Processes with Independent Increments

In a previous post, it was seen that all continuous processes with independent increments are Gaussian. We move on now to look at a much more general class of independent increments processes which need not have continuous sample paths. Such processes can be completely described by their jump intensities, a Brownian term, and a deterministic drift component. However, this class of processes is large enough to capture the kinds of behaviour that occur for more general jump-diffusion processes. An important subclass is that of Lévy processes, which have independent and stationary increments. Lévy processes will be looked at in more detail in the following post, and includes as special cases, the Cauchy process, gamma processes, the variance gamma process, Poisson processes, compound Poisson processes and Brownian motion.

Recall that a process ${\{X_t\}_{t\ge0}}$ has the independent increments property if ${X_t-X_s}$ is independent of ${\{X_u\colon u\le s\}}$ for all times ${0\le s\le t}$ . More generally, we say that X has the independent increments property with respect to an underlying filtered probability space ${(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t\ge0},{\mathbb P})}$ if it is adapted and ${X_t-X_s}$ is independent of ${\mathcal{F}_s}$ for all ${s < t}$ . In particular, every process with independent increments also satisfies the independent increments property with respect to its natural filtration. Throughout this post, I will assume the existence of such a filtered probability space, and the independent increments property will be understood to be with regard to this space.

The process X is said to be continuous in probability if ${X_s\rightarrow X_t}$ in probability as s tends to t. As we now state, a d-dimensional independent increments process X is uniquely specified by a triple ${(\Sigma,b,\mu)}$ where ${\mu}$ is a measure describing the jumps of X, ${\Sigma}$ determines the covariance structure of the Brownian motion component of X, and b is an additional deterministic drift term.

Theorem 1 Let X be an ${{\mathbb R}^d}$ -valued process with independent increments and continuous in probability. Then, there is a unique continuous function ${{\mathbb R}^d\times{\mathbb R}_+\rightarrow{\mathbb C}}$ , ${(a,t)\mapsto\psi_t(a)}$ such that ${\psi_0(a)=0}$ and

$\displaystyle {\mathbb E}\left[e^{ia\cdot (X_t-X_0)}\right]=e^{i\psi_t(a)}$ (1)

for all ${a\in{\mathbb R}^d}$ and ${t\ge0}$ . Also, ${\psi_t(a)}$ can be written as

$\displaystyle \psi_t(a)=ia\cdot b_t-\frac{1}{2}a^{\rm T}\Sigma_t a+\int _{{\mathbb R}^d\times[0,t]}\left(e^{ia\cdot x}-1-\frac{ia\cdot x}{1+\Vert x\Vert}\right)\,d\mu(x,s)$ (2)

where ${\Sigma_t}$ , ${b_t}$ and ${\mu}$ are uniquely determined and satisfy the following,

${t\mapsto\Sigma_t}$ is a continuous function from ${{\mathbb R}_+}$ to ${{\mathbb R}^{d^2}}$ such that ${\Sigma_0=0}$ and ${\Sigma_t-\Sigma_s}$ is positive semidefinite for all ${t\ge s}$ .

${t\mapsto b_t}$ is a continuous function from ${{\mathbb R}_+}$ to ${{\mathbb R}^d}$ , with ${b_0=0}$ .

${\mu}$ is a Borel measure on ${{\mathbb R}^d\times{\mathbb R}_+}$ with ${\mu(\{0\}\times{\mathbb R}_+)=0}$ , ${\mu({\mathbb R}^d\times\{t\})=0}$ for all ${t\ge 0}$ and,

$\displaystyle \int_{{\mathbb R}^d\times[0,t]}\Vert x\Vert^2\wedge 1\,d\mu(x,s)<\infty.$ (3)

Furthermore, ${(\Sigma,b,\mu)}$ uniquely determine all finite distributions of the process ${X-X_0}$ .

Conversely, if ${(\Sigma,b,\mu)}$ is any triple satisfying the three conditions above, then there exists a process with independent increments satisfying (1,2).

