This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoi...This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.展开更多
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
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses...The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.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
This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We exa...This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We examine the contemporaneous relationships using correlation coefficient analysis,and find apparent differences between the information flow-return volatility relationship and the information flowtrading volume relationship.The empirical evidence contradicts the mixture of distribution hypothesis(MDH)and suggests that the rate of information flow distinctly affects trading volume and volatility.We conducted linear and nonlinear Granger causality tests to explore the sequential information arrival hypothesis(SIAH).The empirical results prove that a lead-lag linear and nonlinear causality exists between the information flow and return volatility of commodity futures,which is consistent with SIAH.In other words,a partial equilibrium exists before reaching the ultimate equilibrium when the new information arrives in the market.Finally,these findings are robust to alternative measurement of return volatility and subperiod analysis.Our findings reject the MDH and support the SIAH in the context of Chinese commodity futures.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
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
This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency ...This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.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
In this paper, pyrolysis of Indonesian oil sands （lOS） was investigated by two different heating methods to develop a better understanding of the microwave-assisted pyrolysis. Thermogravimetric analysis was conducte...In this paper, pyrolysis of Indonesian oil sands （lOS） was investigated by two different heating methods to develop a better understanding of the microwave-assisted pyrolysis. Thermogravimetric analysis was conducted to study the thermal decomposition behaviors of lOS, showing that 550 ℃ might be the pyrolysis final temperature. A explanation of the heat-mass transfer process was presented to demonstrate the influence of mi- crowave-assisted pyrolysis on the liquid product distribution. The heat-mass transfer model was also useful to explain the increase of liquid product yield and heavy component content at the same heating rate by two differ- ent heating methods. Experiments were carried out using a fixed bed reactor with and without the microwave irradiation. The results showed that liquid product yield was increased during microwave induced pyrolysis, while the formation of gas and solid residue was reduced in comparison with the conventional pyrolysis. Moreover, the liquid product characterization by elemental analysis and GC-MS indicated the significant effect on the liquid chemical composition by microwave irradiation. High polarity substances （ε 〉 10 at 25 ℃）, such as oxy- organics were increased, while relatively low polarity substances （ε 〈 2 at 25℃）, such as aliphatic hydrocarbons were decreased, suggesting that microwave enhanced the relative volatility of high polarity substances. The yield improvement and compositional variations in the liquid product promoted by the microwave-assisted pyrolysis deserve the further exploitation in the future,展开更多
The North China Plain(NCP)is a region that experiences serious aerosol pollution.A number of studies have focused on aerosol pollution in urban areas in the NCP region;however,research on characterizing aerosols in ru...The North China Plain(NCP)is a region that experiences serious aerosol pollution.A number of studies have focused on aerosol pollution in urban areas in the NCP region;however,research on characterizing aerosols in rural NCP areas is comparatively limited.In this study,we deployed a TD-HR-AMS(thermodenuder high-resolution aerosol mass spectrometer)system at a rural site in the NCP region in summer 2013 to characterize the chemical compositions and volatility of submicron aerosols(PM_(1)).The average PM_(1)mass concentration was 51.2±48.0μg m^(−3) and organic aerosol(OA)contributed most(35.4%)to PM_(1).Positive matrix factorization(PMF)analysis of OA measurements identified four OA factors,including hydrocarbon-like OA(HOA,accounting for 18.4%),biomass burning OA(BBOA,29.4%),lessoxidized oxygenated OA(LO-OOA,30.8%)and more-oxidized oxygenated OA(MO-OOA,21.4%).The volatility sequence of the OA factors was HOA>BBOA>LO-OOA>MO-OOA,consistent with their oxygen-to-carbon(O:C)ratios.Additionally,the mean concentration of organonitrates(ON)was 1.48−3.39μg m−3,contributing 8.1%-19%of OA based on cross validation of two estimation methods with the high-resolution time-of-flight aerosol mass spectrometer(HRToF-AMS)measurement.Correlation analysis shows that ON were more correlated with BBOA and black carbon emitted from biomass burning but poorly correlated with LO-OOA.Also,volatility analysis for ON further confirmed that particulate ON formation might be closely associated with primary emissions in rural NCP areas.展开更多
