In mathematics, Itô's lemma is an identity used in Itô calculus to find the differential of a time-dependent function of a stochastic process. It serves as the stochastic calculus counterpart of the chain rule. Typically, it is memorized by forming the Taylor series expansion of the function up to its second derivatives and identifying the square of an increment in the Wiener process with an increment in time. The lemma is widely employed in mathematical finance, and its best known application is in the derivation of the Black–Scholes equation for option values.
- 1 Informal derivation
- 2 Mathematical formulation of Itô's lemma
- 3 Examples
- 4 See also
- 5 Notes
- 6 References
- 7 External links
A formal proof of the lemma relies on taking the limit of a sequence of random variables. This approach is not presented here since it involves a number of technical details. Instead, we give a sketch of how one can derive Itô's lemma by expanding a Taylor series and applying the rules of stochastic calculus.
Substituting Xt for x and μt dt + σt dBt for dXt gives
In the limit as dt → 0, the terms dt2 and dt dBt tend to zero faster than dB2, which is O(dt). Setting the dt2 and dt dBt terms to zero, substituting dt for dB2, and collecting the dt and dB terms, we obtain
Mathematical formulation of Itô's lemma
In the following subsections we discuss versions of Itô's lemma for different types of stochastic processes.
Itô drift-diffusion processes
In its simplest form, Itô's lemma states the following: for an Itô drift-diffusion process
and any twice differentiable scalar function f(t,x) of two real variables t and x, one has
This immediately implies that f(t,Xt) is itself an Itô drift-diffusion process.
In higher dimensions, if is a vector of Itô processes such that
for a vector and matrix , Itô's lemma then states that
Poisson jump processes
We may also define functions on discontinuous stochastic processes.
Let h be the jump intensity. The Poisson process model for jumps is that the probability of one jump in the interval [t, t + Δt] is hΔt plus higher order terms. h could be a constant, a deterministic function of time, or a stochastic process. The survival probability ps(t) is the probability that no jump has occurred in the interval [0, t]. The change in the survival probability is
Let S(t) be a discontinuous stochastic process. Write for the value of S as we approach t from the left. Write for the non-infinitesimal change in S(t) as a result of a jump. Then
Let z be the magnitude of the jump and let be the distribution of z. The expected magnitude of the jump is
Consider a function of the jump process dS(t). If S(t) jumps by Δs then g(t) jumps by Δg. Δg is drawn from distribution which may depend on , dg and . The jump part of is
If contains drift, diffusion and jump parts, then Itô's Lemma for is
Itô's lemma for a process which is the sum of a drift-diffusion process and a jump process is just the sum of the Itô's lemma for the individual parts.
Itô's lemma can also be applied to general d-dimensional semimartingales, which need not be continuous. In general, a semimartingale is a càdlàg process, and an additional term needs to be added to the formula to ensure that the jumps of the process are correctly given by Itô's lemma. For any cadlag process Yt, the left limit in t is denoted by Yt−, which is a left-continuous process. The jumps are written as ΔYt = Yt − Yt−. Then, Itô's lemma states that if X = (X1, X2, ..., Xd) is a d-dimensional semimartingale and f is a twice continuously differentiable real valued function on Rd then f(X) is a semimartingale, and
This differs from the formula for continuous semimartingales by the additional term summing over the jumps of X, which ensures that the jump of the right hand side at time t is Δf(Xt).
Geometric Brownian motion
A process S is said to follow a geometric Brownian motion with volatility σ and drift μ if it satisfies the stochastic differential equation dS = S(σdB + μdt), for a Brownian motion B. Applying Itô's lemma with f(S) = log(S) gives
It follows that
exponentiating gives the expression for S,
The correction term of − σ2/ corresponds to the difference between the median and mean of the log-normal distribution, or equivalently for this distribution, the geometric mean and arithmetic mean, with the median (geometric mean) being lower. This is due to the AM–GM inequality, and corresponds to the logarithm being convex down, so the correction term can accordingly be interpreted as a convexity correction. This is an infinitesimal version of the fact that the annualized return is less than the average return, with the difference proportional to the variance. See geometric moments of the log-normal distribution for further discussion.
The Doléans-Dade exponential (or stochastic exponential) of a continuous semimartingale X can be defined as the solution to the SDE dY = Y dX with initial condition Y0 = 1. It is sometimes denoted by Ɛ(X). Applying Itô's lemma with f(Y) = log(Y) gives
Exponentiating gives the solution
Itô's lemma can be used to derive the Black–Scholes equation for an option. Suppose a stock price follows a geometric Brownian motion given by the stochastic differential equation dS = S(σdB + μ dt). Then, if the value of an option at time t is f(t, St), Itô's lemma gives
The term ∂f/ dS represents the change in value in time dt of the trading strategy consisting of holding an amount ∂ f/ of the stock. If this trading strategy is followed, and any cash held is assumed to grow at the risk free rate r, then the total value V of this portfolio satisfies the SDE
This strategy replicates the option if V = f(t,S). Combining these equations gives the celebrated Black–Scholes equation
- Kiyosi Itô (1944). Stochastic Integral. Proc. Imperial Acad. Tokyo 20, 519-524. This is the paper with the Ito Formula; Online
- Kiyosi Itô (1951). On stochastic differential equations. Memoirs, American Mathematical Society 4, 1–51. Online
- Bernt Øksendal (2000). Stochastic Differential Equations. An Introduction with Applications, 5th edition, corrected 2nd printing. Springer. ISBN 3-540-63720-6. Sections 4.1 and 4.2.