Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

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

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

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Fast Fourier Transform of Multi-Assets Options under Economic Recession Induced Uncertainties

A Fast Fourier transform approach has been presented by Carr & Madan (2009) on a single underlying asset. In this current research paper, we present fast Fourier transform algorithm for the valuation of Multi-asset Options under Economic Recession Induced Uncertainties. The issue of multi-dimension in both finite and infinite case of Options is part of the focus of this research. The notion of economic recession was incorporated. An intuition behind the introduction of recession induced volatility uncertainty is revealed by huge volatility variation during the period of economic recession compared to the period of recession-free. Nigeria economic recession outbreak in 2016 and its effects on the uncertainty of the payoffs of Nigeria Stocks Exchange (NSE) among other investments was among the motivating factors for proposing economic recession induced volatility in options pricing. The application of the proposed Fast Fourier Transform algorithm in handling multi-assets options was shown. A new result on options pricing was achieved and capable of yielding efficient option prices during and out of recession. Numerical results were presented on assets in 3-dimensions as an illustration taking Black Scholes prices as a bench mark for method effectiveness comparison. The key findings of this research paper among other crucial contributions could be seen in computational procedure of options valuation in multi-dimensions and uncertainties in options payoffs under the exposure of economic recession.

Centralized-local PV voltage control considering opportunity constraint of short-term fluctuation

This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.

区域金融服务能力对中国经济增长的影响效应研究

以金融资源、金融效率和金融生态三个维度构建中国省级区域金融服务能力评价指标体系,测算结果表明:2003-2020年中国区域金融服务能力呈现明显上升趋势,东、中、西部地区与全国变化趋势基本一致,东部各省份的金融服务能力整体上高于中部、西部省份。选定的基准模型估计结果表明:2003-2020年区域金融服务能力对中国经济增长存在显著促进作用;三大地区子样本情形的分析也表明区域金融服务能力对其经济增长均存在显著促进作用;两类经济发展水平地区子样本分析表明:相对发达地区金融服务能力对其经济增长的促进作用更为明显,而欠发达地区这种促进作用相对较弱。全国样本情形下的修正模型估计结果表明:当经济系统遭受外生冲击影响时,区域金融服务能力对经济增长仍存在正向促进作用,但提升区域金融服务能力则对中国经济波动存在一定的反向抑制效应。进一步讨论表明:在全国样本和两类经济发展水平地区子样本情形下,区域金融服务能力对经济增长的贡献率基本上呈现出先波动变化再稳定上升的趋势;相较于增加金融资源而言,提高金融效率或改善金融生态手段在抑制外生冲击、平滑经济波动方面更为有效。优化金融生态环境和区域金融布局将为中国经济发展提供进一步支持。

内循环下的国内价值链长度与经济波动

科学剔除中国区域间投入产出表的外循环成分以准确刻画纯国内投入产出关系,基于此测度内循环下的国内价值链(NVC)长度,然后利用中国省市-行业维度面板数据研究其对经济波动的影响,得到的主要结论有:第一,促进内循环下的NVC长度延伸能显著降低中国经济波动水平,其作用机理在于增强产业韧性、激发国内需求及弥补制度环境的不足。第二,当开放度和全球价值链(GVC)分工地位较高时,延长NVC长度对经济波动的平抑作用更为显著,在全面扩大开放的当前时期,中国应借助NVC长度提升与GVC地位攀升的战略协同来稳定经济运行。第三,相比而言,提升制造业以及东部区域各行业NVC长度对减缓经济波动发挥了更为关键的作用。因此,着力延长内循环下的国内价值链长度并合理定位政策举措,是稳定经济的重要手段。

