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Is Baidu index really powerful to predict the Chinese stock market volatility? : new evidence from the internet information
Lang, Qiaoqi; Wang, Jiqian; Ma, Feng; Huang, Dengshi; … - In: China finance review international 13 (2023) 2, pp. 263-284
Persistent link: https://www.econbiz.de/10014312401
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The Glosten-Jagannathan-Runkle-Generalized Autoregressive Conditional Heteroscedastic approach to investigating the foreign exchange forward premium volatility
Hamzaoui, Nessrine; Regaieg, Boutheina - In: International journal of economics and financial issues … 6 (2016) 4, pp. 1608-1615
Persistent link: https://www.econbiz.de/10011775273
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The stochastic volatility in mean model
Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011303314
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Construction and backtesting of a multi-factor stress-scenario for the stock market
Boldyrev, Kirill; Andrianov, Dmitry; Ivliev, Sergey - In: Financial econometrics and empirical market microstructure, (pp. 37-45). 2015
Persistent link: https://www.econbiz.de/10011326724
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Stock index volatility forecasting with high frequency data
Hol Uspensky, Eugenie; Koopman, Siem Jan - 2002
Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …
Persistent link: https://www.econbiz.de/10011326944
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How useful are the various volatility estimators for improving GARCH-based volatility forecasts? : evidence from the Nasdaq-100 stock index
Wang, Jying-Nan; Hsu, Yuan-Teng; Liu, Hung-Chun - In: International journal of economics and financial issues … 4 (2014) 3, pp. 651-656
Persistent link: https://www.econbiz.de/10010526918
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GARCH models, tail indexes and error distributions : an empirical investigation
Šopov, Boril; Horváth, Roman - 2015
heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 … stock market returns ranging from 1995-2014 and compare these to the tail indexes produced by simulating GARCH models. Our … results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which …
Persistent link: https://www.econbiz.de/10010529886
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Tail-dependence in stock-return pairs
Fortin, Ines; Kuzmics, Christoph - 2002
employed in a bivariate GARCH model, where the joint distribution of the disturbances is split into its marginals and its …-dependent distribution. -- value-at-risk ; copula ; non-normal bivariate GARCH ; asymmetric dependence ; profile likelihood-ratio test …
Persistent link: https://www.econbiz.de/10009725481
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Effects of fiscal policy and monetary policy on the stock market in Poland
Hsing, Yu - In: Economies : open access journal 1 (2013) 3, pp. 19-25
Poland. Applying the GARCH model and based on a sample during 1999.Q2 to 2012.Q4, this paper finds that Poland’s stock market …
Persistent link: https://www.econbiz.de/10010227857
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Forecasting volatility of stock indices with ARCH model
Alam, Md. Zahangir; Siddikee, Md. Noman; Masukujjaman, Md. - In: International journal of financial research 4 (2013) 2, pp. 126-143
Persistent link: https://www.econbiz.de/10010205105