Continue reading “Processes with Independent Increments” →

Bessel Processes

A random variable ${N=(N^1,\ldots,N^n)}$ has the standard n-dimensional normal distribution if its components ${N^i}$ are independent normal with zero mean and unit variance. A well known fact of such distributions is that they are invariant under rotations, which has the following consequence. The distribution of ${Z\equiv\Vert N+\boldsymbol{\mu}\Vert^2}$ is invariant under rotations of ${\boldsymbol{\mu}\in{\mathbb R}^n}$ and, hence, is fully determined by the values of ${n\in{\mathbb N}}$ and ${\mu=\Vert\boldsymbol{\mu}\Vert^2\in{\mathbb R}_+}$ . This is known as the noncentral chi-square distribution with n degrees of freedom and noncentrality parameter ${\mu}$ , and denoted by ${\chi^2_n(\mu)}$ . The moment generating function can be computed,

$\displaystyle M_Z(\lambda)\equiv{\mathbb E}\left[e^{\lambda Z}\right]=\left(1-2\lambda\right)^{-\frac{n}{2}}\exp\left(\frac{\lambda\mu}{1-2\lambda}\right),$

(1)

which holds for all ${\lambda\in{\mathbb C}}$ with real part bounded above by 1/2.

A consequence of this is that the norm ${\Vert B_t\Vert}$ of an n-dimensional Brownian motion B is Markov. More precisely, letting ${\mathcal{F}_t=\sigma(B_s\colon s\le t)}$ be its natural filtration, then ${X\equiv\Vert B\Vert^2}$ has the following property. For times ${s<t}$ , conditional on ${\mathcal{F}_s}$ , ${X_t/(t-s)}$ is distributed as ${\chi^2_n(X_s/(t-s))}$ . This is known as the `n-dimensional’ squared Bessel process, and denoted by ${{\rm BES}^2_n}$ .

A Poisson process sample path — Figure 1: Squared Bessel processes of dimensions n=1,2,3

Alternatively, the process X can be described by a stochastic differential equation (SDE). Applying integration by parts,

$\displaystyle dX = 2\sum_iB^i\,dB^i+\sum_id[B^i].$

(2)

As the standard Brownian motions have quadratic variation ${[B^i]_t=t}$ , the final term on the right-hand-side is equal to ${n\,dt}$ . Also, the covarations ${[B^i,B^j]}$ are zero for ${i\not=j}$ from which it can be seen that

$\displaystyle W_t = \sum_i\int_0^t1_{\{B\not=0\}}\frac{B^i}{\Vert B\Vert}\,dB^i$

is a continuous local martingale with ${[W]_t=t}$ . By Lévy’s characterization, W is a Brownian motion and, substituting this back into (2), the squared Bessel process X solves the SDE

$\displaystyle dX=2\sqrt{X}\,dW+ndt.$

(3)

The standard existence and uniqueness results for stochastic differential equations do not apply here, since ${x\mapsto2\sqrt{x}}$ is not Lipschitz continuous. It is known that (3) does in fact have a unique solution, by the Yamada-Watanabe uniqueness theorem for 1-dimensional SDEs. However, I do not need and will not make use of this fact here. Actually, uniqueness in law follows from the explicit computation of the moment generating function in Theorem 5 below.

Although it is nonsensical to talk of an n-dimensional Brownian motion for non-integer n, Bessel processes can be extended to any real ${n\ge0}$ . This can be done either by specifying its distributions in terms of chi-square distributions or by the SDE (3). In this post I take the first approach, and then show that they are equivalent. Such processes appear in many situations in the theory of stochastic processes, and not just as the norm of Brownian motion. It also provides one of the relatively few interesting examples of stochastic differential equations whose distributions can be explicitly computed.

The ${\chi^2_n(\mu)}$ distribution generalizes to all real ${n\ge0}$ , and can be defined as the unique distribution on ${{\mathbb R}_+}$ with moment generating function given by equation (1). If ${Z_1\sim\chi_m(\mu)}$ and ${Z_2\sim\chi_n(\nu)}$ are independent, then ${Z_1+Z_2}$ has moment generating function ${M_{Z_1}(\lambda)M_{Z_2}(\lambda)}$ and, therefore, has the ${\chi^2_{m+n}(\mu+\nu)}$ distribution. That such distributions do indeed exist can be seen by constructing them. The ${\chi^2_n(0)}$ distribution is a special case of the Gamma distribution and has probability density proportional to ${x^{n/2-1}e^{-x/2}}$ . If ${Z_1,Z_2,\ldots}$ is a sequence of independent random variables with the standard normal distribution and T independently has the Poisson distribution of rate ${\mu/2}$ , then ${\sum_{i=1}^{2T}Z_i^2\sim\chi_0^2(\mu)}$ , which can be seen by computing its moment generating function. Adding an independent ${\chi^2_n(0)}$ random variable Y to this produces the ${\chi^2_n(\mu)}$ variable ${Z\equiv Y+\sum_{i=1}^{2T}Z_i^2}$ .

