Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a pr...Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.展开更多
The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-secti...The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial markets.In this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/4.Furthermore,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization procedure.Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise.Additionally,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.展开更多
We employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day,where the volatility is measured by daily realized volatility.Our em...We employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day,where the volatility is measured by daily realized volatility.Our empirical results,based on long samples of 8 well-known stock indexes,suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility.The level of dependence change over time in a persistent manner.And the dependence structure presents some distinct differences between emerging market indexes and developed market indexes.These results indicate that the technical indicators can provide information on the next day volatility at extremes,and are less informative at normal market.展开更多
Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution int...Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.展开更多
Academic research has identified several factors that affect price movements;however,the scenario changes abruptly in the case of very short time price changes(VSTPC).This topic is not specifically examined in the exi...Academic research has identified several factors that affect price movements;however,the scenario changes abruptly in the case of very short time price changes(VSTPC).This topic is not specifically examined in the existing literature;nonetheless,the behavior of the market microstructure is quite different at the subsecond scale.Indeed,below a certain psychological time threshold,most factors typically influencing price changes cease to apply.This paper analyzes several parameters considered to affect price changes and identifies four of them as potentially influencing VSTPC.These factors are previous volatility,scarce liquidity,high quantity exchanged,and stop-loss(SL)orders(seldom mentioned in the literature).These four parameters are examined by means of a mathematical model,audit trail data analysis,Granger-causality testing,and agent-based model.The results of these four techniques converge to suggest a nonlinear combination of previous volatility,liquidity,and SL orders as the main causes of excess volatility.However,contrary to mainstream literature on trading time above a certain psychological threshold,the volumes exchanged are not integral agents for VSTPC.Currently,financial markets face many ultrafast orders,yet a coherent theory of price change at time scales incomprehensible by humans and only manageable by computers is still lacking.The theory presented in this paper attempts to fill this gap.The outcome of such a theory is important for purposes of market stability,crisis avoidance,investment planning,risk management,and high-frequency trading.展开更多
This commentary is based on the work of Cooper,Davis,and Van Vliet(2016)and the commentary focuses on what problem high-frequency trading poses.It lists key literature on high-frequency trading that is missing and poi...This commentary is based on the work of Cooper,Davis,and Van Vliet(2016)and the commentary focuses on what problem high-frequency trading poses.It lists key literature on high-frequency trading that is missing and points out that the poker analogy to defend deception in financial markets is weak and misleading.The article elaborates on the negative impact created by spoofing and quote stuffing,the two typical deceptive practices used by high-frequency traders.The recent regulations regarding high-frequency trading,in response to the“Flash Crash”of 2010,are preventive,computerized and more effective.They reflect ethical requirements to maintain fair and stable financial markets.展开更多
文摘Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.
基金supported by the National Natural Science Foundation of China under Grant Nos.72271232,71873137the MOE Project of Key Research Institute of Humanities and Social Sciences under Grant No.22JJD110001+1 种基金the support of Public Computing CloudRenmin University of China。
文摘The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial markets.In this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/4.Furthermore,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization procedure.Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise.Additionally,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.
基金Supported by the Natural Science Foundation of Ningbo(2018A610130)National Statistical Science Research Program(2019LY71)+3 种基金MOE(Ministry of Education in China)Liberal Arts and Social Sciences FoundationNational Natural Science Foundation of China(11771399)Ningbo Soft Science Foundation(2017A10113)Fujian Education Department(JT180444).
文摘We employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day,where the volatility is measured by daily realized volatility.Our empirical results,based on long samples of 8 well-known stock indexes,suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility.The level of dependence change over time in a persistent manner.And the dependence structure presents some distinct differences between emerging market indexes and developed market indexes.These results indicate that the technical indicators can provide information on the next day volatility at extremes,and are less informative at normal market.
基金supported by the National Natural Science Foundation of China(Nos.72141304,71790594,71901130)。
文摘Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.
文摘Academic research has identified several factors that affect price movements;however,the scenario changes abruptly in the case of very short time price changes(VSTPC).This topic is not specifically examined in the existing literature;nonetheless,the behavior of the market microstructure is quite different at the subsecond scale.Indeed,below a certain psychological time threshold,most factors typically influencing price changes cease to apply.This paper analyzes several parameters considered to affect price changes and identifies four of them as potentially influencing VSTPC.These factors are previous volatility,scarce liquidity,high quantity exchanged,and stop-loss(SL)orders(seldom mentioned in the literature).These four parameters are examined by means of a mathematical model,audit trail data analysis,Granger-causality testing,and agent-based model.The results of these four techniques converge to suggest a nonlinear combination of previous volatility,liquidity,and SL orders as the main causes of excess volatility.However,contrary to mainstream literature on trading time above a certain psychological threshold,the volumes exchanged are not integral agents for VSTPC.Currently,financial markets face many ultrafast orders,yet a coherent theory of price change at time scales incomprehensible by humans and only manageable by computers is still lacking.The theory presented in this paper attempts to fill this gap.The outcome of such a theory is important for purposes of market stability,crisis avoidance,investment planning,risk management,and high-frequency trading.
文摘This commentary is based on the work of Cooper,Davis,and Van Vliet(2016)and the commentary focuses on what problem high-frequency trading poses.It lists key literature on high-frequency trading that is missing and points out that the poker analogy to defend deception in financial markets is weak and misleading.The article elaborates on the negative impact created by spoofing and quote stuffing,the two typical deceptive practices used by high-frequency traders.The recent regulations regarding high-frequency trading,in response to the“Flash Crash”of 2010,are preventive,computerized and more effective.They reflect ethical requirements to maintain fair and stable financial markets.
