The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market cras...The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option insurance.These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively.The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options markets.These stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial markets.These are expected to be true for Bitcoin options as well.The data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative exchange.The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis.This study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection methods.The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data.Moreover,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options.However,the Newton Raphson forecasting technique converges faster than does the Bisection method.展开更多
This paper concerns an inverse problem of recovering implied volatility in short-term interest rate model from the market prices of zero-coupon bonds. Based on lineariza-tion, an analytic solution, which is given as a...This paper concerns an inverse problem of recovering implied volatility in short-term interest rate model from the market prices of zero-coupon bonds. Based on lineariza-tion, an analytic solution, which is given as a power series, is derived for the direct problem.By neglecting high order terms in the power series, an integral equation about the pertur-bation of volatility is formulated and the Tikhonov regularization method is applied to solvethe integral equation. Finally numerical experiments are given and the results show that the method is effective.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
Stock volatility constitutes an adverse psychological stressor,but few large-scale studies have focused on its impact on major adverse cardiovascular events(MACEs)and suicide.Here,we conducted an individual-level time...Stock volatility constitutes an adverse psychological stressor,but few large-scale studies have focused on its impact on major adverse cardiovascular events(MACEs)and suicide.Here,we conducted an individual-level time-stratified case-crossover study to explore the association of daily stock volatility(daily returns and intra-daily oscillations for three kinds of stock indices)with MACEs and suicide among more than 12 million individual decedents from all counties in the mainland of China between 2013 and 2019.For daily stock returns,both stock increases and decreases were associated with increased mortal-ity risks of all MACEs and suicide.There were consistent and positive associations between intra-daily stock oscillations and mortality due to MACEs and suicide.The excess mortality risks occurred at the cur-rent day(lag 0 d),persisted for two days,and were greatest for suicide and hemorrhagic stroke.Taking the present-day Shanghai and Shenzhen 300 Index as an example,a 1%decrease in daily returns was associated with 0.74%-1.04%and 1.77%increases in mortality risks of MACEs and suicide,respectively;the corresponding risk increments were 0.57%-0.85%and 0.92%for a 1%increase in daily returns and 0.67%-0.77%and 1.09%for a 1%increase in intra-daily stock oscillations.The excess risks were more pro-nounced among individuals aged 65-74 years,males,and those with lower education levels.Our findings revealed considerable health risks linked to sociopsychological stressors,which are helpful for the gov-ernment and general public to mitigate the immediate cardiovascular and mental health risks associated with stock market volatility.展开更多
This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid t...This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.展开更多
Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of Europ...Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.展开更多
Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This...Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.展开更多
The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to Dec...The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.展开更多
文摘The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option insurance.These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively.The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options markets.These stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial markets.These are expected to be true for Bitcoin options as well.The data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative exchange.The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis.This study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection methods.The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data.Moreover,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options.However,the Newton Raphson forecasting technique converges faster than does the Bisection method.
基金Supported by the National Natural Science Foundation of China(11171349)
文摘This paper concerns an inverse problem of recovering implied volatility in short-term interest rate model from the market prices of zero-coupon bonds. Based on lineariza-tion, an analytic solution, which is given as a power series, is derived for the direct problem.By neglecting high order terms in the power series, an integral equation about the pertur-bation of volatility is formulated and the Tikhonov regularization method is applied to solvethe integral equation. Finally numerical experiments are given and the results show that the method is effective.
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supported by the National Key Research and Development Program(2022YFC3702701)the Shanghai Municipal Science and Technology Commission(21TQ015)the Shanghai International Science and Technology Partnership Project,China(21230780200).
文摘Stock volatility constitutes an adverse psychological stressor,but few large-scale studies have focused on its impact on major adverse cardiovascular events(MACEs)and suicide.Here,we conducted an individual-level time-stratified case-crossover study to explore the association of daily stock volatility(daily returns and intra-daily oscillations for three kinds of stock indices)with MACEs and suicide among more than 12 million individual decedents from all counties in the mainland of China between 2013 and 2019.For daily stock returns,both stock increases and decreases were associated with increased mortal-ity risks of all MACEs and suicide.There were consistent and positive associations between intra-daily stock oscillations and mortality due to MACEs and suicide.The excess mortality risks occurred at the cur-rent day(lag 0 d),persisted for two days,and were greatest for suicide and hemorrhagic stroke.Taking the present-day Shanghai and Shenzhen 300 Index as an example,a 1%decrease in daily returns was associated with 0.74%-1.04%and 1.77%increases in mortality risks of MACEs and suicide,respectively;the corresponding risk increments were 0.57%-0.85%and 0.92%for a 1%increase in daily returns and 0.67%-0.77%and 1.09%for a 1%increase in intra-daily stock oscillations.The excess risks were more pro-nounced among individuals aged 65-74 years,males,and those with lower education levels.Our findings revealed considerable health risks linked to sociopsychological stressors,which are helpful for the gov-ernment and general public to mitigate the immediate cardiovascular and mental health risks associated with stock market volatility.
文摘This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.
文摘Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.
基金This work was partially supported by the National Natural Science Foundation of China(Grant No.:72171192)the MOE Layout Foundation of Humanities and Social Sciences(Grant No.:22YJA790007)+1 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.:2021RC3057)the Youth Innovation Team of Shanxi University,and the Fundamental Research Funds for the Central Universities.
文摘Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.
基金Project(71071166)supported by the National Natural Science Foundation of China
文摘The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.