控释掺混肥对小麦–玉米轮作体系产量、氮肥利用效率及氨挥发的影响

【目的】针对华北平原冬小麦-夏玉米轮作体系氮肥施用量大、氮肥利用率低、氮素损失严重等问题,探讨不同配比的控释掺混肥减氮和一次性施肥对周年作物产量、氨挥发损失和施肥经济效益的影响,确立麦玉轮作体系轻简高效和环境友好的施肥技术。【方法】于2022—2023年,在山东省德州市现代农业科技园区开展田间试验。冬小麦和夏玉米均设不施氮对照(CK)、农户习惯施氮(FFP)、优化施氮(OPT)、CRBF1(包膜尿素与普通尿素在冬小麦和夏玉米上的掺混比例分别为5∶5和3∶7)和CRBF2(包膜尿素与普通尿素在冬小麦和夏玉米上的掺混比例分别为7∶3和5∶5),共5个处理。基施和追施肥后,监测了氨挥发量、强度和损失率。分析了作物干物质积累和产量、氮素吸收量和氮肥利用效率,并计算了施肥经济效益。【结果】与FFP处理相比,CRBF1和CRBF2处理的夏玉米、冬小麦和周年产量差异不显著;CRBF1和CRBF2处理夏玉米氮肥偏生产力提高了8.87~9.86 kg/kg,CRBF2处理玉米氮肥回收率提高了48.4%(P<0.05);CRBF1和CRBF2处理冬小麦季氮肥回收率、氮肥农学利用率和氮肥偏生产力分别提高了30.3%~37.2%、5.22~5.77 kg/kg和6.52~7.06 kg/kg(P<0.05)。与FFP和OPT处理相比,CRBF1和CRBF2处理的肥料+施肥人工投入减少381~960元/hm^(2),周年净收入增加1208~2654元/hm^(2)。CRBF1和CRBF2处理的周年氨挥发损失量、损失强度和损失率较FFP处理分别降低了69.7%~71.9%、72.0%~74.5%和90.0%~94.1%(P<0.05)。【结论】包膜尿素与普通尿素在冬小麦、夏玉米季按照5∶5、3∶7,或者按照7∶3、5∶5的比例掺混一次性基施,均可在减少氮肥用量和施肥劳力投入的条件下,确保周年作物产量稳定,提高生产效益,并显著降低氨挥发损失,是华北平原冬小麦-夏玉米轮作系统高产高效、轻简施肥的有效氮肥施用技术。

经济不确定性和地缘政治风险对国内外原油价格的溢出性分析

本文采用多元GARCH模型对条件相关性进行建模,研究2018年3月—2022年6月“一带一路”沿线中东国家油价、国内原油价格、经济政策不确定性和地缘政治风险之间的全球动态关系。实证结果表明,动态条件相关模型拟合数据最好,表明国内外油价与经济不确定性以及地缘政治风险之间存在时变的溢出效应,其中迪拜原油价格与国内原油价格之间的动态条件相关性较高。

宏观经济对股市波动的影响——基于GARCH-MIDAS-RTSRV模型的证据

本文对GARCH-MIDAS模型进行了拓展。首先,在估计GARCH-MIDAS模型的长期波动成分时,采用同时考虑噪声和跳跃影响的稳健双频已实现波动估计量RTSRV来代替传统的已实现波动估计量RV。其次,选取了经济变量并从中提取出主成分,从其水平值和波动率两个层面研究不同主成分对股市波动的影响。研究发现:本文构造的GARCH-MIDAS-RTSRV模型优于传统的GARCH-MIDAS模型,其预测精度更高并且可使投资者获得更高的经济价值;经济变量的主成分和已实现波动率均对股市的波动有显著的影响,并且相较于其水平值,波动率对股市波动的影响更为显著。

