GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets

This study proposes a generalized autoregressive conditional heteroskedasticity (GARCH)-mixed data sampling (MIDAS)-generalized autoregressive score (GAS)-copula model to calculate conditional value at risk (CoVaR). Our approach leverages the GARCH-MIDAS model to enhance stock market volatility modeling and incorporates the GAS mechanism to create a copula with dynamic parameters. This approach allows for the precise calculation of both CoVaR and its changes over time (delta CoVaR). The results of our study demonstrate a significant improvement in CoVaR calculation accuracy compared to other models, showcasing the effectiveness of the GARCH-MIDAS-GAS-copula model. In addition, the CoVaR indicator provides a more comprehensive view of risk spillover relationships compared to value at risk (VaR), offering deeper insights into the asymmetrical risk transmission dynamics between the Chinese and US stock markets, providing valuable information for risk management and investment decisions.     

系统性金融风险的度量及其时变经济效应研究

基于TVP-SV-SVAR模型，分析六个不同金融子市场风险对实体经济的实时冲击效应，结合时变脉冲响应方法构建了动态权重系统性金融风险综合指数，并区分高低风险状态探讨其对实体经济的影响。结果表明，银行部门、股票市场和外部金融市场对系统性金融风险的贡献较大；基于对实体经济冲击视角的动态权重系统性金融风险综合指数与样本期内实际金融经济事件的发展趋势一致；不同状态下系统性金融风险对经济增长的冲击效应不同：短期来看，高风险点系统性金融风险抑制经济增长，低风险点系统性金融风险促进经济增长；长期来看，系统性金融风险在高低状态下对经济增长均有负向冲击效应。研究结论对于防范和化解系统性金融风险的宏观审慎政策制定具有重要的参考意义。     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.     

Economic policy uncertainty and industry risk on China’s stock market

We propose a dynamic mixture Copula with time-varying weight, which is endowed with generalized autoregressive score dynamics. Based on this model, we portray the lower-tail dependence between the return of WIND first-level industry and CSI-300 index as a proxy variable for the industry risk in China’s stock market, and use the VAR-GARCH-in-mean model based on BEKK-GARCH to deconstruct the different impact of the economic policy uncertainty (EPU) on industry risk of the first and second moments in terms of four policy categories, namely fiscal policy, monetary policy, trade policy, and foreign exchange rate and capital account policy. The results are followed. Firstly, the risk of Consumer Discretionary is averagely the highest, while the risk of Utilities remains the lowest. Secondly, category-specific EPU has no significant mean spillover to the risk of overall industries, while the variance spillover is significant for all the cases. Thirdly, except for Real Estate, the GARCH-in-mean effect is not significant of EPU on industry risks. Further more, all those three kinds of impact show industrial heterogeneities. To avoid systemic risks, we advise that the issue of economic policy should be forward-looking, consistent, and targeted, especially for sensitive industries.     

Non-Gaussian models for CoVaR estimation

In this paper we show how to obtain estimates of CoVaR based on models that take into consideration some stylized facts about multivariate financial time series of equity log returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is captured by AR-GARCH dynamics of the Glosten-Jagannathan-Runkle (GJR) type, the other stylized facts are explained by non-Gaussian multivariate models and copula functions. We compare the different models in the period from January 2007 to March 2020. Our empirical study conducted on a sample of listed banks in the euro area confirms that, in measuring CoVaR, it is important to capture the time-varying dynamics of the volatility. Additionally, a correct assessment of the heaviness of the tails and of the dependence structure is needed in the evaluation of this systemic risk measure.     

“保险+期货”模式与农户增收——基于全国124个县的多期双重差分估计

目的 “保险+期货”作为我国金融支农的重要创新，对农户实现增收起着关键作用。在农产品价格风险成为农户主要风险的背景下，分析“保险+期货”对农户增收的影响路径和效果具有重要的理论和现实意义。方法 文章基于准自然实验的思路，将“保险+期货”试点视作外部冲击，构建多期双重差分模型，采用2011—2020年中国124个县的面板数据，估计“保险+期货”试点对农户增收的影响路径和效果。结果 （1）“保险+期货”试点对农户具有显著的增收效应，使农村居民人均可支配收入平均提高了2.75%。（2）“保险+期货”试点对农户的增收效应存在异质性，对贫困县农户更为明显。（3）“保险+期货”试点对农户的增收效应是逐年递增的，试点第5年增收效应达到上限。结论 建议稳步扩大“保险+期货”试点、优先在脱贫地区开展“保险+期货”试点，并且积极探索建立收入保险。     

Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions*

It has been claimed that the fall in US inflation during the Great Recession was surprisingly small. One possible explanation for this is that the Phillips curve is unstable and that its slope was lower around the Great Recession. We investigate the importance of time-varying parameters using Bayesian vector autoregressions for inflation and unemployment. We find support for time variation in the inflation equation and an unstable Phillips curve that was somewhat flatter between 2005 and 2013. However, conditional forecasts mostly suggest that inflation was not unexpectedly high around the Great Recession, which puts the claim of a “missing disinflation” into question.     

Do the carry trades respond to geopolitical risks? Evidence from BRICS countries

This paper aims to analyze the implications of geopolitical risks on the return and volatility of carry trade transactions in the context of BRICS countries for the period 2006–2020. Fixed effects regressions considering the sample countries as a single portfolio document that geopolitical risks are correlated with volatility, while the results are inconclusive for returns. The non-parametric time-varying coefficients panel data estimations further indicate that the effect of geopolitical risks on carry trade volatility is amplified during the Global Financial Crisis and the post-2016 episode. Moving to the disaggregated data, the time-varying robust Granger causality test of Rossi and Wang (2019) show that geopolitical risks have a significant in-sample predictive power for both carry trade return and volatility during a myriad of sub-periods, which can not be captured by standard constant parameter techniques in the presence of instabilities. Overall, our empirical results suggest that the exposure to geopolitical risks should be taken into account by global investors for risk diversification purposes when entering carry trade positions in BRICS countries.     

Time-varying connectedness of metropolitan housing markets

We construct a time-varying measure of connectedness for 382 U.S. metropolitan housing markets using monthly house price data from 1975 to the present. Housing connectedness in the long run is found to be much stronger than the instantaneous connectedness, both of which exhibit notable variation over time and across metropolitan areas. Unlike stock market connectedness, housing market connectedness leads the business cycle; it helps predict the likelihood of future recessions.     

A time-varying copula approach for constructing a daily financial systemic stress index

This paper develops a financial systemic stress index (FSSI) for the US financial market. We propose a time-varying copula method to model the dependence structure among financial sectors in order to build a correlated financial stress model that can signal systemic financial risks. The copula method is preferable to the traditional approach, enabling the modeling of non-linear correlations. Our analyses show that the dependencies across banking, security, and forex markets are best modeled by Archimedian copulas. Finally, we conduct a Markov Switching Autoregressive (MS-AR) model for FSSI and identify high financial stress episodes taking place in 2008–2009, 2011 and 2020.