In mathematics, Itō's lemma is an identity used in Itō 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. It is named after Kiyoshi Itō. It is best memorized using the Taylor series expansion of the function up to its second derivatives and identifying the square of an increment in the stochastic 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. Ito's Lemma is occasionally referred to as the Itō–Doeblin Theorem in recognition of the recently discovered work of Wolfgang Doeblin.
- 1 Mathematical formulation of Itō's lemma
- 2 Informal derivation
- 3 Examples
- 4 See also
- 5 Notes
- 6 References
- 7 External links
Mathematical formulation of Itō's lemma
In the following subsections we discuss versions of Itō's lemma for different types of stochastic processes.
Itō drift-diffusion processes
In its simplest form, Itō's lemma states the following: for an Itō drift-diffusion process
and any twice differentiable scalar function ƒ(t, x) of two real variables t and x, one has
This immediately implies that ƒ(t,X) is itself an Itō drift-diffusion process.
In higher dimensions, Ito's lemma states
More generally, the above formula also holds for any continuous d-dimensional semimartingale X = (X1,X2,…,Xd), and twice continuously differentiable and real valued function f on Rd. Some people prefer to present the formula in another form with cross variation shown explicitly as follows, f(X) is a semimartingale satisfying
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 is plus higher order terms. h could be a constant, a deterministic function of time, or a stochastic process. The survival probability is the probability that no jump has occurred in the interval . The change in the survival probability is
Let 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 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 jump process . If jumps by then jumps by . is drawn from distribution which may depend on , dg and . The jump part of is
If contains drift, diffusion and jump parts, then Itō's Lemma for is
Itō's lemma for a process which is the sum of a drift-diffusion process and a jump process is just the sum of the Itō's lemma for the individual parts.
Itō'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 Itō'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, Itō'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).
A formal proof of the lemma requires us to take the limit of a sequence of random variables, which is not done here. Instead, we give a sketch of how one can derive Itō's lemma by expanding a Taylor series and applying the rules of stochastic calculus.
Assume the Itō process is in the form of
- (dB, Brownian motion)
Expanding f(x, t) in a Taylor series in x and t we have
and substituting a dt + b dB for dx gives
In the limit as dt tends to 0, the dt2 and dt dB terms disappear but the dB2 term tends to dt. The latter can be shown if we prove that
- since ; see Wiener process# Basic properties.
Deleting the dt2 and dt dB terms, substituting dt for dB2, and collecting the dt and dB terms, we obtain
The formal proof is somewhat technical and is beyond the current scope of this article.
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 Itō's lemma with f(S) = log(S) gives
It follows that log(St) = log(S0) + σBt + (μ − σ2/2)t, and exponentiating gives the expression for S,
The correction term of 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 exponential
The Doléans 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 Itō's lemma with f(Y) = log(Y) gives
Exponentiating gives the solution
Itō'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), Itō's lemma gives
The term (∂f/∂S) dS represents the change in value in time dt of the trading strategy consisting of holding an amount ∂f/∂S 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
- Kiyoshi Itō (1944). Stochastic Integral. Proc. Imperial Acad. Tokyo 20, 519-524. This is the paper with the Ito Formula; Online
- Kiyoshi Itō (1951). On stochastic differential equations. Memoirs, American Mathematical Society 4, 1–51. Online
- Hagen Kleinert (2004). Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets, 4th edition, World Scientific (Singapore); Paperback ISBN 981-238-107-4. Also available online: PDF-files. This textbook also derives generalizations of Itō's lemma for non-Wiener (non-Gaussian) processes.
- 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.
- Domingo Tavella (2002). Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance, John Wiley and Sons. ISBN 978-0-471-27479-7. Pages 36–39.