Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading act...Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.展开更多
In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump inte...In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump intensities of the assets are stochastic.Under the proposed framework,first,we derive the analytical pricing formula for power options by using Fourier transform technique,Esscher transform and characteristic function.Then we provide the efficient approximation to calculate the analytical pricing formula of power options by using the FFT approach and examine the accuracy of the approximation by Monte Carlo simulation.Finally,we provide some sensitivity analysis of the model parameters to power options.Numerical examples show this model is suitable for empirical work in practice.展开更多
Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,...Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.展开更多
This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign cur...Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.展开更多
Motivated by a significant impact of price volatility on food security and economic stability inCameroon,this study aims to understand the factors influencing agricultural product price volatility(APPV)and formulateef...Motivated by a significant impact of price volatility on food security and economic stability inCameroon,this study aims to understand the factors influencing agricultural product price volatility(APPV)and formulateeffective policies for mitigating its negative impactand promoting sustainable economic growth.Specifically,this research used theautoregressive distributed lag-error correction model(ARDL-ECM)to analyse the impact of agricultural productivity,agricultural product imports,population,temperature variation,gross domestic product(GDP)per capita,and government expenditure on APPV based on the annual data from 2000 to 2021.The ARDL-ECM estimation results revealed that agricultural productivity(β=4.901),agricultural product imports(β=1.012),population(β=13.635),and GDP per capita(β=2.794)were positively related toAPPV,while temperature variation(β=-0.990)and government expenditure(β=-8.585)were negatively related toAPPVin the long term.However,temperature variation had a positive relationship with APPV in the short term.Moreover,the Granger causality test showed that there werebidirectional causality of APPV with agricultural productivityandagricultural product imports,and unidirectional causality of APPVwith population,temperature variation,GDP per capita,and government expenditure.The findings highlight the importance of public policies in stabilizing agricultural product prices by investing in agricultural research,improving access to agricultural inputs,strengthening farmer capacities,implementing climate adaptation measures,and enhancing rural infrastructure.Thesepolicies can reduce APPV,improve food security,and promote inclusive economic growth in Cameroon.展开更多
Determining which variables affect price realized volatility has always been challenging.This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast.T...Determining which variables affect price realized volatility has always been challenging.This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast.The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility.In particular,the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index,euro-US dollar exchange rate,price of gold,and price of Brent crude oil on the realized volatility of natural gas.These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed;the euro-US dollar exchange rate was the primary financial asset and explained 40.1% of the influence.The results of the proposed daily analysis differed from those of the methodology used to study the entire period.The traditional model,which studies the entire period,cannot determine temporal effects,whereas the proposed methodology can.The proposed methodology allows us to distinguish the effects for each day,week,or month rather than averages for entire periods,with the flexibility to analyze different frequencies and periods.This methodological capability is key to analyzing influences and making decisions about realized volatility.展开更多
This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatili...This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.展开更多
To investigate the volatility of atmospheric particulates and the evolution of other particulate properties(chemical composition,particle size distribution and mixing state)with temperature,a thermodenuder coupled wit...To investigate the volatility of atmospheric particulates and the evolution of other particulate properties(chemical composition,particle size distribution and mixing state)with temperature,a thermodenuder coupled with a single particle aerosol mass spectrometer was used to conduct continuous observations of atmospheric fine particles in Chengdu,southwest China.Because of their complex sources and secondary reaction processes,the average mass spectra of single particles contained a variety of chemical components(including organic,inorganic and metal species).When the temperature rose from room temperature to280℃,the relative areas of volatile and semi-volatile components decreased,while the relative areas of less or non-volatile components increased.Most(>80%)nitrate and sulfate existed in the form of NH_(4)NO_(3)and(NH_(4))_(2)SO_(4),and their volatilization temperatures were50–100℃and 150–280℃,respectively.The contribution of biomass burning(BB)and vehicle emission(VE)particles increased significantly at 280℃,which emphasized the important role of regional biomass burning and local motor vehicle emissions to the core of particles.With the increase in temperature,the particle size of the particles coated with volatile or semi-volatile components was reduced,and their mixing with secondary inorganic components was significantly weakened.The formation of K-nitrate(KNO_(3))and K-sulfate(KSO_(4))particles was dominated by liquid-phase processes and photochemical reactions,respectively.Reducing KNO_(3)and BB particles is the key to improving visibility.These new results are helpful towards better understanding the initial sources,pollution formation mechanisms and climatic effects of fine particulate matter in this megacity in southwest China.展开更多
