Time-Varying Long Memory Property in the Cryptocurrency Markets

2021 ◽  
Vol 23 (1) ◽  
pp. 75-85
Author(s):  
Sang Hoon Kang
Keyword(s):  
Long Memory ◽  
Time Varying ◽  
Memory Property

Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets

2009 ◽  
Vol 10 (2) ◽  
pp. 122-139 ◽  
Author(s):  
Adnan Kasman ◽  
Saadet Kasman ◽  
Erdost Torun
Keyword(s):  
Long Memory ◽  
Stock Markets ◽  
Memory Property

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

2019 ◽  
Vol 81 ◽  
pp. 70-78 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Kun Liu ◽  
Xin-Lei Duan ◽  
Ming-Fang Li
Keyword(s):  
Hybrid Model ◽  
Long Memory ◽  
Risk Evaluation ◽  
Oil Price ◽  
Price Risk ◽  
Time Varying ◽  
Model Based ◽  
Oil Price Risk

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

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.

Structural breaks in unbiased volatility estimator: Modeling and forecasting

2017 ◽  
Vol 11 (1) ◽  
pp. 27-50 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
Extreme Value ◽  
Trading Strategy ◽  
Risk Averse ◽  
Memory Property ◽  
Economic Significance ◽  
Forecasting Performance ◽  
Modeling And Forecasting

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

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