全球政治、经济不确定性与黄金价格波动--基于GARCH-MIDAS模型的实证研究

从政治和经济不确定性两个维度入手构建GJR-GARCH-MIDAS模型,探讨全球不确定性对国际黄金价格波动的溢出效应及其传导路径,并对黄金市场的未来价格波动趋势进行考察。(1)全球政治、经济不确定性对黄金价格波动产生显著正向冲击,纳入不确定性因素的双因子GARCH-MIDAS模型对后者的解释效果较佳。(2)不同政治、经济不确定性子要素对黄金市场的冲击强度和冲击时间具有异质性;地缘政治行动和货币政策不确定性是影响黄金价格长期波动的最主要因素。(3)外部不确定性主要通过影响黄金的商品属性、资产属性、准货币属性对黄金市场施加影响,其中“准货币属性”是近年来不确定性冲击影响黄金市场波动的最主要路径。(4)模型预测结果显示,随着全球政治、经济不确定性因素的减弱和反复,黄金价格长期波动成分从2023年第二季度开始,将逐步下移,随后在第四季度有所反弹。

国际能源价格、中国通货膨胀与经济增长的周期波动关系——基于小波变换频带分析与多元BEKK-GARCH(1,1)模型分析

近些年来国际能源价格变动频繁,给各国经济的发展造成很大的不便与影响。文章现使用小波变换频带分析方法并结合多元GARCH-BEKK(1,1)模型,考察处在不同时间段内国际能源价格、通货膨胀与经济增长三者之间的溢出关系。实证结果表明:三者之间有波动溢出效应,但在不同的周期,其波动效应不同,短期来看三者之间存在双向传递效应,随周期变长发展为单向溢出与双向溢出,长期则国际能源价格向通货膨胀波动溢出效应更为显著。伴随中国经济迈入一个新时期,能源问题一定会成为中国未来经济发展需要重视的挑战,因此需要相关政府机构加快完善国内能源市场化建设,提高能源使用效率,努力减缓国际能源价格波动对中国经济的负面冲击。

Macroeconomic impacts of oil price volatility： mitigation and resilience

Dependency sectors, most notably on oil-derived fuels in various in mobility, has left the global economy vulnerable to several macroeconomic economic side effects. Numerous studies have addressed the effect of price volatility on specific economic parameters. However, the current literature lacks a comprehensive review of the interactions between global macroeconomic performance and oil price volatility （OPV）. Price volatility is intrinsic in commodity markets, but has been advancing at a faster rate in the crude oil market in comparison to other commodities over the past decade, reflecting the status ofoil as the most globalised commodity. In this paper, the analytical literature review and analysis of the behavioral responses of macroeconomic agents to OPV shows that such volatility has several damaging and destabilizing macro- economic impacts that will present a fundamental barrier to future sustainable economic growth if left unchecked. To ensure macroeconomic isolation from OPV, a combination of supply and demand-side policies have been recom- mended that can help to mitigate and build resilience to the economic uncertainty advanced by OPV.

引入方差/波动率资产的动态最优投资组合

本文在构建投资组合时,除了包含传统的股票资产和无风险资产,还引入方差/波动率资产.给出了动态最优投资组合的显式解,发现方差/波动率资产价格是影响投资组合权重的重要状态变量.方差/波动率资产价格反映了市场的波动程度,股票资产以及方差/波动率资产的最优权重都会随着方差/波动率资产价格的增加而降低.基于美国市场的实证研究表明,组合中加入VIX期货有助于分散组合风险和提高组合样本外绩效,而组合中不包含VIX期货会导致投资者遭受一定的经济成本.

