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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.

Diffusion Processes and Ito’s Lemma

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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?

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

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

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.

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)

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

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

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

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

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).

Therefore,

which can be written as

where

So, m also follows a geometric Brownian motion process

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.

- 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.

- 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

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

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

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