Under high relative humidity(RH)conditions,the release of volatile components(such as acetate)has a significant impact on the aerosol hygroscopicity.In this work,one surface plasmon resonance microscopy(SPRM)measureme...Under high relative humidity(RH)conditions,the release of volatile components(such as acetate)has a significant impact on the aerosol hygroscopicity.In this work,one surface plasmon resonance microscopy(SPRM)measurement system was introduced to determine the hygroscopic growth factors(GFs)of three acetate aerosols separately or mixed with glucose at different RHs.For Ca(CH_(3)COO)_(2) or Mg(CH_(3)COO)_(2) aerosols,the hygroscopic growth trend of each time was lower than that of the previous time in three cyclic humidification from 70% RH to 90% RH,which may be due to the volatility of acetic acid leading to the formation of insoluble hydroxide(Ca(OH)_(2) or Mg(OH)_(2))under high RH conditions.Then the third calculated GF(using the Zdanovskii-Stokes-Robinson method)for Ca(CH_(3)COO)_(2) or Mg(CH_(3)COO)_(2) in bicomponent aerosols with 1:1 mass ratio were 3.20% or 5.33% lower than that of the first calculated GF at 90% RH.The calculated results also showed that the hygroscopicity change of bicomponent aerosol was negatively correlated with glucose content,especially when the mass ratio of Mg(CH_(3)COO)_(2) to glucose was 1:2,the GF at 90% RH only decreased by4.67% after three cyclic humidification.Inductively coupled plasma atomic emission spectrum(ICP-AES)based measurements also indicated that the changes of Mg^(2+)concentration in bicomponent was lower than that of the single-component.The results of this study reveal thatduring the efflorescence transitions of atmospheric nanoparticles,the organic acids diffusion rate may be inhibited by the coating effect of neutral organic components,and the particles aging cycle will be prolonged.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
We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of asse...We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals,whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation.The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy,and the profitability of the HFRA is examined to determine the optimal portfolio size.The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes.Using cointegrated price data,it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment.For low-volatility regimes,although increasing portfolio size marginally enhances the HFRA’s profitability,the profits of portfolios of varied sizes do not significantly differ.It is demonstrated that when volatility is relatively high and the trend is upward,the HFRA can yield a substantial return via portfolios of large sizes.Moreover,the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application.The HFRA is more profitable than the PR and TR strategies.This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.展开更多
This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effect...This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effects,especially the high volatility and lack of regulation in the cryptocurrency market.The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets.A daily time series dataset from January 1,2017,to December 31,2021,was employed to analyze the contagion effect.Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market;however,the percentage spillover effect is still low but growing.Specifically,evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets,at least in the short run.Evidence in South Africa,Nigeria,and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market.Similarly,we found no evidence of a spillover effect from the African financial market to the cryptocurrency market.The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market.Specifically,the DCC-GARCH revealed a greater integration in both markets,especially in the long run.The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.展开更多
This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethe...This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.展开更多
This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),an...This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),and four mega-technology companies(Apple,Microsoft,Alphabet,and Amazon)in the US.We analyze data for the period November 15,2017-October 28,2022 using methodologies in Diebold and Yilmaz(Int J Forecast 28(1):57-66,2012)and Baruník and Krehlík(J Financ Economet 16(2):271-2962018).Our analysis shows the COVID-19 pandemic amplified volatility spillovers,thereby intensifying the impact of financial contagion between markets.This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets.Moreover,we show that Bitcoin,Ethereum,Chevron,ConocoPhilips,Apple,and Microsoft are net volatility transmitters,while Tether,BNB,Exxon Mobil,Nextera Energy,Alphabet,and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium-and long-term spillovers,and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected.Furthermore,in contrast to short-term dynamics,longer term patterns display superior hedging efficiency.The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies.The findings in this study have implications for both investors and policymakers.展开更多
