"Saddlepoint Approximations for Continuous-Time Markov Processes"
©
Journal of Econometrics,
October
2006
Volume: 134
|
Issue: 2
|
Pages: 507-51
Publication type: Journal article
Research Archive Topic: Business Economics and Public Policy, Capital Markets and Investments, Corporate Finance
Abstract
This paper proposes saddlepoint expansions as a means to generate closed-form approximations to the transition densities and cumulative distribution functions of Markov processes. This method is applicable to a large class of models considered in finance, for which a Laplace or characteristic functions, but not the transition density, can be found in closed form. But even when such a computation is not possible explicitly, we go one step further by showing how useful approximations can be obtained by replacing the Laplace or characteristic functions by an expansion in small time.
Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.
Each topic is linked to an index of publications on that topic.