Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices

2009 ◽  
Vol 49 (2) ◽  
pp. 578-595 ◽  
Author(s):  
David G. McMillan ◽  
Isabel Ruiz
2019 ◽  
Vol 81 ◽  
pp. 70-78 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Kun Liu ◽  
Xin-Lei Duan ◽  
Ming-Fang Li

Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.


2007 ◽  
Vol 96 (1-3) ◽  
pp. 99-118 ◽  
Author(s):  
Kristina Bružaitė ◽  
Donatas Surgailis ◽  
Marijus Vaičiulis

2011 ◽  
Vol 32 (4) ◽  
pp. 339-352 ◽  
Author(s):  
Massimiliano Caporin ◽  
Juliusz Preś

2020 ◽  
Vol 5 (1) ◽  
pp. 15-34
Author(s):  
Surya Bahadur Rana

This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no significant risk premium to hedge against risk associated with investment in stocks. The study also demonstrates that asymmetric TGARCH (1, 1) and EGARCH (1, 1) models fail to capture the leverage effects on the volatility. Finally, study results show that GARCH (1, 1) with student’s t error distribution model is the best fitted one to capture the volatility persistence of daily returns on NEPSE index series over the sampled period. The findings from this study offers an additional insight in understanding the volatility pattern of daily stock returns in Nepal for the most recent period that helps investors in forming a sound strategy to address the risk pattern of investing in stock market of Nepal.


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