The definition of squared Bessel processes of any real dimension ${n\ge0}$ is as follows. We work with respect to a filtered probability space ${(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t\ge0},{\mathbb P})}$ .

Definition 1 A process X is a squared Bessel process of dimension ${n\ge0}$ if it is continuous, adapted and, for any ${s<t}$ , conditional on ${\mathcal{F}_s}$ , ${X_t/(t-s)}$ has the ${\chi^2_n\left(X_s/(t-s)\right)}$ distribution.

Continue reading “Bessel Processes” →

Properties of Feller Processes

In the previous post, the concept of Feller processes was introduced. These are Markov processes whose transition function ${\{P_t\}_{t\ge0}}$ satisfies certain continuity conditions. Many of the standard processes we study satisfy the Feller property, such as standard Brownian motion, Poisson processes, Bessel processes and Lévy processes as well as solutions to many stochastic differential equations. It was shown that all Feller processes admit a cadlag modification. In this post I state and prove some of the other useful properties satisfied by such processes, including the strong Markov property, quasi-left-continuity and right-continuity of the filtration. I also describe the basic properties of the infinitesimal generators. The results in this post are all fairly standard and can be found, for example, in Revuz and Yor (Continuous Martingales and Brownian Motion).

As always, we work with respect to a filtered probability space ${(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t\ge0},{\mathbb P})}$ . Throughout this post we consider Feller processes X and transition functions ${\{P_t\}_{t\ge0}}$ defined on the lccb (locally compact with a countable base) space E which, taken together with its Borel sigma-algebra, defines a measurable space ${(E,\mathcal{E})}$ .

Recall that the law of a homogeneous Markov process X is described by a transition function ${\{P_t\}_{t\ge0}}$ on some measurable space ${(E,\mathcal{E})}$ . This specifies that the distribution of ${X_t}$ conditional on the history up until an earlier time ${s<t}$ is given by the measure ${P_{t-s}(X_s,\cdot)}$ . Equivalently,

$\displaystyle {\mathbb E}[f(X_t)\mid\mathcal{F}_s]=P_{t-s}f(X_s)$

for any bounded and measurable function ${f\colon E\rightarrow{\mathbb R}}$ . The strong Markov property generalizes this idea to arbitrary stopping times.

Definition 1 Let X be an adapted process and ${\{P_t\}_{t\ge 0}}$ be a transition function.

Then, X satisfies the strong Markov property if, for each stopping time ${\tau}$ , conditioned on ${\tau<\infty}$ the process ${\{X_{\tau+t}\}_{t\ge0}}$ is Markov with the given transition function and with respect to the filtration ${\{\mathcal{F}_{\tau+t}\}_{t\ge0}}$ .

As we see in a moment, Feller processes satisfy the strong Markov property. First, as an example, consider a standard Brownian motion B, and let ${\tau}$ be the first time at which it hits a fixed level ${K>0}$ . The reflection principle states that the process ${\tilde B}$ defined to be equal to B up until time ${\tau}$ and reflected about K afterwards, is also a standard Brownian motion. More precisely,

$\displaystyle \tilde B_t=\begin{cases} B_t,&\textrm{if }t\le\tau,\\ 2K-B_t,&\textrm{if }t\ge\tau, \end{cases}$

is a Brownian motion. This useful idea can be used to determine the distribution of the maximum ${B^*_t=\max_{s\le t}B_s}$ . If ${B^*_t\ge K}$ then either the process itself ends up above K or it hits K and then drops below this level by time t, in which case ${\tilde B_t>K}$ . So, by the reflection principle,

$\displaystyle {\mathbb P}(B^*_t\ge K)={\mathbb P}(B_t\ge K)+{\mathbb P}(\tilde B_t> K)=2{\mathbb P}(B_t\ge K).$

Continue reading “Properties of Feller Processes” →

Feller Processes

The definition of Markov processes, as given in the previous post, is much too general for many applications. However, many of the processes which we study also satisfy the much stronger Feller property. This includes Brownian motion, Poisson processes, Lévy processes and Bessel processes, all of which are considered in these notes. Once it is known that a process is Feller, many useful properties follow such as, the existence of cadlag modifications, the strong Markov property, quasi-left-continuity and right-continuity of the filtration. In this post I give the definition of Feller processes and prove the existence of cadlag modifications, leaving the further properties until the next post.

