Chapter10 Applications of Stochastic

Chapter10 - Chapter 10 Applications of Stochastic Calculus in Finance Since Black and Scholes(1973 and Merton(1973 the idea of using stochastic

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Chapter 10 Applications of Stochastic Calculus in Finance Since Black and Scholes (1973) and Merton (1973), the idea of using stochastic calculus for modelling prices of risky assets (e.g., share prices of stock, stock indices such as Dow Jones, foreign exchange rates, interest rates, etc.) has been generally accepted. This led to a new branch of applied probability theory, the field of mathematical finance. It is a symbiosis of stochastic modelling, economic reasoning and practical financial engineering. In this chapter, we consider the Black-Scholes model for pricing options (in particular, a European option), using two different approaches (with em- phasis on the latter): (i) PDE approach; (ii) probabilistic approach The basic financial concepts needed are: bond, stock, option, portfo- lio, volatility, trading strategy, hedging, maturity of a contract, self-financing, arbitrage . On the other hand, some terminology in stochastic calculus will be encountered such as: Brownian motion (BM), Geometric BM, Ito’s inte- gral, Stochastic differential equivation (SDE), martingale, equivalent martin- gale measure, etc. The key tools include • Ito’s Lemma, • Girsanov Theorem (change of measure theorem), • Martingale representation theorem, • Feymann-Kac theorem 1 10.1 Introduction to some basic concepts We are given a filtered probability space (Ω , F , {F t ,t ∈ [0 ,T ] } ,P ). 1. Risky asset: stock The price S t of a risky asset (called stock ) is supposed to follow a geometric Brownian motion (BM): dS t S t = μdt + σdB t ( P ) ⇐⇒ S t = S e ( μ- . 5 σ 2 ) t + σB t ( P ) (1.1) Remark 10.1 (a) The measure P is also called the market measure. Formally, we have E ˆ dS t S t ! = μdt, and V ar ˆ dS t S t ! = σ 2 V ar ( dB t ) = σ 2 dt. So μ and σ are the mean and standard deviation (or volatility ) of rate of return (or relative return, or log return) per unit of time. (b) In (6.19), the rates of return are assumed to be: (i) constant per unit of time; (ii) independent for different time intervals. How- ever, empirical evidence indicates that, although the rates of return are uncorrelated for different time periods, they are not independent since the absolute values or the squares of rates of returns are corre- lated). Therefore, the geometric BM is only a reasonable, but crude, first approximation to a real price process. Incidentally, in time series, GARCH models are introduced to take care of the correlated volatilities. (c) If σ 2 = 0 , then S t = S e μt . Namely, taking away the noise, stock markets grow exponentially. People in economics believe in expo- nential growth, and empirical evidence generally supports this belief as well....
View Full Document

This note was uploaded on 02/01/2012 for the course MATH 5010 taught by Professor D during the Spring '11 term at HKU.

Page1 / 17

Chapter10 - Chapter 10 Applications of Stochastic Calculus in Finance Since Black and Scholes(1973 and Merton(1973 the idea of using stochastic

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online