The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algo...The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.展开更多
Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.T...Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.展开更多
This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on wh...This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.展开更多
文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能...文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能够捕捉上证综指的波动特征,具有更好的波动率拟合和预测效果,且VaR度量效果更优。研究丰富了时变长记忆高频波动率模型,从时变波动和噪声异方差结构视角为投资者和监管机构进行风险管控提供参考。展开更多
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
运用Realized GARCH模型用于期权定价,并考虑隔夜效应的影响及运用Hansen and Lunde(2005a)方法对隔夜效应进行调整,最后将其定价结果与Black-Scholes期权定价结果、GARCH期权定价结果及实际值进行比较。实证结果表明,基于Realized GARC...运用Realized GARCH模型用于期权定价,并考虑隔夜效应的影响及运用Hansen and Lunde(2005a)方法对隔夜效应进行调整,最后将其定价结果与Black-Scholes期权定价结果、GARCH期权定价结果及实际值进行比较。实证结果表明,基于Realized GARCH模型的期权定价结果比经典的Black-Scholes模型和GARCH模型具有更高的定价精确性。展开更多
为了对股票市场的非线性结构进行研究,通过Realized GARCH将高频数据的信息引入到模型之中,并结合Pair Copula分解法对股票指数进行分析,建立股票指数之间的结构关系。计算了AIC(Akaike information criterion)/BIC(Bayesian informatio...为了对股票市场的非线性结构进行研究,通过Realized GARCH将高频数据的信息引入到模型之中,并结合Pair Copula分解法对股票指数进行分析,建立股票指数之间的结构关系。计算了AIC(Akaike information criterion)/BIC(Bayesian information criterion)的值以确定最优Pair Copula函数,最终发现C-Vine的结构更加适合被用来描述股票市场间关系。展开更多
A new type of superconductive true random number generator(TRNG) based on a negative-inductance superconducting quantum interference device(nSQUID) is proposed. The entropy harnessed to generate random numbers comes f...A new type of superconductive true random number generator(TRNG) based on a negative-inductance superconducting quantum interference device(nSQUID) is proposed. The entropy harnessed to generate random numbers comes from the phenomenon of symmetry breaking in the nSQUID. The experimental circuit is fabricated by the Nb-based lift-off process. Low-temperature tests of the circuit verify the basic function of the proposed TRNG.The frequency characteristics of the TRNG have been analyzed by simulation. The generation rate of random numbers is expected to achieve hundreds of megahertz to tens of gigahertz.展开更多
When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approach...When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.展开更多
基金The National Natural Science Foundation of China(Grant No.81973791)funded this research.
文摘The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.
文摘Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.
文摘This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.
文摘文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能够捕捉上证综指的波动特征,具有更好的波动率拟合和预测效果,且VaR度量效果更优。研究丰富了时变长记忆高频波动率模型,从时变波动和噪声异方差结构视角为投资者和监管机构进行风险管控提供参考。
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.
文摘运用Realized GARCH模型用于期权定价,并考虑隔夜效应的影响及运用Hansen and Lunde(2005a)方法对隔夜效应进行调整,最后将其定价结果与Black-Scholes期权定价结果、GARCH期权定价结果及实际值进行比较。实证结果表明,基于Realized GARCH模型的期权定价结果比经典的Black-Scholes模型和GARCH模型具有更高的定价精确性。
文摘为了对股票市场的非线性结构进行研究,通过Realized GARCH将高频数据的信息引入到模型之中,并结合Pair Copula分解法对股票指数进行分析,建立股票指数之间的结构关系。计算了AIC(Akaike information criterion)/BIC(Bayesian information criterion)的值以确定最优Pair Copula函数,最终发现C-Vine的结构更加适合被用来描述股票市场间关系。
基金Supported by the State Key Program for Basic Research of China under Grant No 2011CBA00304the National Natural Science Foundation of China under Grant No 60836001the Tsinghua University Initiative Scientific Research Program under Grant No 20131089314
文摘A new type of superconductive true random number generator(TRNG) based on a negative-inductance superconducting quantum interference device(nSQUID) is proposed. The entropy harnessed to generate random numbers comes from the phenomenon of symmetry breaking in the nSQUID. The experimental circuit is fabricated by the Nb-based lift-off process. Low-temperature tests of the circuit verify the basic function of the proposed TRNG.The frequency characteristics of the TRNG have been analyzed by simulation. The generation rate of random numbers is expected to achieve hundreds of megahertz to tens of gigahertz.
文摘When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.