The definition of Feller processes involves putting continuity constraints on the transition function, for which it is necessary to restrict attention to processes lying in a topological space ${(E,\mathcal{T}_E)}$ . It will be assumed that E is locally compact, Hausdorff, and has a countable base (lccb, for short). Such spaces always possess a countable collection of nonvanishing continuous functions ${f\colon E\rightarrow{\mathbb R}}$ which separate the points of E and which, by Lemma 6 below, helps us construct cadlag modifications. Lccb spaces include many of the topological spaces which we may want to consider, such as ${{\mathbb R}^n}$ , topological manifolds and, indeed, any open or closed subset of another lccb space. Such spaces are always Polish spaces, although the converse does not hold (a Polish space need not be locally compact).

Given a topological space E, ${C_0(E)}$ denotes the continuous real-valued functions vanishing at infinity. That is, ${f\colon E\rightarrow{\mathbb R}}$ is in ${C_0(E)}$ if it is continuous and, for any ${\epsilon>0}$ , the set ${\{x\colon \vert f(x)\vert\ge\epsilon\}}$ is compact. Equivalently, its extension to the one-point compactification ${E^*=E\cup\{\infty\}}$ of E given by ${f(\infty)=0}$ is continuous. The set ${C_0(E)}$ is a Banach space under the uniform norm,

$\displaystyle \Vert f\Vert\equiv\sup_{x\in E}\vert f(x)\vert.$

We can now state the general definition of Feller transition functions and processes. A topological space ${(E,\mathcal{T}_E)}$ is also regarded as a measurable space by equipping it with its Borel sigma algebra ${\mathcal{B}(E)=\sigma(\mathcal{T})}$ , so it makes sense to talk of transition probabilities and functions on E.

Definition 1 Let E be an lccb space. Then, a transition function ${\{P_t\}_{t\ge 0}}$ is Feller if, for all ${f\in C_0(E)}$ ,

${P_tf\in C_0(E)}$ .

${t\mapsto P_tf}$ is continuous with respect to the norm topology on ${C_0(E)}$ .

${P_0f=f}$ .

A Markov process X whose transition function is Feller is a Feller process.

Continue reading “Feller Processes” →

Markov Processes

In these notes, the approach taken to stochastic calculus revolves around stochastic integration and the theory of semimartingales. An alternative starting point would be to consider Markov processes. Although I do not take the second approach, all of the special processes considered in the current section are Markov, so it seems like a good idea to introduce the basic definitions and properties now. In fact, all of the special processes considered (Brownian motion, Poisson processes, Lévy processes, Bessel processes) satisfy the much stronger property of being Feller processes, which I will define in the next post.

Intuitively speaking, a process X is Markov if, given its whole past up until some time s, the future behaviour depends only its state at time s. To make this precise, let us suppose that X takes values in a measurable space ${(E,\mathcal{E})}$ and, to denote the past, let ${\mathcal{F}_t}$ be the sigma-algebra generated by ${\{X_s\colon s\le t\}}$ . The Markov property then says that, for any times ${s\le t}$ and bounded measurable function ${f\colon E\rightarrow{\mathbb R}}$ , the expected value of ${f(X_t)}$ conditional on ${\mathcal{F}_s}$ is a function of ${X_s}$ . Equivalently,

$\displaystyle {\mathbb E}\left[f(X_t)\mid\mathcal{F}_s\right]={\mathbb E}\left[f(X_t)\mid X_s\right]$

(1)

(almost surely). More generally, this idea makes sense with respect to any filtered probability space ${\mathbb{F}=(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t\ge 0},{\mathbb P})}$ . A process X is Markov with respect to ${\mathbb{F}}$ if it is adapted and (1) holds for times ${s\le t}$ . Continue reading “Markov Processes” →

Poisson Processes

A Poisson process is a continuous-time stochastic process which counts the arrival of randomly occurring events. Commonly cited examples which can be modeled by a Poisson process include radioactive decay of atoms and telephone calls arriving at an exchange, in which the number of events occurring in each consecutive time interval are assumed to be independent. Being piecewise constant, Poisson processes have very simple pathwise properties. However, they are very important to the study of stochastic calculus and, together with Brownian motion, forms one of the building blocks for the much more general class of Lévy processes. I will describe some of their properties in this post.

