Martingale Representation Research Articles

A martingale representation theorem and valuation of defaultable securities

AbstractWe consider a financial framework with two levels of information: the public information generated by the financial assets, and a larger flow of information that contains additional knowledge about a random time. This random time can represent many economic and financial settings, such as the default time of a firm for credit risk, and the death time of an insured for life insurance. As the random time cannot be seen before its occurrence, the progressive enlargement of filtration seems tailor‐fit to model the larger flow of information that incorporates both the public flow and the information about the random time. In this context, our interest focuses on the following challenges: (a) How to single out the various risks coming from the financial assets, the random time, and their correlations? (b) How these risks interplay and lead to the formation of any risk in the larger flow of information? It is clear that understanding how risks build‐up and interact, when one enlarges the flow of information, is vital for an efficient risk management and derivatives' evaluation in those informational markets. Our answers to these challenges are full and complete no matter what the model for the random time is and no matter how the random time is related to the public flow. In fact, we introduce “pure default” risks, and quantify and classify these risks afterward. Then we elaborate our martingale representation results, which state that any martingale in the large filtration stopped at the random time can be decomposed into orthogonal local martingales (i.e., local martingales whose product remains a local martingale). This constitutes our first principal contribution, while our second contribution consists in evaluating various defaultable securities according to the recovery policy, within our financial setting that encompasses any default model, using a martingale “basis.” Our pricing formulas explain the impact of various recovery policies on securities and determine the types of pure default risk they entail.

Simultaneous Impulse and Continuous Control of a Markov Chain in Continuous Time

We consider continuous and impulse control of a Markov chain (MC) with a finite set of states in continuous time. Continuous control determines the intensity of transitions between MC states, while transition times and their directions are random. Nevertheless, sometimes it is necessary to ensure a transition that leads to an instantaneous change in the state of the MC. Since such transitions require different influences and can produce different effects on the state of the MC, such controls can be interpreted as impulse controls. In this work, we use the martingale representation of a controllable MC and give an optimality condition, which, using the principle of dynamic programming, is reduced to a form of quasi-variational inequality. The solution to this inequality can be obtained in the form of a dynamic programming equation, which for an MC with a finite set of states reduces to a system of ordinary differential equations with one switching line. We prove a sufficient optimality condition and give examples of problems with deterministic and random impulse action.

Modeling Options Prices Considering Mandelbrotian Movement of Prices

Classical Black Scholes model, however still widely used in the financial circles, is known for its inability to generate volatility satisfying the market observations. Besides the assumption of the normal distribution of the underlying assets and perfect liquidity of the market, classical Black-Scholes provides poor modeling of the in-the-money call option price, and does not account for the term structure of the options. Current work concentrates on the fractional integration of a diffusion process, relaxes classical assumptions, and provides theoretically solid modeling of volatility smile and the term structure of options. Inspired by the work of Comte, Coutin, and Renault(2003), we present a modification of the classical Heston model, where the volatility process is defined through a fractional integration of a diffusion process. Our construction allows us to compute a martingale representation for the volatility process efficiently. From this representation, and using calibrated parameters, we provide a numerical result of the model. Results give evidence that the fractional version of the Heston volatility model has better accuracy than the classical Black-Scholes model in terms of prediction error. Among other advantages, the fractional Heston model is a robust heuristical approach to the model parameters, which allows us to interpret them to understand the market behavior.

Representation of martingales under signed measures and the study of the classes ∑ s and ∑ s ′

ABSTRACT Let Q be a bounded, non-null signed measure on a filtered probability space such that and P are equivalent. In this paper, we establish the representation of a Q-local martingale as a time changed Brownian motion, as well as a decomposition of it. We also generalize and characterize processes of the classes and .

An averaging principle for two-time-scale stochastic functional differential equations

Delays are ubiquitous, pervasive, and entrenched in everyday life, thus taking it into consideration is necessary. Dupire recently developed a functional Itô formula, which has changed the landscape of the study of stochastic functional differential equations and encouraged a reconsideration of many problems and applications. Based on the new development, this work examines functional diffusions with two-time scales in which the slow-varying process includes path-dependent functionals and the fast-varying process is a rapidly-changing diffusion. The gene expression of biochemical reactions occurring in living cells in the introduction of this paper is such a motivating example. This paper establishes mixed functional Itô formulas and the corresponding martingale representation. Then it develops an averaging principle using weak convergence methods. By treating the fast-varying process as a random “noise”, under appropriate conditions, it is shown that the slow-varying process converges weakly to a stochastic functional differential equation whose coefficients are averages of that of the original slow-varying process with respect to the invariant measure of the fast-varying process.

