SAMBA: Stochastic Analytical Model with a Bayesian Approach

Model-based Path Integral Stochastic Control: A Bayesian Nonparametric Approach

Over the last few years, sampling-based stochastic optimal control (SOC) frameworks have shown impressive performances in reinforcement learning (RL) with applications in robotics. However, such approaches require a large amount of samples from many interactions with the physical systems. To improve learning efficiency, we present a novel model-based and data-driven SOC framework based on path ...

Valuing Options in Heston's Stochastic Volatility Model: Another Analytical Approach

We are concerned with the valuation of European options in the Heston stochastic volatility model with correlation. Based on Mellin transforms, we present new solutions for the price of European options and hedging parameters. In contrast to Fourier-based approaches, where the transformation variable is usually the log-stock price at maturity, our framework focuses on directly transforming the ...

A Stochastic Model Approach

Quality of service (QoS) optimization for end-to-end (e2e) services always depends on performance analysis in cloud-based service delivery industry. However, performance analysis of e2e services becomes difficult as the scale and complexity of virtualized computing environments increase. In this paper, the authors present a novel hierarchical stochastic approach to evaluate the QoS of e2e virtu...

Bayesian Analysis of a Stochastic Volatility Model

Analytical formulas for local volatility model with stochastic rates

This paper presents new approximation formulae of European options in a local volatility model with stochastic interest rates. This is a companion paper to our work on perturbation methods for local volatility models in Benhamou et al. (2009c) for the case of stochastic interest rates. The originality of this approach is to model the local volatility of the discounted spot and to obtain accurat...