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Dynamics of global stock market correlations : the VIX and attention allocation
Ceylan, Özcan - In: Journal of applied economics 24 (2021) 1, pp. 392-400
This paper investigates the dynamics of international stock return correlations between the U.S., the U.K., Germany and France. Estimated correlations are modeled in an ARDL framework to evaluate how the market-wide uncertainty in the U.S. affects international stock market comovements. Results...
Persistent link: https://www.econbiz.de/10013174300
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Modelling the directional spillovers from DJIM Index to conventional benchmarks : different this time?
Ahmad, Wasim; Rais, Shirin; Shaik, Abdul Rahman - In: The quarterly review of economics and finance : journal … 67 (2018), pp. 14-27
Persistent link: https://www.econbiz.de/10012034392
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Impact of the monetary policy instruments on Islamic stock market index return
Albaity, Mohamed Shikh - 2011
variables, interest rates, and inflation rate on two Islamic stock market indices. Using time series analysis such as GARCH the …
Persistent link: https://www.econbiz.de/10010305856
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Optimal Prediction Pools
Geweke, John; Amisano, Gianni - 2009
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10011605063
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Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M model
Christensen, Bent Jesper; Nielsen, Morten Ørregaard; … - 2009
We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory …-in-mean effect is significant, and the FIEGARCH-M model outperforms the original FIEGARCH model and alternative GARCH …
Persistent link: https://www.econbiz.de/10010290338
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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