The consideration of environmental,social,and governance(ESG)aspects has become an integral part of investment decisions for individual and institutional investors.Most recently,corporate leaders recognized the core v...The consideration of environmental,social,and governance(ESG)aspects has become an integral part of investment decisions for individual and institutional investors.Most recently,corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts.While stock market prediction is a complex and challenging task,several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices.To address this challenge,we propose an integrated computational framework to implement deep learning model architectures,specifically long short-term memory(LSTM),gated recurrent unit,and convolutional neural network,to predict the volatility of the ESG index in an identical environment.A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data,technical indicators,and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction.The performance of the constructed models was evaluated using standard assessment metrics.Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model.Furthermore,a series of statistical analyses was conducted to validate the robustness and reliability of the model.Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.展开更多
The effects of stochastic volatility,jump clustering,and regime switching are considered when pricing variance swaps.This study established a two-stage procedure that simplifies the derivation by first isolating the r...The effects of stochastic volatility,jump clustering,and regime switching are considered when pricing variance swaps.This study established a two-stage procedure that simplifies the derivation by first isolating the regime switching from other stochastic sources.Based on this,a novel probabilistic approach was employed,leading to pricing formulas with time-dependent and regime-switching parameters.The formulated solutions were easy to implement and differed from most existing results of variance swap pricing,where Fourier inversion or fast Fourier transform must be performed to obtain the final results,since they are completely analytical without involving integrations.The numerical results indicate that jump clustering and regime switching have a significant influence on variance swap prices.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
Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to ...Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.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
文摘Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.
文摘In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump intensities of the assets are stochastic.Under the proposed framework,first,we derive the analytical pricing formula for power options by using Fourier transform technique,Esscher transform and characteristic function.Then we provide the efficient approximation to calculate the analytical pricing formula of power options by using the FFT approach and examine the accuracy of the approximation by Monte Carlo simulation.Finally,we provide some sensitivity analysis of the model parameters to power options.Numerical examples show this model is suitable for empirical work in practice.
文摘Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
文摘Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
文摘Motivated by a significant impact of price volatility on food security and economic stability inCameroon,this study aims to understand the factors influencing agricultural product price volatility(APPV)and formulateeffective policies for mitigating its negative impactand promoting sustainable economic growth.Specifically,this research used theautoregressive distributed lag-error correction model(ARDL-ECM)to analyse the impact of agricultural productivity,agricultural product imports,population,temperature variation,gross domestic product(GDP)per capita,and government expenditure on APPV based on the annual data from 2000 to 2021.The ARDL-ECM estimation results revealed that agricultural productivity(β=4.901),agricultural product imports(β=1.012),population(β=13.635),and GDP per capita(β=2.794)were positively related toAPPV,while temperature variation(β=-0.990)and government expenditure(β=-8.585)were negatively related toAPPVin the long term.However,temperature variation had a positive relationship with APPV in the short term.Moreover,the Granger causality test showed that there werebidirectional causality of APPV with agricultural productivityandagricultural product imports,and unidirectional causality of APPVwith population,temperature variation,GDP per capita,and government expenditure.The findings highlight the importance of public policies in stabilizing agricultural product prices by investing in agricultural research,improving access to agricultural inputs,strengthening farmer capacities,implementing climate adaptation measures,and enhancing rural infrastructure.Thesepolicies can reduce APPV,improve food security,and promote inclusive economic growth in Cameroon.
