Itô's Lemma

In mathematics, Itô's lemma or Itô's formula (also called the Itô-Doeblin formula, especially in French literature) 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. It can be heuristically derived by forming the Taylor series expansion of the function up to its second derivatives and retaining terms up to first order in the time increment and second order in the Wiener process increment. 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.

Motivation

Suppose we are given the stochastic differential equation
$dX_t = \mu_t\ dt + \sigma_t\ dB_t,$
where is a Wiener process and the functions $\mu_t, \sigma_t$ are deterministic (not stochastic) functions of time. In general, it's not possible to write a solution $X_t$ directly in terms of $B_t.$ However, we can formally write an integral solution
$X_t = \int_0^t \mu_s\ ds + \int_0^t \sigma_s\ dB_s.$

This expression lets us easily read off the mean and variance of $X_t$ (which has no higher moments). First, notice that every $\mathrmB_t$ individually has mean 0, so the expectation value of $X_t$ is simply the integral of the drift function:
\mathrm E _t\int_0^t \mu_s\ ds.

Similarly, because the $dB$ terms have variance 1 and no correlation with one another, the variance of $X_t$ is simply the integral of the variance of each infinitesimal step in the random walk:
\mathrm _t= \int_0^t\sigma_s^2\ ds.

However, sometimes we are faced with a stochastic differential equation for a more complex process $Y_t,$ in which the process appears on both sides of the differential equation. That is, say
$dY_t = a_1(Y_t,t) \ dt + a_2(Y_t,t)\ dB_t,$
for some functions $a_1$ and $a_2.$ In this case, we cannot immediately write a formal solution as we did for the simpler case above. Instead, we hope to write the process $Y_t$ as a function of a simpler process $X_t$ taking the form above. That is, we want to identify three functions $f(t,x), \mu_t,$ and $\sigma_t,$ such that $Y_t=f(t, X_t)$ and $dX_t = \mu_t\ dt + \sigma_t\ dB_t.$ In practice, Ito's lemma is used in order to find this transformation. Finally, once we have transformed the problem into the simpler type of problem, we can determine the mean and higher moments of the process.

Informal derivation

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.

Suppose is an Itô drift-diffusion process that satisfies the stochastic differential equation

:$dX_t= \mu_t \, dt + \sigma_t \, dB_t,$

where is a Wiener process.

If is a twice-differentiable scalar function, its expansion in a Taylor series is

:$df = \frac\,dt + \frac\,dx + \frac\frac\,dx^2 + \cdots .$

Substituting for and therefore for gives

:$df = \frac\,dt + \frac(\mu_t\,dt + \sigma_t\,dB_t) + \frac\frac \left (\mu_t^2\,dt^2 + 2\mu_t\sigma_t\,dt\,dB_t + \sigma_t^2\,dB_t^2 \right ) + \cdots.$

In the limit , the terms and tend to zero faster than , which is . Setting the and terms to zero, substituting for (due to the quadratic variation of a Wiener process), and collecting the and terms, we obtain

:$df = \left(\frac + \mu_t\frac + \frac\frac\right)dt + \sigma_t\frac\,dB_t$

as required.

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 (due to: Kunita–Watanabe)

In its simplest form, Itô's lemma states the following: for an Itô drift-diffusion process

:$dX_t= \mu_t \, dt + \sigma_t \, dB_t$

and any twice differentiable scalar function of two real variables and , one has

:$df(t,X_t) =\left(\frac + \mu_t \frac + \frac\frac\right)dt+ \sigma_t \frac\,dB_t.$

This immediately implies that is itself an Itô drift-diffusion process.

In higher dimensions, if $\mathbf_t = (X^1_t, X^2_t, \ldots, X^n_t)^T$ is a vector of Itô processes such that

:$d\mathbf_t = \boldsymbol_t\, dt + \mathbf_t\, d\mathbf_t$

for a vector $\boldsymbol_t$ and matrix $\mathbf_t$, Itô's lemma then states that

:$\begin df(t,\mathbf_t) &= \frac\, dt + \left (\nabla_\mathbf f \right )^T\, d\mathbf_t + \frac \left(d\mathbf_t \right )^T \left( H_\mathbf f \right) \, d\mathbf_t, \\ &= \left\ dt + \left (\nabla_\mathbf f \right)^T \mathbf_t\, d\mathbf_t \end$

where $\nabla_\mathbf f$ is the gradient of w.r.t. , is the Hessian matrix of w.r.t. , and is the trace operator.

