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Density Forecast Evaluations via a Simulation-Based Dynamic Probability Integral Transformation

Title
Density Forecast Evaluations via a Simulation-Based Dynamic Probability Integral Transformation
Authors
Yun, Jaeho
Ewha Authors
윤재호
SCOPUS Author ID
윤재호scopus
Issue Date
2020
Journal Title
JOURNAL OF FINANCIAL ECONOMETRICS
ISSN
1479-8409JCR Link

1479-8417JCR Link
Citation
JOURNAL OF FINANCIAL ECONOMETRICS vol. 18, no. 1, pp. 24 - 58
Keywords
affine jump diffusion modelsdensity forecastsparticle filterssimulation-based dynamic probability integral transform
Publisher
OXFORD UNIV PRESS
Indexed
SSCI; SCOPUS WOS
Document Type
Article
Abstract
This paper presents simulation-based density forecast evaluation methods using particle filters. The simulation-based dynamic probability integral transformation or log-likelihood evaluation method is combined with the existing density forecast evaluation methods. This methodology is applicable to various density forecast models, such as log stochastic volatility models and affine jump diffusion (AJD) models, for which the probability integral transform or likelihood computation is difficult due to the presence of latent stochastic volatilities. This methodology is applied to the daily S&P 500 stock index. The empirical results show that the AJD models with jumps perform the best for out-of-sample evaluations.
DOI
10.1093/jjfinec/nby030
Appears in Collections:
사회과학대학 > 경제학전공 > Journal papers
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