On Valuation With Stochastic Proportional Hazard Models in Finance

Abstract

While the proportional hazard model is recognized to be statistically meaningful for analyzing and estimating financial event risks, the existing literature that analytically deals with the valuation problems is very limited. In this paper, adopting the proportional hazard model in continuous time setting, we provide an analytical treatment for the valuation problems. The derived formulas, which are based on the generalized Edgeworth expansion and give approximate solutions to the valuation problems, are widely useful for evaluating a variety of financial products such as corporate bonds, credit derivatives, mortgage-backed securities, saving accounts and time deposits. Furthermore, the formulas are applicable to the proportional hazard model having not only continuous processes (e.g., Gaussian, affine, and quadratic Gaussian processes) but also discontinuous processes (e.g., Lévy and time-changed Lévy processes) as stochastic covariates. Through numerical examples, it is demonstrated that very accurate values can be quickly obtained by the formulas such as a closed-form formula.

Why do firms adopt enterprise risk management (ERM)? Empirical evidence from France

“…Hazard model enables researchers to adjust for time it takes firms to prepare for an event and implement an action. These methods are widely recognized as meaningful for empirical investigations in finance (Thomann et al, 2012;Yamazaki, 2013). Second, we have used a more rigorous approach towards the identification of the firms' using ERM and the actual timing when they started the programme.…”