Brownian Motion
description
Transcript of Brownian Motion
Brownian Motion
Chuan-Hsiang HanNovember 24, 2010
Symmetric Random Walk
Given ; let and , and denotes the outcome of th toss. Define the r.v.'s that for each
A S.R.W. is a process such that and
Independent Increments of S.R.W.
Choose , the r.v.s are independent, where the increment is defined by Note:(1) Increments are independent.(2) The increment has mean 0 and variance .(Stationarity)
Martingale Property of S.R.W.
For any nonnegative integers ,
contains all the information of the first coin tosses.If R.W. is not symmetric, it is not a martingale.
Markov Property of S.R.W.
For any nonnegative integers and any integrable function
If R.W. is not symmetric, it is still a Markov process.
Quadratic Variation of S.R.W.
The quadratic variation up to time is defined to be
Note the difference between (an average over all paths), and (pathwise property)
Scaled S.R.W.
Goal: to approximate Brownian Motion
1. new time interval is "very small" of instead of 12. magnitude is "small" of instead of 1.For any can be defined as a linear interpolation between the nearest such that .
Properties of Scaled S.R.W.
(i) independent increments: for any are independent. Each is an integer.
(ii) Stationarity: for any ,
(iii) Martingale property
(iv) Markov Property: for any function , these exists a function so that
(v) Quadratic variation: for any ,
Limiting (Marginal) Distribution of S.R.W.
Theorem 3.2.1. (Central Limit Theorem)For any fixed ,
in dist.or
Proof: shown in class.
A Numerical Example
Log-Normality as the Limit of the Binomial Model
Theorem 3.2.2. (Central Limit Theorem)For any fixed ,
in the distribution sense, where , ,and
What is Brownian Motion?
"If is a continuous process with independent increments that are normally distributed, then is a Brownian motion."
Standard Brownian Motions
check Definition 3.3.1 in the text.Definition of SBM: Let the stochastic process under a probability space be continuous and satisfy:1. 2. 3. is independent of for .
Covariance Matrix
Check for any nonnegative and For any vector with
In fact,
Joint Moment-Generating Function of BM
π (π’1 ,π’2 ,β― ,π’π )=πΈ [ππ₯π (οΏ½βοΏ½ βπ ) ]=πΈ [ππ₯π (π’1π π‘1+π’2π π‘ 2
+β―+π’ππ π‘π )]=ππ₯π {12 (π’1+π’2+β―+π’π )2π‘1+12
(π’2+β―+π’π )2 (π‘ 2βπ‘ 1 )+β―+ 12π’π2 (π‘πβπ‘1 )}
Alternative Characteristics of BrownianMotion (Theorem 3.3.2)
For any continuous process with , the following three properties are equivalent.(i) increments are independent and normally distributed.(ii) For any , are jointly normally distributed.(ii) has the joint moment-generating function as before.If any of the three holds, then , is a SBM.
Filtration for B.M.
Definition 3.3.3 Let be a probability space on which the B.M. is defined. A filtration for the B.M. is a collection of -algebras , satisfying(i) (Information accumulates) For , .(ii) (Adaptivity) each is -measurable.(iii) (Independence of future increments) , the increment is independent of . [Note, this property leads to Efficient Market Hypothesis.]
Martingale property
Theorem 3.3.4 B.M. is a martingale.Proof:
Levy's Characteristics of Brownian Motion
The process is SBM iff the conditional characterization function is
Variations: First-Order (Total) Variation
Given a function defined on , the total variation is defined by
where the partition and
If is differentiable,
for some . Then .
Quadratic Variation
Def. 3.4.1 The quadratic variation of up to time is defined by
If is continuous differentiable,
for some . Then
Quadratic Variation of B.M.
Thm. 3.4.3 Let be a Brownian Motion. Then for all a.s..B.M. accumulates quadratic variation at rate one per unit time.Informal notion:, ,
Geometric Brownian Motion
The geometric Brownian motion is a process of the following form
where is the current value, is a B.M., is the drift and is the volatility.For each partition , define the log returns
Volatility Estimation of GBM
The realized variance is defined by
which converges to as
BM is a Markov process
Thm. 3.5.1 Let be a B.M. and be a filtration for this B.M.. Then(1)Wt0 is a Markov process.
Thm. 3.6.1.(2) is martingale.(We call exponential martingale.)Note: