General time interval multidimensional BSDEs with generators satisfying a weak stochastic-monotonicity condition

<p style='text-indent:20px;'>This paper establishes an existence and uniqueness result for the adapted solution of a general time interval multidimensional backward stochastic differential equation (BSDE), where the generator <inline-formula> <tex-math id="M1">\begin{document}$ g $\end{document}</tex-math> </inline-formula> satisfies a weak stochastic-monotonicity condition and a general growth condition in the state variable <inline-formula> <tex-math id="M2">\begin{document}$ y $\end{document}</tex-math> </inline-formula>, and a stochastic-Lipschitz condition in the state variable <inline-formula> <tex-math id="M3">\begin{document}$ z $\end{document}</tex-math> </inline-formula>. This unifies and strengthens some known works. In order to prove this result, we develop some ideas and techniques employed in Xiao and Fan [<xref ref-type="bibr" rid="b25">25</xref>] and Liu et al. [<xref ref-type="bibr" rid="b15">15</xref>]. In particular, we put forward and prove a stochastic Gronwall-type inequality and a stochastic Bihari-type inequality, which generalize the classical ones and may be useful in other applications. The martingale representation theorem, Itô’s formula, and the BMO martingale tool are used to prove these two inequalities. </p>

SOME NOTES ABOUT THE MARTINGALE REPRESENTATION THEOREM AND THEIR APPLICATIONS

An important theorem in stochastic finance field is the martingale representation theorem. It is useful in the stage of making hedging strategies (such as cross hedging and replicating hedge) in the presence of different assets with different stochastic dynamics models. In the current paper, some new theoretical results about this theorem including derivation of serial correlation function of a martingale process and its conditional expectations approximation are proposed. Applications in optimal hedge ratio and financial derivative pricing are presented and sensitivity analyses are studied. Throughout theoretical results, simulation-based results are also proposed. Two real data sets are analyzed and concluding remarks are given. Finally, a conclusion section is given.

Decomposing Dynamic Risks into Risk Components

The decomposition of dynamic risks a company faces into components associated with various sources of risk, such as financial risks, aggregate economic risks, or industry-specific risk drivers, is of significant relevance in view of risk management and product design, particularly in (life) insurance. Nevertheless, although several decomposition approaches have been proposed, no systematic analysis is available. This paper closes this gap in literature by introducing properties for meaningful risk decompositions and demonstrating that proposed approaches violate at least one of these properties. As an alternative, we propose a novel martingale representation theorem (MRT) decomposition that relies on martingale representation and show that it satisfies all of the properties. We discuss its calculation and present detailed examples illustrating its applicability. This paper was accepted by Baris Ata, stochastic models and simulation.

The Mathematics of the Martingale Approach

In this chapter we present the two main mathematical results which are needed for the application of the martingale approach to pricing and hedging. We first discuss and prove the martingale representation theorem which says that in a Wiener framework, every martingale can be represented as a stochastic integral. We then discuss and prove the Girsanov Theorem which gives us control over the class of absolutely continuous measure transformations. The abstract theory is then applied to stochastic differential equations, and to maximum likelihood estimation.

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.

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.