A random variable N has the Poisson distribution with parameter ${\lambda}$ , denoted by ${N\sim{\rm Po}(\lambda)}$ , if it takes values in the set of nonnegative integers and

$\displaystyle {\mathbb P}(N=n)=\frac{\lambda^n}{n!}e^{-\lambda}$

(1)

for each ${n\in{\mathbb Z}_+}$ . The mean and variance of N are both equal to ${\lambda}$ , and the moment generating function can be calculated,

$\displaystyle {\mathbb E}\left[e^{aN}\right] = \exp\left(\lambda(e^a-1)\right),$

which is valid for all ${a\in{\mathbb C}}$ . From this, it can be seen that the sum of independent Poisson random variables with parameters ${\lambda}$ and ${\mu}$ is again Poisson with parameter ${\lambda+\mu}$ . The Poisson distribution occurs as a limit of binomial distributions. The binomial distribution with success probability p and m trials, denoted by ${{\rm Bin}(m,p)}$ , is the sum of m independent ${\{0,1\}}$ -valued random variables each with probability p of being 1. Explicitly, if ${N\sim{\rm Bin}(m,p)}$ then

$\displaystyle {\mathbb P}(N=n)=\frac{m!}{n!(m-n)!}p^n(1-p)^{m-n}.$

In the limit as ${m\rightarrow\infty}$ and ${p\rightarrow 0}$ such that ${mp\rightarrow\lambda}$ , it can be verified that this tends to the Poisson distribution (1) with parameter ${\lambda}$ .

Poisson processes are then defined as processes with independent increments and Poisson distributed marginals, as follows.

Definition 1 A Poisson process X of rate ${\lambda\ge0}$ is a cadlag process with ${X_0=0}$ and ${X_t-X_s\sim{\rm Po}(\lambda(t-s))}$ independently of ${\{X_u\colon u\le s\}}$ for all ${s\le t}$ .

An immediate consequence of this definition is that, if X and Y are independent Poisson processes of rates ${\lambda}$ and ${\mu}$ respectively, then their sum ${X+Y}$ is also Poisson with rate ${\lambda+\mu}$ . Continue reading “Poisson Processes” →

Continuous Processes with Independent Increments

A stochastic process X is said to have independent increments if ${X_t-X_s}$ is independent of ${\{X_u\}_{u\le s}}$ for all ${s\le t}$ . For example, standard Brownian motion is a continuous process with independent increments. Brownian motion also has stationary increments, meaning that the distribution of ${X_{t+s}-X_t}$ does not depend on t. In fact, as I will show in this post, up to a scaling factor and linear drift term, Brownian motion is the only such process. That is, any continuous real-valued process X with stationary independent increments can be written as

$\displaystyle X_t = X_0 + b t + \sigma B_t$

(1)

for a Brownian motion B and constants ${b,\sigma}$ . This is not so surprising in light of the central limit theorem. The increment of a process across an interval [s,t] can be viewed as the sum of its increments over a large number of small time intervals partitioning [s,t]. If these terms are independent with relatively small variance, then the central limit theorem does suggest that their sum should be normally distributed. Together with the previous posts on Lévy’s characterization and stochastic time changes, this provides yet more justification for the ubiquitous position of Brownian motion in the theory of continuous-time processes. Consider, for example, stochastic differential equations such as the Langevin equation. The natural requirements for the stochastic driving term in such equations is that they be continuous with stationary independent increments and, therefore, can be written in terms of Brownian motion.

