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Tytuł pozycji:

Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters.

Tytuł:
Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters.
Autorzy:
Bégin, Jean-François (AUTHOR)
Boudreault, Mathieu (AUTHOR)
Temat:
STANDARD & Poor's 500 Index
SMOOTHNESS of functions
GREAT Recession, 2008-2013
KALMAN filtering
MAXIMUM likelihood statistics
Źródło:
Journal of Computational & Graphical Statistics. Apr-Jun2021, Vol. 30 Issue 2, p452-466. 15p.
Czasopismo naukowe
In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa's method to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering method is precise and much faster than the particle filter, in addition to yielding a smooth function over the parameter space. We then find the maximum likelihood estimates of various models that include stochastic volatility, jumps in the returns and variance, and also stochastic jump arrival intensity with the S&P 500 daily returns. During the Great Recession, the jump arrival intensity increases significantly and contributes to the clustering of volatility and negative returns. [ABSTRACT FROM AUTHOR]
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