This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR mod...This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR models.The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly horizons.The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects.The out-of-sample results were robust across three different methods.More importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.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
This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GA...This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)futures.We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models.Our empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type models.The results also indicate that EPU has noticeable effect on the volatility of EUA futures.Specifically,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index.Robustness checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures prices.Additionally,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.展开更多
A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility.To address this issue,we find a phenomenon,“momentum of jumps”(MoJ),that the predi...A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility.To address this issue,we find a phenomenon,“momentum of jumps”(MoJ),that the predictive ability of the jump component is persistent when forecasting the oil futures market volatility.Specifically,we propose a strategy that allows the predictive model to switch between a benchmark model without jumps and an alternative model with a jump component according to their recent past forecasting performance.The volatility data are based on the intraday prices of West Texas Intermediate.Our results indicate that this simple strategy significantly outperforms the individual models and a series of competing strategies such as forecast combinations and shrinkage methods.A mean–variance investor who targets a constant Sharpe ratio can realize the highest economic gains using the MoJ-based volatility forecasts.Our findings survive a wide variety of robustness tests,including different jump measures,alternative volatility measures,various financial markets,and extensive model specifications.展开更多
This paper studies the performance of the GARCH model and two of its non linear modifications to forecast China′s weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and R...This paper studies the performance of the GARCH model and two of its non linear modifications to forecast China′s weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and Runkle models which have proposed to describe the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations and that the GJR model cannot be recommended for forecasting.展开更多
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 chara...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.展开更多
This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework.In-sample results indicate that oil futures intraday information is helpful to in...This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework.In-sample results indicate that oil futures intraday information is helpful to increase the predictability.Moreover,compared to the benchmark model,the proposed models improve their predictive ability with the help of oil futures realized volatility.In particular,the multivariate HAR model outperforms the univariate model.Accordingly,considering the contemporaneous connection is useful to predict the US stock market volatility.Furthermore,these findings are consistent across a variety of robust checks.展开更多
基金supported by the National Natural Science Foundation of China(Nos.72071166,71701176,and 72133003)。
文摘This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR models.The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly horizons.The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects.The out-of-sample results were robust across three different methods.More importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.
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supported by the National Natural Science Foundation of China(Nos.71871030,72131011)the Open Fund Project of Key Research Institute of Philosophies and Social Sciences in Hunan University of China(No.20FEFMZ1).
文摘This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)futures.We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models.Our empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type models.The results also indicate that EPU has noticeable effect on the volatility of EUA futures.Specifically,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index.Robustness checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures prices.Additionally,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.
基金Yaojie Zhang acknowledges the financial support from the National Natural Science Foundation of China(72001110)the Fundamental Research Funds for the Central Universities(30919013232)+4 种基金the Research Fund for Young Teachers of School of Economics and Management,NJUST(JGQN2009)Yudong Wang acknowledges the financial support from the National Natural Science Foundation of China(72071114)Feng Ma acknowledges the support from the National Natural Science Foundation of China(71701170,72071162)Yu Wei acknowledges the support from the National Natural Science Foundation of China(71671145,71971191)Science and technology innovation team of Yunnan provincial.
文摘A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility.To address this issue,we find a phenomenon,“momentum of jumps”(MoJ),that the predictive ability of the jump component is persistent when forecasting the oil futures market volatility.Specifically,we propose a strategy that allows the predictive model to switch between a benchmark model without jumps and an alternative model with a jump component according to their recent past forecasting performance.The volatility data are based on the intraday prices of West Texas Intermediate.Our results indicate that this simple strategy significantly outperforms the individual models and a series of competing strategies such as forecast combinations and shrinkage methods.A mean–variance investor who targets a constant Sharpe ratio can realize the highest economic gains using the MoJ-based volatility forecasts.Our findings survive a wide variety of robustness tests,including different jump measures,alternative volatility measures,various financial markets,and extensive model specifications.
文摘This paper studies the performance of the GARCH model and two of its non linear modifications to forecast China′s weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and Runkle models which have proposed to describe the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations and that the GJR model cannot be recommended for forecasting.
基金Supported by the National Natural Science Foundation of China(71490725)the Humanities and Social Science Project of Ministry of Education(14YJA630015)
文摘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.
基金supported by the Natural Science Foundation of China[71701170,71901041,71971191,72071162]
文摘This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework.In-sample results indicate that oil futures intraday information is helpful to increase the predictability.Moreover,compared to the benchmark model,the proposed models improve their predictive ability with the help of oil futures realized volatility.In particular,the multivariate HAR model outperforms the univariate model.Accordingly,considering the contemporaneous connection is useful to predict the US stock market volatility.Furthermore,these findings are consistent across a variety of robust checks.