This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk e...This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.展开更多
This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neu...This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.展开更多
文摘This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.
文摘This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
