This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,t...This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.展开更多
We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factor...We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.展开更多
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 ...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.展开更多
基金This work is supported by the National Natural Science Foundation of China(71790594,71701150,and U1811462).
文摘This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.
基金supported by grants from the National Natural Science Foundation of China(72171088,71803049,72003205)the Ministry of Education of the People's Republic of China of Humanities and Social Sciences Youth Fundation(20YJC790142)the General Project of Social Science Planning in Guangdong Province,China(GD22CYJ12).
文摘We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.
基金This work is partially supported by the grants from the Key Programs of the National Natural Science Foundation of China(NSFC No.71631005)the National Natural Science Foundation of China(NSFC No.71471161)the Key Programs of the National Social Science Foundation of China(No.17ZDA074).
文摘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.
