Abstract

http://ssrn.com/abstract=1422410
 
 

Citations



 


 



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


Michael S. Johannes


Columbia Business School - Finance and Economics

Nick Polson


University of Chicago - Booth School of Business

Jonathan R. Stroud


University of Pennsylvania - Statistics Department

July 2009

The Review of Financial Studies, Vol. 22, Issue 7, pp. 2559-2599, 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

Accepted Paper Series





Not Available For Download

Date posted: June 22, 2009  

Suggested Citation

Johannes, Michael S. 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: http://ssrn.com/abstract=1422410 or http://dx.doi.org/hhn110

Contact Information

Michael Slater Johannes
Columbia Business School - Finance and Economics ( 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
University of Pennsylvania - Statistics Department ( email )
Wharton School
Philadelphia, PA 19104
United States
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