Financial Derivatives/Notions of Stochastic Calculus

Stochastic Process

A stochastic process X is an indexed collection of random variables:

X_t(ω)

Where $\omega \in \Omega$ our sample space, and $t\in T$ is the index of the process which may be either discrete or continuous. Typically, in finance, T is an interval [a,b] and we deal with a continuous process. In this text we interpret T as the time.

If we fix a $t\in T$ the stochastic process becomes the random variable:

X_t = X_t(ω)

On the other hand, if we fix the outcome of our random experiment to $\omega \in \Omega$ we obtain a deterministic function of time: a realization or sample path of the process.

Brownian Motion

A stochastic process W_t(ω) with $t\in[0,\infty]$ is called a Wiener Procees (or Brownian Motion) if:

- W₀ = 0

- It has independent, stationary increments. Let $s \leq t$, then: $X_{t_2} - X_{t_1}, \ldots, X_{t_n} - X_{t_{n-1}}$ are independent. And $X_t - X_s = X_{t+h} - X_{s+h} \sim \mathcal{N}(0,t-s)$

- W_t is almost surely continuous

References

Wikipedia on Stochastic Process Wikipedia on Wiener Process