The definition of standard Brownian motion extends naturally to multidimensional processes and general covariance matrices. A standard d-dimensional Brownian motion ${B=(B^1,\ldots,B^d)}$ is a continuous process with stationary independent increments such that ${B_t}$ has the ${N(0,tI)}$ distribution for all ${t\ge 0}$ . That is, ${B_t}$ is joint normal with zero mean and covariance matrix tI. From this definition, ${B_t-B_s}$ has the ${N(0,(t-s)I)}$ distribution independently of ${\{B_u\colon u\le s\}}$ for all ${s\le t}$ . This definition can be further generalized. Given any ${b\in{\mathbb R}^d}$ and positive semidefinite ${\Sigma\in{\mathbb R}^{d^2}}$ , we can consider a d-dimensional process X with continuous paths and stationary independent increments such that ${X_t}$ has the ${N(tb,t\Sigma)}$ distribution for all ${t\ge 0}$ . Here, ${b}$ is the drift of the process and ${\Sigma}$ is the `instantaneous covariance matrix’. Such processes are sometimes referred to as ${(b,\Sigma)}$ -Brownian motions, and all continuous d-dimensional processes starting from zero and with stationary independent increments are of this form.

Theorem 1 Let X be a continuous ${{\mathbb R}^d}$ -valued process with stationary independent increments.

Then, there exist unique ${b\in{\mathbb R}^d}$ and ${\Sigma\in{\mathbb R}^{d^2}}$ such that ${X_t-X_0}$ is a ${(b,\Sigma)}$ -Brownian motion.

Continue reading “Continuous Processes with Independent Increments” →

The Martingale Representation Theorem

The martingale representation theorem states that any martingale adapted with respect to a Brownian motion can be expressed as a stochastic integral with respect to the same Brownian motion.

Theorem 1 Let B be a standard Brownian motion defined on a probability space ${(\Omega,\mathcal{F},{\mathbb P})}$ and ${\{\mathcal{F}_t\}_{t\ge 0}}$ be its natural filtration.

Then, every ${\{\mathcal{F}_t\}}$ –local martingale M can be written as

$\displaystyle M = M_0+\int\xi\,dB$

for a predictable, B-integrable, process ${\xi}$ .

As stochastic integration preserves the local martingale property for continuous processes, this result characterizes the space of all local martingales starting from 0 defined with respect to the filtration generated by a Brownian motion as being precisely the set of stochastic integrals with respect to that Brownian motion. Equivalently, Brownian motion has the predictable representation property. This result is often used in mathematical finance as the statement that the Black-Scholes model is complete. That is, any contingent claim can be exactly replicated by trading in the underlying stock. This does involve some rather large and somewhat unrealistic assumptions on the behaviour of financial markets and ability to trade continuously without incurring additional costs. However, in this post, I will be concerned only with the mathematical statement and proof of the representation theorem.

In more generality, the martingale representation theorem can be stated for a d-dimensional Brownian motion as follows.

Theorem 2 Let ${B=(B^1,\ldots,B^d)}$ be a d-dimensional Brownian motion defined on the filtered probability space ${(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t\ge 0},{\mathbb P})}$ , and suppose that ${\{\mathcal{F}_t\}}$ is the natural filtration generated by B and ${\mathcal{F}_0}$ .

$\displaystyle \mathcal{F}_t=\sigma\left(\{B_s\colon s\le t\}\cup\mathcal{F}_0\right)$

Then, every ${\{\mathcal{F}_t\}}$ -local martingale M can be expressed as

$\displaystyle M=M_0+\sum_{i=1}^d\int\xi^i\,dB^i$ (1)

for predictable processes ${\xi^i}$ satisfying ${\int_0^t(\xi^i_s)^2\,ds<\infty}$ , almost surely, for each ${t\ge0}$ .

Continue reading “The Martingale Representation Theorem” →

SDEs Under Changes of Time and Measure

The previous two posts described the behaviour of standard Brownian motion under stochastic changes of time and equivalent changes of measure. I now demonstrate some applications of these ideas to the study of stochastic differential equations (SDEs). Surprisingly strong results can be obtained and, in many cases, it is possible to prove existence and uniqueness of solutions to SDEs without imposing any continuity constraints on the coefficients. This is in contrast to most standard existence and uniqueness results for both ordinary and stochastic differential equations, where conditions such as Lipschitz continuity is required. For example, consider the following SDE for measurable coefficients ${a,b\colon{\mathbb R}\rightarrow{\mathbb R}}$ and a Brownian motion B

$\displaystyle dX_t=a(X_t)\,dB_t+b(X_t)\,dt.$

(1)

If a is nonzero, ${a^{-2}}$ is locally integrable and b/a is bounded then we can show that this has weak solutions satisfying uniqueness in law for any specified initial distribution of X. The idea is to start with X being a standard Brownian motion and apply a change of time to obtain a solution to (1) in the case where the drift term b is zero. Then, a Girsanov transformation can be used to change to a measure under which X satisfies the SDE for nonzero drift b. As these steps are invertible, every solution can be obtained from a Brownian motion in this way, which uniquely determines the distribution of X.