Poisson jump processes

We may also define functions on discontinuous stochastic processes.

Let 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. 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

:$d p_s(t) = -p_s(t) h(t) \, dt.$

So

:$p_s(t) = \exp \left(-\int_0^t h(u) \, du \right).$

Let be a discontinuous stochastic process. Write $S(t^-)$ for the value of ''S'' as we approach ''t'' from the left. Write $d_j S(t)$ for the non-infinitesimal change in as a result of a jump. Then

:$d_j S(t)=\lim_(S(t+\Delta t)-S(t^-))$

Let ''z'' be the magnitude of the jump and let $\eta(S(t^-),z)$ be the distribution of ''z''. The expected magnitude of the jump is

:E _j S(t)h(S(t^-)) \, dt \int_z z \eta(S(t^-),z) \, dz.

Define $d J_S(t)$, a compensated process and martingale, as

:d J_S(t)=d_j S(t)-E _j S(t)S(t)-S(t^-) - \left ( h(S(t^-))\int_z z \eta \left (S(t^-),z \right) \, dz \right ) \, dt.

Then

:d_j S(t) = E _j S(t)+ d J_S(t) = h(S(t^-)) \left (\int_z z \eta(S(t^-),z) \, dz \right ) dt + d J_S(t).

Consider a function $g(S(t),t)$ of the jump process . If jumps by then jumps by . is drawn from distribution $\eta_g()$ which may depend on $g(t^-)$, ''dg'' and $S(t^-)$. The jump part of $g$ is

:$g(t)-g(t^-) =h(t) \, dt \int_ \, \Delta g \eta_g(\cdot) \, d\Delta g + d J_g(t).$

If $S$ contains drift, diffusion and jump parts, then Itô's Lemma for $g(S(t),t)$ is

:$d g(t) = \left( \frac+\mu \frac+\frac \frac + h(t) \int_ \left (\Delta g \eta_g(\cdot) \, dg \right ) \, \right) dt + \frac \sigma \, d W(t) + d J_g(t).$

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.

Non-continuous semimartingales

Itô's lemma can also be applied to general -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 , the left limit in is denoted by , which is a left-continuous process. The jumps are written as . Then, Itô's lemma states that if is a -dimensional semimartingale and ''f'' is a twice continuously differentiable real valued function on then ''f''(''X'') is a semimartingale, and

:\begin
f(X_t)
&=
f(X_0)
+\sum_^d\int_0^t f_(X_)\,dX^i_s
+ \frac\sum_^d \int_0^t f_(X_)\,d ^i,X^js\\
&\qquad+ \sum_ \left(\Delta f(X_s)-\sum_^df_(X_)\,\Delta X^i_s
-\frac\sum_^d f_(X_)\,\Delta X^i_s \, \Delta X^j_s\right).
\end

This differs from the formula for continuous semi-martingales by the additional term summing over the jumps of ''X'', which ensures that the jump of the right hand side at time is Δ''f''(''X_t'').

Multiple non-continuous jump processes

There is also a version of this for a twice-continuously differentiable in space once in time function f evaluated at (potentially different) non-continuous semi-martingales which may be written as follows:
:\begin
f(t,X^1_t,\ldots,X^d_t)
= &
f(0,X^1_0,\ldots,X^d_0)
+\int_0^t \dot(,X^1_,\ldots,X^d_) d\\
& +\sum_^d \int_0^t f_(,X^1_,\ldots,X^d_)\,dX^_s\\
& + \frac\sum_^d \int_0^t f_(,X^1_,\ldots,X^d_)\,dX^_s\cdots X^_s\\
& + \sum_
\left f(s,X^1_s,\ldots,X^d_s) - f(,X^1_,\ldots,X^d_)
\right\end

where $X^$ denotes the continuous part of the ''i''th semi-martingale.

