Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model： A Realized Volatility Approach

Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.

Heterogeneous Traders, the Leverage Effect and Volatility of the Chinese P2P Market

The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market.This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China.Based on the background data of the P2P platform,Honglingchuangtou,we use the factor analysis method to construct a platform volatility(PV)index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market.The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility.Similar to traditional financial markets,the volatility of the P2P market also shows a leverage effect,which means that the negative volatility of trader actions should have a negative impact on market fluctuations.With regard to the leverage effect,the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.

Empirical Analysis of ARCH Family Models on Oil Price Fluctuations

This paper selects the daily data of national oil prices from January 2, 2014 to February 28, 2019, establishes an ARMA (2, 0) model, and tests its residuals for ARCH effects. Finally, the TARCH (1, 1) model is determined to quantitatively analyze the volatility of the crude oil market.

The Effect of Market Quality on the Causality between Returns and Volatilities: Evidence from CSI 300 Index Futures

This paper investigates the impact of market quality on volatility asymmetry of CSI 300 index futures by using short-and long-run causality measures proposed by Dufour et al.(2012).We use a high-frequency-based noise variance estimator as the comprehensive proxy for market quality and find that volatility asymmetry is closely related to market quality.Specifically,in the period of poor market quality,the volatility asymmetry will vanish or even be reversed,which is mainly due to the sharp decline of the leverage effects.Moreover,the volatility feedback effect will be enhanced while the leverage effect will be weakened if the noise variance is taken into consideration in the causal analysis.Finally,we use other market quality indices as auxiliary variables in the robustness analysis and get similar results.

A Study on the Volatility of the Bangladesh Stock Market--Based on GARCH Type Models

The generalized autoregressive conditional heteroskedasticity(GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant "low-yield associated with high-risk" phenomenon is detected in the crisis period and the "leverage effect" occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity(TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

Research on the influence of equity pledge on stock price crash risk:based on financial shock of 2015 stock market crisis

The 2015 Chinese stock market crisis triggered liquidation because of equity pledge so that the leverage effect of the small probability event with severe results got intensive attention from investors.It is found that the effects of equity pledge on stock price crash risk reversed significantly before and after the 2015 stock market crisis.In the mechanism analysis,we further find that the equity pledge influenced the stock price crash risk by longer suspension and greater price fluctuation.The shareholding ratio of institutional investors and information environment also had a significant moderating effect on the influence of equity pledge on stock price crash risk.Alternative interpretation tests excluded the tunnel effect and pressure effect by shareholders and incentive effect by management.This study by analysing empirical data provides evidence on the change of investors’risk recognition,which is caused by financial shock,in the Chinese capital market.

Shape-constrained semiparametric additive stochastic volatility models

Nonparametric stochastic volatility models,although providing great flexibility for modelling thevolatility equation,often fail to account for useful shape information.For example,a model maynot use the knowledge that the autoregressive component of the volatility equation is monotonically increasing as the lagged volatility increases.We propose a class of additive stochasticvolatility models that allow for different shape constraints and can incorporate the leverageeffect–asymmetric impact of positive and negative return shocks on volatilities.We developa Bayesian fitting algorithm and demonstrate model performance on simulated and empiricaldatasets.Unlike general nonparametric models,our model sacrifices little when the true volatility equation is linear.In nonlinear situations we improve the model fit and the ability to estimatevolatilities over general,unconstrained,nonparametric models.