Was there a Portfolio Effect of the Micro-economic Leverage ？ Some Empirical Evidence from the Chinese Stock Market

The study is on a linear model of the relationship between the systematic risk and the micro-economic leverage and analyzed the data from the steel, energy source and chemical fibre industry listed companies in the Chinese stock market in 2002 and 2001. Using the linear regression method, empirical equations were found. The portfolio effect was shown so that some empirical evidence had been found to support the micro-economic leverage portfolio effect theory, which was that the listed companies balanced the operating and financial leverage to minimize the systematic risk.

The Quantitative Analysis on Leverage and its Effects

Leverage of modem enterprise＇s financial management includes operating leverage and financial leverage. Both of them exist objectively, are not changeable with human＇s minds. They enlarge enterprise＇s benefit and risk, so they have both positive and negative effects. The degrees of them are measured as DOL and DFL. In financial management, the relationship between DOL and operating risk has regularity in quantity, and so does the relationship between DFL and financial risk.

Volatility forecasting in Chinese nonferrous metals futures market

This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.

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.

Popular cryptoassets(Bitcoin,Ethereum,and Dogecoin),Gold,and their relationships:volatility and correlation modeling

Cryptoassets have experienced dramatic volatility in their prices,especially during the COVID-19 pandemic era.This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets(Bitcoin,Ethereum,and Dogecoin)as well as Gold.Multiple Generalized Autoregressive Conditional Heteroskedasticity(GARCH)models are analyzed.We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude,perhaps a manifestation of the fear of missing out(FOMO)effect.Our research is one of the first to use COVID-19-period volatility of financial assets(in-sample data)to forecast their later COVID-19-period volatility(out-of-sample data).This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data.Our results indicate that generally,the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data.We also evaluate the correlations among the assets using the Dynamic Conditional Correlation(DCC)framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years.The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased.Unsurprisingly,there is a strong positive correlation among the cryptocurrencies.Pairwise correlation among all four assets was stronger during the COVID-19 pandemic.Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.

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.

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.

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.

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.