The harmful trace elements will be released during coal utilization, which can cause environment pollution and further endangering human health, especially for heavy metal elements. Compared to combustion, the release...The harmful trace elements will be released during coal utilization, which can cause environment pollution and further endangering human health, especially for heavy metal elements. Compared to combustion, the release of heavy metal elements during coal pyrolysis process, as a critical initial reaction stage of combustion, has not received sufficient attention. In the present paper, a low rank coal, from Xinjiang province in China, was pyrolyzed in a fixed bed reactor from room temperature, at atmospheric pressure, with the heating rate of 10 °C/min, and the final pyrolysis temperature was from 400 to 800℃ with the interval of 100℃. The volatility of heavy metal elements (including As, Hg, Cd and Pb) during pyrolysis process was investigated. The results showed the volatility of all heavy metal elements increased obviously with increasing temperature, and followed the sequence as Hg > Cd > As > Pb, which was mainly caused by their thermodynamic property and occurrence modes in coal. The occurrence modes of heavy metals were studied by sink-andfloat test and sequential chemical extraction procedure, and it can be found that the heavy metal elements were mainly in the organic and residual states (clay minerals) in the raw coal. And most of the organic heavy metals escaped during the pyrolysis process, the remaining elements were mainly in the residual state, and the elements in Fe-Mn state also tended to remain in the char.展开更多
With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to eva...With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.展开更多
This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,t...This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.展开更多
文摘This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
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.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
基金supported by the Ministry of Education,Singapore,under its Academic Research Fund Tier 2 Grant MOE-T2EP20222-0003.
文摘The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.
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.
基金supported by the National Social Science Fund of China(24CGL027)the National Natural Science Foundation of China(72101009,72141304,72201122)National Key Research and Development Program of China(2022YFC3303304).
文摘This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We examine the contemporaneous relationships using correlation coefficient analysis,and find apparent differences between the information flow-return volatility relationship and the information flowtrading volume relationship.The empirical evidence contradicts the mixture of distribution hypothesis(MDH)and suggests that the rate of information flow distinctly affects trading volume and volatility.We conducted linear and nonlinear Granger causality tests to explore the sequential information arrival hypothesis(SIAH).The empirical results prove that a lead-lag linear and nonlinear causality exists between the information flow and return volatility of commodity futures,which is consistent with SIAH.In other words,a partial equilibrium exists before reaching the ultimate equilibrium when the new information arrives in the market.Finally,these findings are robust to alternative measurement of return volatility and subperiod analysis.Our findings reject the MDH and support the SIAH in the context of Chinese commodity futures.
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.
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.
基金Project(13&ZD169)supported by the Major Program of the National Social Science Foundation of ChinaProject(2016zzts009)supported by Doctoral Students Independent Explore Innovation Project of Central South University,China+3 种基金Project(13YJAZH149)supported by the Social Science Foundation of Ministry of Education of ChinaProject(2015JJ2182)supported by the Social Science Foundation of Hunan Province,ChinaProject(71573282)supported by the National Natural Science Foundation of ChinaProject(15K133)supported by the Educational Commission of Hunan Province of China
文摘This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.