文摘Determining which variables affect price realized volatility has always been challenging.This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast.The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility.In particular,the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index,euro-US dollar exchange rate,price of gold,and price of Brent crude oil on the realized volatility of natural gas.These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed;the euro-US dollar exchange rate was the primary financial asset and explained 40.1% of the influence.The results of the proposed daily analysis differed from those of the methodology used to study the entire period.The traditional model,which studies the entire period,cannot determine temporal effects,whereas the proposed methodology can.The proposed methodology allows us to distinguish the effects for each day,week,or month rather than averages for entire periods,with the flexibility to analyze different frequencies and periods.This methodological capability is key to analyzing influences and making decisions about realized volatility.
基金National Natural Science Foundation of China(Grant no.71771006)Science and Technology Support Plan of Guizhou(Grant no.2023-221).
文摘This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.
基金supported by the Sichuan Natural Science Foundation (No.2022NSFSC0982)the Sichuan Science and Technology Program (No.2019YFS0476)the National Natural Science Foundation of China (No.41805095)。
文摘To investigate the volatility of atmospheric particulates and the evolution of other particulate properties(chemical composition,particle size distribution and mixing state)with temperature,a thermodenuder coupled with a single particle aerosol mass spectrometer was used to conduct continuous observations of atmospheric fine particles in Chengdu,southwest China.Because of their complex sources and secondary reaction processes,the average mass spectra of single particles contained a variety of chemical components(including organic,inorganic and metal species).When the temperature rose from room temperature to280℃,the relative areas of volatile and semi-volatile components decreased,while the relative areas of less or non-volatile components increased.Most(>80%)nitrate and sulfate existed in the form of NH_(4)NO_(3)and(NH_(4))_(2)SO_(4),and their volatilization temperatures were50–100℃and 150–280℃,respectively.The contribution of biomass burning(BB)and vehicle emission(VE)particles increased significantly at 280℃,which emphasized the important role of regional biomass burning and local motor vehicle emissions to the core of particles.With the increase in temperature,the particle size of the particles coated with volatile or semi-volatile components was reduced,and their mixing with secondary inorganic components was significantly weakened.The formation of K-nitrate(KNO_(3))and K-sulfate(KSO_(4))particles was dominated by liquid-phase processes and photochemical reactions,respectively.Reducing KNO_(3)and BB particles is the key to improving visibility.These new results are helpful towards better understanding the initial sources,pollution formation mechanisms and climatic effects of fine particulate matter in this megacity in southwest China.
基金supported by the National Natural Science Foundation of China(Nos.41905028,91544218,12134013,and 62127818)the National Key Researchand Development Program of China(No.2017YFC0209504)+3 种基金Anhui Provincial Natural Science Foundation(Nos.1908085MD114 and 2108085MD139)the Hefei Municipal Natural Science Foundation(No.2021007)the Key Research&Development program of Anhui Province(No.202104a05020010)the HFIPS Director’s Fund(Nos.YZJJ2022QN04 and BJPY2021A04)。
文摘Under high relative humidity(RH)conditions,the release of volatile components(such as acetate)has a significant impact on the aerosol hygroscopicity.In this work,one surface plasmon resonance microscopy(SPRM)measurement system was introduced to determine the hygroscopic growth factors(GFs)of three acetate aerosols separately or mixed with glucose at different RHs.For Ca(CH_(3)COO)_(2) or Mg(CH_(3)COO)_(2) aerosols,the hygroscopic growth trend of each time was lower than that of the previous time in three cyclic humidification from 70% RH to 90% RH,which may be due to the volatility of acetic acid leading to the formation of insoluble hydroxide(Ca(OH)_(2) or Mg(OH)_(2))under high RH conditions.Then the third calculated GF(using the Zdanovskii-Stokes-Robinson method)for Ca(CH_(3)COO)_(2) or Mg(CH_(3)COO)_(2) in bicomponent aerosols with 1:1 mass ratio were 3.20% or 5.33% lower than that of the first calculated GF at 90% RH.The calculated results also showed that the hygroscopicity change of bicomponent aerosol was negatively correlated with glucose content,especially when the mass ratio of Mg(CH_(3)COO)_(2) to glucose was 1:2,the GF at 90% RH only decreased by4.67% after three cyclic humidification.Inductively coupled plasma atomic emission spectrum(ICP-AES)based measurements also indicated that the changes of Mg^(2+)concentration in bicomponent was lower than that of the single-component.The results of this study reveal thatduring the efflorescence transitions of atmospheric nanoparticles,the organic acids diffusion rate may be inhibited by the coating effect of neutral organic components,and the particles aging cycle will be prolonged.