Second order backward SDE with random terminal time

Backward stochastic differential equations extend the martingale representation theorem to the nonlinear setting. This can be seen as path-dependent counterpart of the extension from the heat equation to fully nonlinear parabolic equations in the Markov setting. This paper extends such a nonlinear representation to the context where the random variable of interest is measurable with respect to the information at a finite stopping time. We provide a complete wellposedness theory which covers the semilinear case (backward SDE), the semilinear case with obstacle (reflected backward SDE), and the fully nonlinear case (second order backward SDE).

Production Planning with Risk Hedging under a CVaR Objective

A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff), and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including, for instance, a tradable market index as a proxy for the general economy) via a demand rate function. This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information along with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier, and quantify the improvement over the production-only approach. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the hedging budget and the risk aversion level, along with other qualitative insights.

Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework.

Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator for finite-population inference using a superpopulation model framework. We also clarify conditions for its robustness. For variance estimation, the conventional bootstrap inference is invalid for matching estimators with a fixed number of matches due to the nonsmoothness nature of the matching estimator. We propose a new replication variance estimator, which is asymptotically valid. The key strategy is to construct replicates directly based on the linear terms of the martingale representation for the matching estimator, instead of individual records of variables. Simulation studies confirm that the proposed method provides valid inference.

AN ARITHMETIC PURE-JUMP MULTI-CURVE INTEREST RATE MODEL

We propose a multi-curve model involving interest rates and spreads which are modeled by arithmetic martingale processes being larger than some arbitrarily chosen constant. Under our mean-reverting pure-jump approach, we derive tractable martingale representations for the OIS rate, the spread, as well as the LIBOR rate, and provide analytical caplet price formulae. In a second part, we introduce an extended jump-diffusion version of our model and investigate hedging and the computation of Greeks under this new specification. As a by-product, we infer the related arithmetic pure-jump single-curve model. We finally consider the modeling of future information in multi-curve interest rate markets by enlarged filtrations and deduce the related OIS and LIBOR rate representations as well as the corresponding information premium.

Stability results for martingale representations: The general case

In this paper, we obtain stability results for martingale representations in a very general framework. More specifically, we consider a sequence of martingales, each adapted to its own filtration, and a sequence of random variables measurable with respect to those filtrations. We assume that the terminal values of the martingales and the associated filtrations converge in the extended sense, and that the limiting martingale is quasi left continuous and admits the predictable representation property. Then we prove that each component in the martingale representation of the sequence converges to the corresponding component of the martingale representation of the limiting random variable relative to the limiting filtration, under the Skorokhod topology. This extends in several directions earlier contributions in the literature and has applications to stability results for backward stochastic differential equations with jumps, and to discretization schemes for stochastic systems.

Optimal Incentive Contract for Sales Team with Loss Aversion Preference

When manufacturing enterprises employ sales team (or multiple salesmen) to sell products, there is asymmetric information such as the ability and efforts salesmen. Enterprises can use contracts to incentivize salesmen to work hard to maximize their profits. Assuming that market demand is sensitive to effort, and the salesman can exploit the market by increasing effort, a multi-agent model is established for the case of symmetrical information and asymmetrical information, in which the sales team has a loss aversion preference. In this multi-agent model, the agents’ utility function is non-concave and cannot be solved by traditional methods. We use a backward stochastic differential equation (BSDE) to represent agents’ contract through the martingale representation theorem and use the stochastic optimal control and matrix method to obtain the explicit solution of the optimal contract. Based on the conclusions of the research, an empirical analysis is made on the sales team of an enterprise.

Minimal variance hedging in multicurve interest rate modeling

We consider minimal variance hedging in a pure-jump multicurve interest rate model. In the first part, we derive arithmetic multifactor martingale representations for the spread, OIS, and LIBOR rate, which are bounded from below by a real-valued constant. In the second part, we investigate minimal variance hedging and provide a closed-form formula for the related minimal variance portfolio. We apply this result to several examples covering both replicable and nonreplicable claims. We conclude the paper with a consideration of delta hedging.

Density of the set of probability measures with the martingale representation property

Let $\psi$ be a multidimensional random variable. We show that the set of probability measures $\mathbb{Q}$ such that the $\mathbb{Q}$-martingale $S^{\mathbb{Q}}_{t}=\mathbb{E}^{\mathbb{Q}}[\psi \lvert\mathcal{F}_{t}]$ has the Martingale Representation Property (MRP) is either empty or dense in $\mathcal{L}_{\infty}$-norm. The proof is based on a related result involving analytic fields of terminal conditions $(\psi(x))_{x\in U}$ and probability measures $(\mathbb{Q}(x))_{x\in U}$ over an open set $U$. Namely, we show that the set of points $x\in U$ such that $S_{t}(x)=\mathbb{E}^{\mathbb{Q}(x)}[\psi(x)\lvert\mathcal{F}_{t}]$ does not have the MRP, either coincides with $U$ or has Lebesgue measure zero. Our study is motivated by the problem of endogenous completeness in financial economics.