A standard example demonstrating the concept of weak solutions and uniqueness in law is provided by Tanaka’s SDE

$\displaystyle dX_t={\rm sgn}(X_t)\,dB_t$

(2)

Continue reading “SDEs Under Changes of Time and Measure” →

Girsanov Transformations

Girsanov transformations describe how Brownian motion and, more generally, local martingales behave under changes of the underlying probability measure. Let us start with a much simpler identity applying to normal random variables. Suppose that X and ${Y=(Y^1,\ldots,Y^n)}$ are jointly normal random variables defined on a probability space ${(\Omega,\mathcal{F},{\mathbb P})}$ . Then ${U\equiv\exp(X-\frac{1}{2}{\rm Var}(X)-{\mathbb E}[X])}$ is a positive random variable with expectation 1, and a new measure ${{\mathbb Q}=U\cdot{\mathbb P}}$ can be defined by ${{\mathbb Q}(A)={\mathbb E}[1_AU]}$ for all sets ${A\in\mathcal{F}}$ . Writing ${{\mathbb E}_{\mathbb Q}}$ for expectation under the new measure, then ${{\mathbb E}_{\mathbb Q}[Z]={\mathbb E}[UZ]}$ for all bounded random variables Z. The expectation of a bounded measurable function ${f\colon{\mathbb R}^n\rightarrow{\mathbb R}}$ of Y under the new measure is

$\displaystyle {\mathbb E}_{\mathbb Q}\left[f(Y)\right]={\mathbb E}\left[f\left(Y+{\rm Cov}(X,Y)\right)\right],$

(1)

where ${{\rm Cov}(X,Y)}$ is the covariance. This is a vector whose i’th component is the covariance ${{\rm Cov}(X,Y^i)}$ . So, Y has the same distribution under ${{\mathbb Q}}$ as ${Y+{\rm Cov}(X,Y)}$ has under ${{\mathbb P}}$ . That is, when changing to the new measure, Y remains jointly normal with the same covariance matrix, but its mean increases by ${{\rm Cov}(X,Y)}$ . Equation (1) follows from a straightforward calculation of the characteristic function of Y with respect to both ${{\mathbb P}}$ and ${{\mathbb Q}}$ .

Now consider a standard Brownian motion B and fix a time ${T>0}$ and a constant ${\mu}$ . Then, for all times ${t\ge 0}$ , the covariance of ${B_t}$ and ${B_T}$ is ${{\rm Cov}(B_t,B_T)=t\wedge T}$ . Applying (1) to the measure ${{\mathbb Q}=\exp(\mu B_T-\mu^2T/2)\cdot{\mathbb P}}$ shows that

$\displaystyle B_t=\tilde B_t + \mu (t\wedge T)$

where ${\tilde B}$ is a standard Brownian motion under ${{\mathbb Q}}$ . Under the new measure, B has gained a constant drift of ${\mu}$ over the interval ${[0,T]}$ . Such transformations are widely applied in finance. For example, in the Black-Scholes model of option pricing it is common to work under a risk-neutral measure, which transforms the drift of a financial asset to be the risk-free rate of return. Girsanov transformations extend this idea to much more general changes of measure, and to arbitrary local martingales. However, as shown below, the strongest results are obtained for Brownian motion which, under a change of measure, just gains a stochastic drift term. Continue reading “Girsanov Transformations” →

	Anonymous on Spitzer’s Formula
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	Anonymous on Brownian Bridges
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	Anonymous on Spitzer’s Formula
	Anonymous on Spitzer’s Formula
	Anonymous on Spitzer’s Formula
	Anonymous on Spitzer’s Formula
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	Anonymous on Spitzer’s Formula