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We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals,whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation.The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy,and the profitability of the HFRA is examined to determine the optimal portfolio size.The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes.Using cointegrated price data,it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment.For low-volatility regimes,although increasing portfolio size marginally enhances the HFRA’s profitability,the profits of portfolios of varied sizes do not significantly differ.It is demonstrated that when volatility is relatively high and the trend is upward,the HFRA can yield a substantial return via portfolios of large sizes.Moreover,the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application.The HFRA is more profitable than the PR and TR strategies.This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.
文摘This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effects,especially the high volatility and lack of regulation in the cryptocurrency market.The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets.A daily time series dataset from January 1,2017,to December 31,2021,was employed to analyze the contagion effect.Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market;however,the percentage spillover effect is still low but growing.Specifically,evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets,at least in the short run.Evidence in South Africa,Nigeria,and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market.Similarly,we found no evidence of a spillover effect from the African financial market to the cryptocurrency market.The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market.Specifically,the DCC-GARCH revealed a greater integration in both markets,especially in the long run.The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.
文摘This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
文摘This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),and four mega-technology companies(Apple,Microsoft,Alphabet,and Amazon)in the US.We analyze data for the period November 15,2017-October 28,2022 using methodologies in Diebold and Yilmaz(Int J Forecast 28(1):57-66,2012)and Baruník and Krehlík(J Financ Economet 16(2):271-2962018).Our analysis shows the COVID-19 pandemic amplified volatility spillovers,thereby intensifying the impact of financial contagion between markets.This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets.Moreover,we show that Bitcoin,Ethereum,Chevron,ConocoPhilips,Apple,and Microsoft are net volatility transmitters,while Tether,BNB,Exxon Mobil,Nextera Energy,Alphabet,and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium-and long-term spillovers,and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected.Furthermore,in contrast to short-term dynamics,longer term patterns display superior hedging efficiency.The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies.The findings in this study have implications for both investors and policymakers.
文摘The consideration of environmental,social,and governance(ESG)aspects has become an integral part of investment decisions for individual and institutional investors.Most recently,corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts.While stock market prediction is a complex and challenging task,several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices.To address this challenge,we propose an integrated computational framework to implement deep learning model architectures,specifically long short-term memory(LSTM),gated recurrent unit,and convolutional neural network,to predict the volatility of the ESG index in an identical environment.A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data,technical indicators,and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction.The performance of the constructed models was evaluated using standard assessment metrics.Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model.Furthermore,a series of statistical analyses was conducted to validate the robustness and reliability of the model.Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.
基金supported by the National Natural Science Foundation of China(Nos.12101554,12301614),the Fundamental Research Funds for Zhejiang Provincial Universities(No.GB202103001)Zhejiang Provincial Natural Science Foundation of China(No.LQ22A010010)Ministry of Educational Social Science Foundation of China(No.21YJC880050).
文摘The effects of stochastic volatility,jump clustering,and regime switching are considered when pricing variance swaps.This study established a two-stage procedure that simplifies the derivation by first isolating the regime switching from other stochastic sources.Based on this,a novel probabilistic approach was employed,leading to pricing formulas with time-dependent and regime-switching parameters.The formulated solutions were easy to implement and differed from most existing results of variance swap pricing,where Fourier inversion or fast Fourier transform must be performed to obtain the final results,since they are completely analytical without involving integrations.The numerical results indicate that jump clustering and regime switching have a significant influence on variance swap prices.
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Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.
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.
