Diffusion processes and ito s lemma l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 32

Diffusion Processes and Ito’s Lemma PowerPoint PPT Presentation


  • 362 Views
  • Uploaded on
  • Presentation posted in: General

Diffusion Processes and Ito’s Lemma. Question : If we model asset prices as continuous time stochastic processes, can we identify trading strategies that hedge price risk? . 1. We want to show that Brownian motion can be viewed as the limit of a discrete time process.

Download Presentation

Diffusion Processes and Ito’s Lemma

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Diffusion processes and ito s lemma l.jpg

Diffusion Processes and Ito’s Lemma

Question: If we model asset prices as continuous time stochastic processes, can we identify trading strategies that hedge price risk?


1 we want to show that brownian motion can be viewed as the limit of a discrete time process l.jpg

1. We want to show that Brownian motion can be viewed as the limit of a discrete time process.

Define a stochastic process z(t) at date t and let the discrete change in time be the interval Δt.


How does z t change over time l.jpg

How does z(t) change over time?


Slide4 l.jpg

From (8.3) we can write the first two moments of z(T) – z(0)


Continuous time limit l.jpg

Continuous time limit

Now take the limit of z(T) – z(0) as the number of intervals goes to infinity, assuming that the є are IID. Then, by the Central Limit Theorem

and z(T) – z(0) has a normal distribution.

The central limit theorem states conditions under which the mean of a sufficiently large number of IID (identically and independently distributed) random variables, each with finite mean and variance, will be approximately normally distributed.


Slide6 l.jpg

A Wiener (continuous time) process dz(t) exhibits Brownian motion – a particular type of Markov stochastic process which has two properties - -

Δz = є√Δt during a small period of time Δt

Δz for any two Δt are independent (a Markov process)


Z t exhibits brownian motion a random walk l.jpg

z(t) exhibits Brownian motion (a random walk)


An it integral is the change in z t over a finite interval 0 t l.jpg

An Itô integral isthe change in z(t) over a finite interval [0,T]


2 we can use the basic wiener process 8 7 to construct a generalized wiener process 8 11 l.jpg

2. We can use the basic Wiener process (8.7) to construct a generalized Wiener process (8.11)


Arithmetic brownian motion l.jpg

arithmetic Brownian motion


If and can be time varying we can describe x t as a stochastic differential equation an it process l.jpg

If μ and σ can be “time-varying,” we can describe x(t) as a stochastic differential equation - - an Itô process


We can characterize the stochastic process f x t t by using it s lemma l.jpg

We can characterize the stochastic process F(x(t), t) by using Itô’s lemma


Slide15 l.jpg

Then x(t) = eF(t) has a lognormal distribution over any discrete interval, and geometric Brownian motion is lognormally distributed over any time interval.


3 geometric brownian motion is used for modeling stock prices l.jpg

3. Geometric Brownian motion is used for modeling stock prices

We note that a generalized Wiener process with constant drift rate and constant variance rate would fail to account for the characteristic that the expected return (μ) required by investors is independent of the stock price (S). (We assume the stock pays no dividends.)

We need to have it that the expected return (expected drift divided by the stock price) is a constant.

Then, the expected increase in S = μSΔt

And, if the volatility of stock price = 0, we also have it that ΔS = μSΔt


Geometric brownian motion l.jpg

geometric Brownian motion

In the limit as Δt →0, dS = μS dt

or, dS/S = μ dt

Integrating from [0,T] we get ST = S0e μT

=> when the variance rate is zero, the stock price grows at a continuously compounded rate μ per unit of time.

In reality stock prices exhibit volatility, so we need to modify this approach. Let’s assume that σ is proportional to S (i.e., investors are just as uncertain at $10 /share as they are at $50/share)

Then, dS = μS dt + σ S dz

or, dS/S = μ dt + σ dz


A geometric brownian motion process l.jpg

a geometric Brownian motion process


A discrete time example l.jpg

A discrete time example

Assume a stock that pays no dividends, has volatility σ = .30, and has expected return μ = .15

If the process for stock price is

dS = 0.15 S dt + 0.30 S dz(price)

dS/S = 0.15 dt + 0.30 dz(return)

Rewriting in discrete time intervals,

ΔS/S = 0.15*Δt + 0.30*є√Δt where є ~ N(0,1)

For time interval of 1 week (0.0192 years),

ΔS/S = 0.00288 + 0.0416 є

ΔS = 0.00288 S + 0.0416 S є


Figure 8 2 sample paths for geometric brownian motion where 0 10 0 30 t 2 l.jpg

Figure 8.2: Sample paths for geometric Brownian motion, where μ=0.10, σ=0.30, T=2


Problem 2 pennacchi l.jpg

Problem #2 (Pennacchi)

Let P be a price index, such as the Consumer Price Index. Let M equal the nominal supply of money in the economy. Assume P and M each follow geometric Brownian motion processes,

with dzp dzm = ρdt.

Monetary economists define real money balances, m = M/P.

Derive the stochastic process for m (the bivariate case).


Applying it s lemma to the two state variables l.jpg

Applying Itô’s lemma to the two state variables

Therefore,

which can be written as

where

So, m also follows a geometric Brownian motion process


4 extending diffusion processes to include jump processes pennacchi ch 11 l.jpg

4. Extending diffusion processes to include jump processes (Pennacchi, Ch. 11)

Brownian motion processes may not be realistic for modeling economic and financial time series where the paths of the random variables are not continuous, rather they exhibit discontinuities or “jumps.” Think of these as asset price shocks.

We need to augment the diffusion process with another source of uncertainty to capture that price discontinuity. For this purpose we can use the sum of a Brownian motion diffusion process and a Poisson jump process.

Note: A Poisson process is a continuous-time counting process N( ) with the following properties:

  • N(0) = 0,

  • the numbers of occurrences counted in disjoint intervals are independent from each other,

  • the probability distribution of the number of occurrences counted in any time interval only depends on the length of the interval, and

  • none of the occurrences are simultaneous.


Jumps in continuous time l.jpg

Jumps in continuous time

  • Assume the continuous time process for a stock,

    where dz is the Wiener process and dq is a Poisson jump process with the characteristics

    During each time interval (dt) there is a λdt probability that q will jump once during the interval, where λ is assumed to be a constant and (Y-1) is i.i.d. When a jump occurs dS = (Y-1)S, and S → YS.


It s lemma for jump diffusion processes l.jpg

Itô’s lemma for jump diffusion processes

  • Let c(S,t) be the value of a variable that is a twice-differentiable function of S(t), and S(t) follows the jump diffusion process

  • Extending Itô’s lemma to this case, the value of c(S,t) is


Slide28 l.jpg

Define μc as the instantaneous expected rate of return on c, i.e., E[dc/c], conditional on a jump not occurring.

From (11.5) we get

where kc(t) is the time-varying expected jump size of c(S,t), and


Valuing the contingent claim l.jpg

Valuing the contingent claim

Now develop a portfolio that includes a contingent claim (call option) with price c, an underlying asset that follows the jump diffusion process in (11.1), and a riskless asset that pays return r.

Let the proportions invested in the portfolio be: w1 in the underlying asset, w2 in the contingent claim, w3 = (1 - w1 - w2) in the riskless asset.

The instantaneous rate of return on the portfolio, with substitutions for dS/S from (11.1) and dc/c from (11.7), is


Imperfect hedge jump size is random l.jpg

Imperfect hedge (jump size is random)


Slide32 l.jpg

Hedge portfolio return with jump - - during “quiet times” the call writer earns a profit, but during the infrequent “active times” the call writer realizes a loss


  • Login