Estimation of Volatility on the Small Sample with Generalized Maximum Entropy

© 2018, Springer International Publishing AG, part of Springer Nature. Generalized autoregressive conditional heteroscedasticity (GARCH) provides useful techniques for modeling the dynamic volatility model. Several estimation techniques have been developed over the years, for examples Maximum likeli...

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Main Authors: Quanrui Song, Songsak Sriboonchitta, Somsak Chanaim, Chongkolnee Rungruang
Format: Book Series
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/58577
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-585772018-09-05T04:33:29Z Estimation of Volatility on the Small Sample with Generalized Maximum Entropy Quanrui Song Songsak Sriboonchitta Somsak Chanaim Chongkolnee Rungruang Computer Science Mathematics © 2018, Springer International Publishing AG, part of Springer Nature. Generalized autoregressive conditional heteroscedasticity (GARCH) provides useful techniques for modeling the dynamic volatility model. Several estimation techniques have been developed over the years, for examples Maximum likelihood, Bayesian, and Entropy. Among these, entropy can be considered an efficient tool for estimating GARCH model since it does not require any distribution assumptions which must be given in Maximum likelihood and Bayesian estimators. Moreover, we address the problem of estimating GARCH model characterized by ill-posed features. We introduce a GARCH framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform a Monte Carlo experiment and we analyze a real case study. The results show that entropy estimator is successful in estimating the parameters in GARCH model and the estimated parameters are close to the true values. 2018-09-05T04:26:25Z 2018-09-05T04:26:25Z 2018-01-01 Book Series 16113349 03029743 2-s2.0-85043990881 10.1007/978-3-319-75429-1_27 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043990881&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/58577
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Mathematics
spellingShingle Computer Science
Mathematics
Quanrui Song
Songsak Sriboonchitta
Somsak Chanaim
Chongkolnee Rungruang
Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
description © 2018, Springer International Publishing AG, part of Springer Nature. Generalized autoregressive conditional heteroscedasticity (GARCH) provides useful techniques for modeling the dynamic volatility model. Several estimation techniques have been developed over the years, for examples Maximum likelihood, Bayesian, and Entropy. Among these, entropy can be considered an efficient tool for estimating GARCH model since it does not require any distribution assumptions which must be given in Maximum likelihood and Bayesian estimators. Moreover, we address the problem of estimating GARCH model characterized by ill-posed features. We introduce a GARCH framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform a Monte Carlo experiment and we analyze a real case study. The results show that entropy estimator is successful in estimating the parameters in GARCH model and the estimated parameters are close to the true values.
format Book Series
author Quanrui Song
Songsak Sriboonchitta
Somsak Chanaim
Chongkolnee Rungruang
author_facet Quanrui Song
Songsak Sriboonchitta
Somsak Chanaim
Chongkolnee Rungruang
author_sort Quanrui Song
title Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
title_short Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
title_full Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
title_fullStr Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
title_full_unstemmed Estimation of Volatility on the Small Sample with Generalized Maximum Entropy
title_sort estimation of volatility on the small sample with generalized maximum entropy
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043990881&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58577
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