Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices

Posted: 22 Jun 2009

See all articles by Michael S. Johannes

Michael S. Johannes

Graduate School of Business, Columbia University

Nick Polson

University of Chicago - Booth School of Business

Jonathan R Stroud

McDonough School of Business, Georgetown University

Date Written: July 2009

Abstract

This paper provides an optimal filtering methodology in discretely observed continuous-time jump-diffusion models. Although the filtering problem has received little attention, it is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines time-discretization schemes with Monte Carlo methods. It is quite general, applying in nonlinear and multivariate jump-diffusion models and models with nonanalytic observation equations. We provide a detailed analysis of the filter's performance, and analyze four applications: disentangling jumps from stochastic volatility, forecasting volatility, comparing models via likelihood ratios, and filtering using option prices and returns.

Keywords: C11, C13, C15, C51, C52, G11, G12, G17

Suggested Citation

Johannes, Michael Slater and Polson, Nick and Stroud, Jonathan R., Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices (July 2009). The Review of Financial Studies, Vol. 22, Issue 7, pp. 2559-2599, 2009, Available at SSRN: https://ssrn.com/abstract=1422410 or http://dx.doi.org/hhn110

Michael Slater Johannes

Graduate School of Business, Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

Jonathan R. Stroud

McDonough School of Business, Georgetown University ( email )

3700 O Street NW
Washington, DC 20057
United States

HOME PAGE: http://jonathanrstroud.com

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