Distance between the fractional Brownian motion and the space of adapted Gaussian martingales

We consider the distance between the fractional Brownian motion defined on the interval [0,1] and the space of Gaussian martingales adapted to the same filtration. As the distance between stochastic processes, we take the maximum over [0,1] of mean-square deviances between the values of the processes. The aim is to calculate the function a in the Gaussian martingale representation ∫0ta(s)dWs that minimizes this distance. So, we have the minimax problem that is solved by the methods of convex analysis. Since the minimizing function a can not be either presented analytically or calculated explicitly, we perform discretization of the problem and evaluate the discretized version of the function a numerically.

An efficient conditional Monte Carlo method for European option pricing with stochastic volatility and stochastic interest rate

ABSTRACTThis paper studies the variance reduction methods for pricing European options under stochastic volatility and stochastic interest rate model. A general conditional Monte Carlo pricing framework is constructed to reduce the variance and save the time cost of Monte Carlo simulation. Based on Martingale Representation Theorem, two efficient martingale control variates are designed to combine with the conditional Monte Carlo simulation. Numerical results show that this hybrid method has great variance reduction effect and robust performance. The idea is also applicable for pricing other financial derivatives with stochastic volatility and/or stochastic interest rate.

Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities

Pricing multi-asset options has always been one of the key problems in financial engineering because of their high dimensionality and the low convergence rates of pricing algorithms. This paper studies a method to accelerate Monte Carlo (MC) simulations for pricing multi-asset options with stochastic volatilities. First, a conditional Monte Carlo (CMC) pricing formula is constructed to reduce the dimension and variance of the MC simulation. Then, an efficient martingale control variate (CV), based on the martingale representation theorem, is designed by selecting volatility parameters in the approximated option price for further variance reduction. Numerical tests illustrated the sensitivity of the CMC method to correlation coefficients and the effectiveness and robustness of our martingale CV method. The idea in this paper is also applicable for the valuation of other derivatives with stochastic volatility.

Martingale representation theorem for G-Brownian motion

This paper firstly discuss some related properties for a type of G-function by introducing a G-Brownian motion martingale. And a martingale representation theorem is then proved by using Multiple G-Itô integral appeared in [1].

Martingale spaces and representations under absolutely continuous changes of probability

In a fully general setting, we study the relation between martingale spaces under two locally absolutely continuous probabilities and prove that the martingale representation property (MRP) is always stable under locally absolutely continuous changes of probability. Our approach relies on minimal requirements, is constructive and, as shown by a simple example, enables us to study situations which cannot be covered by the existing theory.

Locally Risk-Minimizing Hedging of Counterparty Risk for Portfolio of Credit Derivatives

We discuss dynamic hedging of counterparty risk for a portfolio of credit derivatives by the local risk-minimization approach. We study the problem from the perspective of an investor who, trading with credit default swaps (CDS) referencing the counterparty, wants to protect herself/himself against the loss incurred at the default of the counterparty. We propose a credit risk intensity-based model consisting of interacting default intensities by taking into account direct contagion effects. The portfolio of defaultable claims is of generic type, including CDS portfolios, risky bond portfolios and first-to-default claims with payments allowed to depend on the default state of the reference firms and counterparty. Using the martingale representation of the conditional expectation of the counterparty risk price payment stream under the minimal martingale measure, we recover a closed-form representation for the locally risk minimizing strategy in terms of classical solutions to nonlinear recursive systems of Cauchy problems. We also discuss applications of our framework to the most prominent classes of credit derivatives.

An extension of the Clark–Haussmann formula and applications

ABSTRACTThis work considers a financial market stochastic model where the uncertainty is driven by a multidimensional Brownian motion. The market price of the risk process makes the transition between real world probability measure and risk neutral probability measure. Traditionally, the martingale representation formulas under the risk neutral probability measure require the market price of risk process to be bounded. However, in several financial models the boundedness assumption of the market price of risk fails; for example a financial market model with the market price of risk following an Ornstein–Uhlenbeck process. This work extends the Clark–Haussmann representation formula to underlying stochastic processes which fail to satisfy the standard requirements. Our methodology is classical, and it uses a sequence of mollifiers. Our result can be applied to hedging and optimal investment in financial markets with unbounded market price of risk. In particular, the mean variance optimization problem can be addressed within our framework.