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 National Key Research and Development Program of China(2016YFB0301800)the partial support by The Royal Society International Exchange Award(IE161344)the State Scholarship Fund of China Scholarship Council(CSC)(201706255020)
文摘In this paper, pyrolysis of Indonesian oil sands （lOS） was investigated by two different heating methods to develop a better understanding of the microwave-assisted pyrolysis. Thermogravimetric analysis was conducted to study the thermal decomposition behaviors of lOS, showing that 550 ℃ might be the pyrolysis final temperature. A explanation of the heat-mass transfer process was presented to demonstrate the influence of mi- crowave-assisted pyrolysis on the liquid product distribution. The heat-mass transfer model was also useful to explain the increase of liquid product yield and heavy component content at the same heating rate by two differ- ent heating methods. Experiments were carried out using a fixed bed reactor with and without the microwave irradiation. The results showed that liquid product yield was increased during microwave induced pyrolysis, while the formation of gas and solid residue was reduced in comparison with the conventional pyrolysis. Moreover, the liquid product characterization by elemental analysis and GC-MS indicated the significant effect on the liquid chemical composition by microwave irradiation. High polarity substances （ε 〉 10 at 25 ℃）, such as oxy- organics were increased, while relatively low polarity substances （ε 〈 2 at 25℃）, such as aliphatic hydrocarbons were decreased, suggesting that microwave enhanced the relative volatility of high polarity substances. The yield improvement and compositional variations in the liquid product promoted by the microwave-assisted pyrolysis deserve the further exploitation in the future,
基金This work was supported by the Ministry of Science and Technology of China(Grant No.2017YFC0210004)the National Natural Science Foundation of China(Grant No.91744202)the China Postdoctoral Science Foundation and Guangdong Province Outstanding Young Talents for the International Education&Development Plan:Post-Doctoral Program.
文摘The North China Plain(NCP)is a region that experiences serious aerosol pollution.A number of studies have focused on aerosol pollution in urban areas in the NCP region;however,research on characterizing aerosols in rural NCP areas is comparatively limited.In this study,we deployed a TD-HR-AMS(thermodenuder high-resolution aerosol mass spectrometer)system at a rural site in the NCP region in summer 2013 to characterize the chemical compositions and volatility of submicron aerosols(PM_(1)).The average PM_(1)mass concentration was 51.2±48.0μg m^(−3) and organic aerosol(OA)contributed most(35.4%)to PM_(1).Positive matrix factorization(PMF)analysis of OA measurements identified four OA factors,including hydrocarbon-like OA(HOA,accounting for 18.4%),biomass burning OA(BBOA,29.4%),lessoxidized oxygenated OA(LO-OOA,30.8%)and more-oxidized oxygenated OA(MO-OOA,21.4%).The volatility sequence of the OA factors was HOA>BBOA>LO-OOA>MO-OOA,consistent with their oxygen-to-carbon(O:C)ratios.Additionally,the mean concentration of organonitrates(ON)was 1.48−3.39μg m−3,contributing 8.1%-19%of OA based on cross validation of two estimation methods with the high-resolution time-of-flight aerosol mass spectrometer(HRToF-AMS)measurement.Correlation analysis shows that ON were more correlated with BBOA and black carbon emitted from biomass burning but poorly correlated with LO-OOA.Also,volatility analysis for ON further confirmed that particulate ON formation might be closely associated with primary emissions in rural NCP areas.
基金The authors are grateful to the financial support of the National Key Research and Development Program of China (2016YFB0600304)the National Natural Science Foundation of China (No. 51804313).
文摘The harmful trace elements will be released during coal utilization, which can cause environment pollution and further endangering human health, especially for heavy metal elements. Compared to combustion, the release of heavy metal elements during coal pyrolysis process, as a critical initial reaction stage of combustion, has not received sufficient attention. In the present paper, a low rank coal, from Xinjiang province in China, was pyrolyzed in a fixed bed reactor from room temperature, at atmospheric pressure, with the heating rate of 10 °C/min, and the final pyrolysis temperature was from 400 to 800℃ with the interval of 100℃. The volatility of heavy metal elements (including As, Hg, Cd and Pb) during pyrolysis process was investigated. The results showed the volatility of all heavy metal elements increased obviously with increasing temperature, and followed the sequence as Hg > Cd > As > Pb, which was mainly caused by their thermodynamic property and occurrence modes in coal. The occurrence modes of heavy metals were studied by sink-andfloat test and sequential chemical extraction procedure, and it can be found that the heavy metal elements were mainly in the organic and residual states (clay minerals) in the raw coal. And most of the organic heavy metals escaped during the pyrolysis process, the remaining elements were mainly in the residual state, and the elements in Fe-Mn state also tended to remain in the char.
基金supported in part by the National Key R&D Program of China (No.2017YFE0109000)the project of China Datang Corporation Ltd
文摘With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.
基金This work is supported by the National Natural Science Foundation of China(71790594,71701150,and U1811462).
文摘This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.
