This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future ...This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future stock returns,and higher ULD leads to the subsequent decline of stock returns.Our results show that our new predictor generates R^2 values of up to 2.531%and 3.988%in-sample and out-of-sample,respectively;these values are much larger than the previous fundamental predictors.Moreover,the predictive information contained in ULD can help mean–variance investors achieve certainty equivalent return gains of as high as 327.1 basis points.Finally,the extension analysis and robustness tests indicate that recession is the primary cause of return predictability;our results are robust under different settings.展开更多
The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a...The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.展开更多
We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure ...We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.展开更多
基金supported by the National Natural Science Foundation of China granted:72131011Ministry of Education Humanities and Social Sciences Project granted:22YJA790011.
文摘This paper proposes a new predictor by calculating the difference between the Japanese candlestick’s upper and lower shadows(ULD)constructed from CBOE volatility index(VIX)data.ULD is a powerful predictor for future stock returns,and higher ULD leads to the subsequent decline of stock returns.Our results show that our new predictor generates R^2 values of up to 2.531%and 3.988%in-sample and out-of-sample,respectively;these values are much larger than the previous fundamental predictors.Moreover,the predictive information contained in ULD can help mean–variance investors achieve certainty equivalent return gains of as high as 327.1 basis points.Finally,the extension analysis and robustness tests indicate that recession is the primary cause of return predictability;our results are robust under different settings.
基金This research has been funded by Scientific Research Deanship at University of Ha’il,Saudi Arabia through Project number RG-20210.
文摘The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.
基金financial interest(such as honorariaeducational grants+2 种基金participation in speakers’bureausmembership,employment,consultancies,stock ownership,or other equity interestand expert testimony or patent-licensing arrangements),or nonfinancial interest(such as personal or professional relationships,affiliations,knowledge or beliefs)in the subject matter or materials discussed in this manuscript.
文摘We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.
