的基类是 StochasticProcess ,然后衍⽣出三⼤类别:HestonProcess :特殊的⼆维随机过程——Heston 过程;BatesProcess :⼀种带跳跃的 Heston 过程;StochasticProcessArray :描述⼀般的多维随机过程;StochasticProcess1D :描述常⽤的若⼲⼀维随机过程。

GeneralizedBlackScholesProcess :Black-Scholes 框架下四种最常⽤的随机过程BlackScholesProcess :d ln S (t )=r (t )−σ(t ,S )22dt +σdW t BlackScholesMertonProcess :d ln S (t ,S )=r (t )−q (t )−σ(t ,S )22dt +σdW t BlackProcess :d ln S (t )=−σ(t ,S )22dt +σdW tGarmanKohlagenProcess :d ln S (t )=r (t )−r f (t )−σ(t ,S )22dt +σdW t VarianceGammaProcess Merton76Process GeometricBrownianMotionProcess :dS (t ,S )=µSdt +σSdW t HullWhiteProcess HullWhiteForwardProcessGsrProcess 基类 StochasticProcess 模拟⼀个 d 维 Ito 过程:d S t =µt ,S t d t +σt ,S t d W tquantlib-python 默认的离散化⽅法是 Euler ⽅法:S (t +Δt )=µt ,S t Δt +σt ,S t ΔW t ⽤法与接⼝随机过程类的⽤法基本上是⾸先初始化⼀个实例,然后并将其传递给其他类的实例,这些类的实例从中提取所需的变量。

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称 X|Y (x | y) = P X ≤ x | Y = y} F ˆ { x f (u, y) =∫ du 设 Y (y) > 0 ( f ) −∞ f ( y) Y
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