Examples

Geometric Brownian motion

A process S is said to follow a geometric Brownian motion with constant volatility ''σ'' and constant drift ''μ'' if it satisfies the stochastic differential equation $dS_t = \sigma S_t\,dB_t + \mu S_t\,dt$, for a Brownian motion ''B''. Applying Itô's lemma with $f(S_t) = \log(S_t)$ gives

:$\begin df & = f^\prime(S_t)\,dS_t + \fracf^ (S_t) (dS_t)^2 \\ & = \frac\,dS_t + \frac (-S_t^) (S_t^2\sigma^2\,dt) \\ & = \frac \left( \sigma S_t\,dB_t + \mu S_t\,dt\right) - \frac\sigma^2\,dt \\ &= \sigma\,dB_t +\left (\mu-\tfrac \right )\,dt. \end$

It follows that

:$\log (S_t) = \log (S_0) + \sigma B_t + \left (\mu-\tfrac \right )t,$

exponentiating gives the expression for ''S'',

:$S_t=S_0\exp\left(\sigma B_t+ \left (\mu-\tfrac \right )t\right).$

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 concave (or convex upwards), 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 same factor of appears in the ''d''₁ and ''d''₂ auxiliary variables of the Black–Scholes formula, and can be interpreted as a consequence of Itô's lemma.

Doléans-Dade exponential

The Doléans-Dade exponential (or stochastic exponential) of a continuous semimartingale ''X'' can be defined as the solution to the SDE with initial condition . It is sometimes denoted by .
Applying Itô's lemma with ''f''(''Y'') = log(''Y'') gives

:\begin
d\log(Y) &= \frac\,dY -\frac\,d \\ pt&= dX - \tfrac\,d
\end

Exponentiating gives the solution

:Y_t = \exp\left(X_t-X_0-\tfrac t\right).

Black–Scholes formula

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 . Then, if the value of an option at time is ''f''(''t'', ''S_t''), Itô's lemma gives

:$df(t,S_t) = \left(\frac + \frac\left(S_t\sigma\right)^2\frac\right)\,dt +\frac\,dS_t.$

The term represents the change in value in time ''dt'' of the trading strategy consisting of holding an amount 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

:$dV_t = r\left(V_t-\fracS_t\right)\,dt + \frac\,dS_t.$

This strategy replicates the option if ''V'' = ''f''(''t'',''S''). Combining these equations gives the celebrated Black–Scholes equation

:$\frac + \frac\frac + rS\frac-rf = 0.$

Product rule for Itô processes

Let $\mathbf X_t$ be a two-dimensional Ito process with SDE:
:$d\mathbf X_t = d\beginX_t^1\\ X_t^2\end = \begin\mu_t^1\\ \mu_t^2\end dt + \begin\sigma_t^1\\ \sigma_t^2\end \, dB_t$

Then we can use the multi-dimensional form of Ito's lemma to find an expression for $d(X_t^1X_t^2)$.

We have $\mu_t=\begin\mu_t^1\\ \mu_t^2\end$ and $\mathbf G = \begin\sigma_t^1\\ \sigma_t^2\end$.

We set $f(t,\mathbf X_t)=X_t^1X_t^2$ and observe that $\frac=0,\ (\nabla_\mathbf Xf)^T = (X_t^2\ \ X_t^1)$ and $H_\mathbf Xf=\begin0&1\\1&0\end$

Substituting these values in the multi-dimensional version of the lemma gives us:

:$\begin d(X_t^1X_t^2) &= df(t,\mathbf X_t)\\ &= 0\cdot dt + (X_t^2\ \ X_t^1) \, d\mathbf X_t + \frac12 (dX_t^1\ \ dX_t^2)\begin0&1\\1&0\end \begindX_t^1\\dX_t^2\end\\ &=X_t^2 \, dX_t^1 + X^1_t dX_t^2 + dX_t^1 \, dX_t^2 \end$

This is a generalisation of Leibniz's product rule to Ito processes, which are non-differentiable.

Further, using the second form of the multidimensional version above gives us

:\begin
d(X_t^1X_t^2)
&=\left\ \, dt
+ (X_t^2\sigma_t^1 + X^1_t \sigma_t^2) \, dB_t\\ pt&=
\left(X_t^2\mu_t^1 + X^1_t \mu_t^2 + \sigma_t^1\sigma_t^2\right) \, dt
+ (X_t^2\sigma_t^1 + X^1_t \sigma_t^2) \, dB_t
\end

so we see that the product $X_t^1X_t^2$ is itself an Itô drift-diffusion process.

See also

*Wiener process
* Itô calculus
* Feynman–Kac formula
* Euler–Maruyama method

Notes

References

*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. . Sections 4.1 and 4.2.
*Philip E Protter (2005). ''Stochastic Integration and Differential Equations'', 2nd edition. Springer. . Section 2.7.

External links

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Prof. Thayer Watkins

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