scholarly journals VOLATILITY ASYMMETRY OF SCALE INDEXES - TAKING CHINA AS AN EXAMPLE

2020 ◽  
Vol 10 (4) ◽  
pp. 158-169
Author(s):  
Shih-Yung Wei ◽  
Jao-Hong Cheng ◽  
Li-Wei Lin ◽  
Su-Mei Gan
Keyword(s):  
Volatility Asymmetry

The reverse volatility asymmetry in Chinese financial market

2014 ◽  
Vol 24 (24) ◽  
pp. 1555-1575 ◽  
Author(s):  
Die Wan ◽  
Ke Cheng ◽  
Xiaoguang Yang
Keyword(s):  
Financial Market ◽  
Volatility Asymmetry

scholarly journals Regime-Dependent Good and Bad Volatility of Bitcoin

2020 ◽  
Vol 13 (12) ◽  
pp. 312
Author(s):  
Kislay Kumar Jha ◽  
Dirk G. Baur
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Realized Volatility ◽  
Forecast Horizon ◽  
Volatility Asymmetry ◽  
Volatility Regimes ◽  
The Asymmetry

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

scholarly journals Stochastic Volatility in General Equilibrium

2011 ◽  
Vol 01 (04) ◽  
pp. 707-731 ◽  
Author(s):  
George Tauchen
Keyword(s):  
Stock Market ◽  
General Equilibrium ◽  
A Priori ◽  
Market Volatility ◽  
Elasticity Of Substitution ◽  
Stock Market Volatility ◽  
Volatility Risk ◽  
Risk Premiums ◽  
Volatility Asymmetry ◽  
Main Model

The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a two-factor structure for stock market volatility along with time-varying risk premiums on consumption and volatility risk. It also generates endogenously a dynamic leverage effect (volatility asymmetry), the sign of which depends upon the magnitudes of the risk aversion and the intertemporal elasticity of substitution parameters.

scholarly journals Think again: volatility asymmetry and volatility persistence

2018 ◽  
Vol 23 (1) ◽  
Author(s):  
Dirk G. Baur ◽  
Thomas Dimpfl
Keyword(s):  
Quantile Regression ◽  
Realized Volatility ◽  
Leverage Effect ◽  
Variance Estimator ◽  
Volatility Persistence ◽  
Volatility Models ◽  
Volatility Asymmetry ◽  
High Volatility ◽  
Volatility Regimes ◽  
Volatility Feedback

Abstract We use a leveraged quantile heterogeneous autoregressive model of realized volatility to illustrate that volatility persistence and the asymmetric “leverage” effect are high volatility phenomena. More specifically, we find that (i) low volatility is not persistent, but high volatility all the more, even featuring properties of explosive processes; and (ii) asymmetry of volatility is only a high volatility phenomenon and there is no asymmetry in low volatility regimes. Our results turn out to be robust to the choice of the realized variance estimator, in particular with respect to jumps. The analysis illustrates that quantile regression can provide information that is hidden in commonly used GARCH or realized volatility models. The quantile regression results can also be linked to the weak empirical evidence of the leverage effect and the volatility feedback effect.

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