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volatility forecasting in Chinese nonferrous metals futures market

Transactions of Nonferrous Metals Society of China 2017年第5期27卷 1206-1215页

作者： Xue-hong ZHU Hong-wei ZHANG Mei-rui ZHONG School of Business Central South University Changsha 410083 China Institute of Metal Resources Strategy Central South University Changsha 410083 China

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 *** 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 ***,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market ***,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.

关键词： volatility forecasting leverage effect time-varying volatility nonferrous metals futures high-frequency data

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Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

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Journal of Management Science and Engineering 2023年第2期8卷 191-213页

作者： Xu Gong Boqiang Lin School of Management China Institute for Studies in Energy PolicyXiamen UniversityXiamen361005China

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on *** day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR *** estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly *** out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week *** out-of-sample results were robust across three different *** importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

关键词： Day-of-the-weekeffects Structural breaks volatility forecasting Realized volatility Electricitymarket

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forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH‑MIDAS model

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Financial Innovation 2021年第1期7卷 1615-1633页

作者： Jian Liu Ziting Zhang Lizhao Yan Fenghua Wen School of Economics and Management Changsha University of Science and TechnologyChangshaHunanPeople’s Republic of China School of Business Hunan Normal UniversityChangshaHunanPeople’s Republic of China Business School Central South UniverdityChangshaHunanPeople’s Republic of China

This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures *** GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)*** then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type *** empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type *** results also indicate that EPU has noticeable effect on the volatility of EUA ***,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU *** checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures ***,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.

关键词： EUA Economic policy uncertainty GARCH-MIDAS volatility forecasting Futures

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The role of oil futures intraday information on predicting US stock market volatility

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Journal of Management Science and Engineering 2021年第1期6卷 64-74页

作者： Yusui Tang Xiao Xiao M.I.M.Wahab Feng Ma School of Economics&Management Southwest Jiaotong UniversityChengduChina Department of Mechanical and Industrial Engineering Ryerson UniversityTorontoCanada

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR ***-sample results indicate that oil futures intraday information is helpful to increase the ***,compared to the benchmark model,the proposed models improve their predictive ability with the help of oil futures realized *** particular,the multivariate HAR model outperforms the univariate ***,considering the contemporaneous connection is useful to predict the US stock market ***,these findings are consistent across a variety of robust checks.

关键词： volatility forecasting The US stock Market Oil market volatility Realized volatility DCC model

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To jump or not to jump:momentum of jumps in crude oil price volatility prediction

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Financial Innovation 2022年第1期8卷 1647-1677页

作者： Yaojie Zhang Yudong Wang Feng Ma Yu Wei School of Economics and Management Nanjing University of Science and TechnologyXiaolingwei 200Xuanwu DistrictNanjing 210094China School of Economics and Management Southwest Jiaotong UniversityChengduChina School of Finance Yunnan University of Finance and EconomicsKunmingChina

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price *** address this issue,we find a phenomenon,“momentum of jumps”(MoJ),that the predictive ability of the jump component is persistent when forecasting the oil futures market ***,we propose a strategy that allows the predictive model to switch between a benchmark model without jumps and an alternative model with a jump component according to their recent past forecasting *** volatility data are based on the intraday prices of West Texas *** results indicate that this simple strategy significantly outperforms the individual models and a series of competing strategies such as forecast combinations and shrinkage methods.A mean–variance investor who targets a constant Sharpe ratio can realize the highest economic gains using the MoJ-based volatility *** findings survive a wide variety of robustness tests,including different jump measures,alternative volatility measures,various financial markets,and extensive model specifications.

关键词： Oil futures market volatility forecasting Momentum of jumps Model switching Portfolio exercise

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A Study on the volatility of the Bangladesh Stock Market--Based on GARCH Type Models

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Journal of Systems Science and Information 2017年第3期8卷 193-215页

作者： Bhowmik RONI Chao WU Roy Kumar JEWEL Shouyang WANG Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Daffodil International University Jatiya Kabi Kazi Nazrul Islam University

The generalized autoregressive conditional heteroskedasticity(GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant "low-yield associated with high-risk" phenomenon is detected in the crisis period and the "leverage effect" occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity(TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

关键词： Bangladesh stock market volatility forecasting GARCH type models leverage effect

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不同矩属性波动模型对中国股市波动率的预测精度分析

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统计与精算 2010年第5期 550-559页

作者：王鹏魏宇王建琼

金融时间序列的波动性建模经历了从一阶矩到二阶矩直到高阶矩（包含三阶矩和四阶矩）的过程，而对于高阶矩波动模型是否有助于对未来市场的波动率预测这一问题，国内外学术界尚无文献讨论。以上证综指长达七年的每五分钟高频数据样本为... 详细信息

金融时间序列的波动性建模经历了从一阶矩到二阶矩直到高阶矩（包含三阶矩和四阶矩）的过程，而对于高阶矩波动模型是否有助于对未来市场的波动率预测这一问题，国内外学术界尚无文献讨论。以上证综指长达七年的每五分钟高频数据样本为例，通过构建具有不同矩属性的波动模型，计算了中国股票市场波动率的预测值，并利用具有bootstrap特性的SPA检验法，实证检验了不同矩属性波动模型的波动率预测精度差异。实证结果显示：就中国股市而言，四阶矩波动模型能够取得比二阶矩波动模型更优的波动率预测精度，而三阶矩波动模型并未表现出比二阶矩波动模型更强的预测能力；在高阶矩波动模型中包含杠杆效应项并不能提高模型的预测精度。最后提出了在金融风险管理、衍生产品定价等领域引入四阶矩波动模型的研究思路。

关键词：波动预测高阶矩波动模型 GARCH族模型 SPA检验 volatility forecasting higher moments volatility models GARCH family model SPA test

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中国金融市场波动率建模及其预测研究：基于新的分解方法

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管理科学 2024年第2期

作者：马锋何晓凤鲁心洁

在复杂多变的金融市场环境中，对金融资产波动率准确建模和预测具有极其重要的理论和实践意义。因此，本研究基于多种波动率分解方法，并嵌入结合马尔可夫机制模型，重新构建了多个新的异质自回归已实现波动率模型，进一步以上证50ETF... 详细信息

在复杂多变的金融市场环境中，对金融资产波动率准确建模和预测具有极其重要的理论和实践意义。因此，本研究基于多种波动率分解方法，并嵌入结合马尔可夫机制模型，重新构建了多个新的异质自回归已实现波动率模型，进一步以上证50ETF为研究对象，对比了各模型的预测精度。主要实证结果发现：1）利用模型信度集（model confidence set，MCS）检验，发现预测效果最佳的是结合马尔可夫机制并采用分位数组波动率新构建的模型（MS-PHAR），且各种稳健性检验也支持上述结论；2）在高低波动时期、疫情前后和考虑杠杆效应等不同情形下，新构建的MS-PHAR模型仍具有较好的表现。

关键词：金融风险管理波动率预测 HAR类模型波动率分解方法 financial risk management volatility forecasting HAR-RV-type models volatility decomposition methods

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