Estimating Long Memory Volatility Using High-Frequency Data of Asian Stock Markets PDF Download
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Author: Geeta Duppati
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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Book Description
This article analyzed the presence of long memory in volatility in 5 Asian equity indices namely SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using 5 minutes intraday return series ranging from 05-jan-2015 to 06-Aug-2015. The study employed ARFIMA-FIGARCH model and ARFIMA-APARCH model and compared them with GARCH (1,1) model and APARACH(1,1) in terms of in-sample forecast accuracy. The results confirmed the presence of long memory in both the return and volatility series for all the five markets under study. Among the group, CNIA and STI showed most persistence in both the return and conditional volatility. In terms of forecast measures, the long-memory GARCH models were found to be performing better compared to the short-memory GARCH models.
Author: Geeta Duppati
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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Book Description
This article analyzed the presence of long memory in volatility in 5 Asian equity indices namely SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using 5 minutes intraday return series ranging from 05-jan-2015 to 06-Aug-2015. The study employed ARFIMA-FIGARCH model and ARFIMA-APARCH model and compared them with GARCH (1,1) model and APARACH(1,1) in terms of in-sample forecast accuracy. The results confirmed the presence of long memory in both the return and volatility series for all the five markets under study. Among the group, CNIA and STI showed most persistence in both the return and conditional volatility. In terms of forecast measures, the long-memory GARCH models were found to be performing better compared to the short-memory GARCH models.
Author: Guglielmo Maria Caporale
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566
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Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Author: Roel C. A. Oomen
Publisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 48
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Book Description
Author: Kashif Saleem
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
Using high frequency data, this paper examines the long memory property in the conditional volatility of the precious metals return series at different time frequencies using FIGARCH models. Very significant long memory characteristics have been detected in absolute returns by using Semiparametric local Whittle estimation of the long memory parameter. Estimation of the long memory parameter across many different data sampling frequencies gives consistent estimates of the long memory parameter, indicating that the series are exactly to show some degree of self-similarity. Results indicate that the long memory property remains quite consistent across different time frequencies for both unconditional and conditional volatility measures. This study is useful for investors and traders (with different trading horizons) and it can be used in predicting expected future volatility and in designing and implementing trading strategies at different time frequencies.
Author: David Lee Kuo Chuen
Publisher: Academic Press
ISBN: 0128010630
Category : Business & Economics
Languages : en
Pages : 531
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Book Description
Participants in Asian financial markets have witnessed the unprecedented growth and sophistication of their investments since the 1997 crisis. Handbook of Asian Finance: REITs, Trading, and Fund Performance analyzes the forces behind these growth rates. Insights into banking, fund performance, and the effects of trading technologies for practitioners to tax evasion, market manipulation, and corporate governance issues are all here, presented by expert scholars. Offering broader and deeper coverage than other handbooks, the Handbook of Asian Finance: REITs, Trading, and Fund Performance explains what is going on in Asia today. Presents the only micro- and market-related analysis of pan-Asian finance available today Explores the implications implicit in the expansion of sovereign funds and the growth of the hedge fund and real estate fund management industries Investigates the innovations in technology that have ushered in faster capital flow and larger trading volumes
Author: Jonathan H. Wright
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 42
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Book Description
Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that the choice of volatility measure makes little difference to the log-periodogram regression estimator if the data is Gaussian conditional on the volatility process. But, if the data is conditionally leptokurtic, the log periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In U.S. stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.
Author: Guangren Xi
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Book Description
Author: Michael P. Clements
Publisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732
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Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author: Yuthana Sethapramote
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
This paper examines the accuracy of Value-at-Risk (VaR) estimation in the Stock Exchange of Thailand. We apply standard conditional volatility models (GARCH) and the GARCH model with long memory process (FIGARCH) in calculation of VaR. The empirical results from R|S statistics show that there is significant evidence of long memory process in volatility but not in mean of SET50 index returns. Comparing accuracy of VaR estimation, the results from the Kupiec-LR test show that 1-day ahead 1% VaR values calculated using FIGARCH(1,d,1) model with normal innovations are more accurate than those generated using short memory GARCH(1,1) models. Considering the Bank of International Settlement (BIS)'s regulatory back-testing, the results also confirm that the long memory models provide better performance than those of the standard GARCH models. In summary, our empirical results indicate that long-range memory could provide better performance in risk management than that of standard GARCH in the case of Stock Exchange of Thailand. However, our results from FIGARCH still do not outperform those of the asymmetric GARCH.