经济政策不确定性对中国股市波动率的影响

借鉴REGARCH-MIDAS SK模型的建模思路及框架,引入经济政策不确定性指数(CEPU),提出了REGARCH-MIDAS-CEPU SK模型,该模型优势在于可将高频日内数据与低频不确定性指数数据结合,同时考虑股市收益率的多种特性,能够准确分析经济政策不确定性对股市波动的影响机理。采用上证综指收盘价日度数据和经济政策不确定性指数及其细化指数月度数据,利用引入了经济政策不确定性的双因素REGARCH-MIDAS SK(REGARCH-MIDAS-CEPU SK)模型,实证分析总体和细化经济政策不确定性对股市波动率的影响作用。实证结果表明:提出的双因素模型在样本内拟合与样本外预测实证中,其性能均优于现有双因素模型,说明该模型的提出具有显著的理论与实践意义。此外,研究证明各类经济政策不确定性水平增加均会促进股票市场波动,其中货币政策和贸易政策不确定性对股票波动长期趋势影响较大,贸易政策不确定性对股市波动率的影响持续时间较长,股市波动对财政政策以及外汇和资本账户政策不确定性变动较为敏感,由此可见四类经济政策不确定性对股市波动的影响机理各不相同。

经济政策不确定性与短期利率波动——基于BHK-L-MIDAS模型的实证研究

本文在BHK模型的基础上,考虑短期利率波动杠杆效应和EPU对短期利率波动的影响,构建了一个包含杠杆效应和EPU的混频短期利率模型,即BHK-L-MIDAS模型对短期利率波动进行建模与预测。采用上海银行间同业拆借利率(SHIBOR)和中国EPU指数数据对构建的BHK-L-MIDAS模型进行实证分析,结果表明:短期利率波动存在“反向杠杆效应”;EPU对短期利率波动具有显著负向的影响;BHK-L-MIDAS模型相较于竞争模型(BHK和BHK-MIDAS模型)获得了更好的样本内数据拟合效果。基于三种损失函数以及模型置信集(MCS)检验对模型样本外短期利率波动预测能力的分析表明:BHK-L-MIDAS模型相比BHK模型和BHK-MIDAS模型具有更高的样本外预测精度,且BHK-L-MIDAS模型在不同预测窗口表现出的预测能力具有稳健性。最后,VaR分析表明BHK-L-MIDAS模型在短期利率市场风险度量方面的经济价值。

经济政策不确定性与中国股市波动率预测——在GARCH-MIDAS模型下的实证研究

文章以中国经济政策不确定性指数(CEPU)、美国经济政策不确定性指数(AEPU)和全球经济政策不确定性指数(GEPU),上证综合指数5分钟高频数据和日收益率为研究对象,运用GARCH-MIDAS模型分析不同经济政策不确定性对上证综合指数波动率的影响。实验结果表明,研究结果发现:GARCH-MIDAS模型比GARCH模型可以更好地拟合我国股市的波动状况,同时加入EPU指数能提升模型的预测性能。此外,通过不同的预测窗口和DM检验结果发现,CEPU对中国股票市场波动影响最大,说明本国经济政策是中国股票市场长期波动的主因。外国经济政策虽然会对本国股票市场波动产生影响,但其影响的程度远没有本国的经济政策对股市影响的效果强烈。

经济政策不确定性对农产品价格的时变冲击效应研究——基于TVP-VAR模型

农产品价格波动对国民经济的稳定发展有重要影响,本文基于2011年1月至2020年12月的粮食类、畜肉类和鲜菜类农产品价格数据和中国经济政策不确定性指数,通过构建时变参数向量自回归(TVP-VAR)模型研究经济政策不确定性对农产品价格的影响。实证结果表明:在面对经济政策不确定性冲击时农产品价格波动具有时变特征,呈现出正负交替的走势,而且该影响存在品种、时期和地区上的差异。第一,畜肉类和鲜菜类农产品价格波动幅度明显大于粮食类农产品;第二,随着滞后期的不断增加,经济政策不确定性冲击对农产品价格的影响越来越小;第三,由于不确定性事件的性质不同,2011年欧洲债务危机、2015年股市危机和2020年新冠疫情三个时期内经济政策不确定性对农产品价格的影响具有明显差异;第四,经济政策不确定性对农村农产品价格的影响大于对城市农产品价格的影响。基于上述结论,本文提出稳定经济政策、针对不同种类农产品制定差异化政策、利用金融工具稳定价格和健全价格预警机制等政策建议。