Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model

Given that policy uncertainty shocks in the economic environment can exacerbate financial market volatility and pose financial risks, this paper utilizes a smooth transition version of the GARCH-MIDAS model to investigate the impact of different structural state changes in economic policy uncertainty (EPU) on stock market volatility. The extended model explains the nonlinear effects of the macro variables and the structural break changes in regime transitions. The empirical results confirm that the EPU indicators provide effective prediction information for stock volatility from the in-sample and out-of-sample analyses, which reveals that the smooth transition model provides an effective method for detecting the possible regime changes between stock volatility and macroeconomic uncertainty. Additionally, we further confirm that some category-specific EPU indicators also have strong smooth transition behaviour with respect to stock volatility. More important, our new model provides significant economic value to investors from a utility gain perspective. Overall, the institutional changes present in EPU play a nonnegligible and important role in stock market volatility. Accurate identification of the structural features of financial data helps investors deepen their understanding of the sources of stock market volatility.     

Macroeconomic fundamentals,jump dynamics and expected volatility

In this paper, we develop a new volatility model capturing the effects of macroeconomic variables and jump dynamics on the stock volatility. The proposed GARCH-Jump-MIDAS model is applied to the S&P 500 index. Our in-sample results indicate that macroeconomic activities have important impacts on aggregate market volatility. Out-of-sample evidence suggests that our model with macroeconomic variables significantly outperform a wide range of competitors including the original GARCH(1,1), GARCH-MIDAS and GJR-A-MIDAS models. The volatility timing results also show that the information from jumps and macroeconomic activity is helpful for improving the portfolio performance.     

Procyclical volatility in Chinese stock markets

Review of Quantitative Finance and Accounting - We investigate the macroeconomic determinants of stock market volatility in China using the two-component GARCH-MIDAS model of Engle et al....     

China,Japan and the US Stock Markets and the Global Financial Crisis

In this paper, while focusing on the impact that the global financial crisis had on the stock markets of China, Japan, and the United States, the stock-price volatilities and linkage between these three countries are analyzed. In addition, the relationships between macroeconomic variables (real-economy variables and monetary-policy variables) and stock price volatility in each country are investigated. The estimation results of the EGARCH model revealed that although China’s stock price volatility was far greater than those of Japanese and US stock prices, China was less affected by the global financial crisis in 2007 than Japan and the United States. For China, stock price volatility was greater in the early 1990s, shortly after the stock market had been established, than in 2007 when the global financial crisis occurred. Furthermore, it has been revealed that the linkage of Chinese, Japanese, and US stock prices has increased since the global financial crisis. Moreover, Granger causality testing revealed China’s real-economy variables and monetary-policy variables do not affect China’s stock price volatility.     

The determinants of the model-free positive and negative volatilities

In this paper we analyze the role of macroeconomic and financial determinants in explaining stock market volatilities in the U.S. market. Both implied and realized volatility are computed model-free and decomposed into positive and negative components, thereby allowing us to compute directional volatility risk premia. We capture the behaviour of each component of implied volatility and risk premium in relation to their different determinants. The negative implied volatility appears to be linked more towards financial conditions variables such as uncertainty and geopolitical risk indexes, whereas positive implied volatility is driven more by macro variables such as inflation and GDP. There is a clear shift in importance from macro towards financial determinants moving from the pre towards the post financial crisis. A mixed frequency Granger causality approach uncovers causality relationships between volatilities and risk premia and macro variables and vice versa, a finding which is not detected with a conventional low frequency VAR model.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.     

Smooth transition patterns in the realized stock–bond correlation

This paper explores the time variation in the stock–bond correlation using high-frequency data. Gradual transitions between regimes of negative and positive stock–bond correlation are well accommodated by the smooth transition regression (STR) model. We find that the regimes are systematically related to movements in financial and to a minor extent macroeconomic transition variables. In particular, the most informative transition variables are the short rate, the yield spread, and the VIX volatility index. Importantly, both in-sample and out-of-sample evaluation criteria show that multiple transition variable STR specifications considerably outperform single transition variable STR models. Our results are robust to different forecast horizons.     

The volatility of consumption-based stochastic discount factors and economic cycles

This paper aims to assess the macroeconomic and financial impact of economic uncertainty using information contained in the second moments of financial risk factors employed in the asset pricing literature. Specifically, we propose the volatility of consumption-based stochastic discount factors (SDFs) as a predictor of future economic and stock market cycles. We employ both contemporaneous and ultimate consumption risk specifications with durable and non-durable consumption. Alternative empirical tests show that this volatility has significant forecasting ability from 1985 to 2006. The degree of predictability tends to dominate that shown by standard predictor variables. We argue that the significant predictability of the volatility of consumption-based SDFs reported in this paper relies mainly on the joint effect of their components.     

Determinants of asymmetric return comovements of gold and other financial assets

Using conditional time-varying copula models, we characterize the dependence structure of return comovements of gold and other financial assets (stocks, bonds, real estate and oil) during economic expansion and contraction regimes. We also investigate which key macroeconomic and non-macroeconomic variables significantly impact the asset return comovements using a two stage Markov Switching Stochastic Volatility (MSSV) framework. Our results show that the non-macro variables have significant influence on the return comovements. We find that gold is an inappropriate hedge against interest rate changes for real-estate and oil-based portfolios, while for bond portfolios, gold offers a good hedge against inflation uncertainty. We also provide evidence that the “flight to safety” phenomenon is due to the implied volatility of the stock market, rather than the observed stock market uncertainty. Finally, we forecast the asset return comovements and examine their economic significance. We show that a dynamic MSSV model which includes the macroeconomic and non-macroeconomic variables yields superior forecast of future asset return comovements when compared with a multivariate conditional covariance model.     

波动溢出网络视角的金融风险传染研究

当前各类经济风险交叉关联,金融系统的风险溢出效应备受关注,为刻画我国金融系统性风险传染的路径特征,本文从波动溢出网络的视角分析金融系统内部的风险传染机制。首先使用广义动态因子模型对收益波动的共同波动率成分和特质性波动率成分进行区分。然后,根据货币市场、资本市场、大宗商品交易市场、外汇市场、房地产市场和黄金市场之间的特质性波动溢出效应,利用基于TVP-VAR模型的方差分解溢出指数分析金融系统波动溢出的动态联动性和风险传递机制。在分析方向性波动溢出效应的基础上,采用方差分解网络方法构建起信息溢出复杂网络,从网络视角分析金融系统内部的风险传染特征。实证研究发现,房地产市场和外汇市场的净溢出效应绝对值相较于其他市场更大,其受其他市场风险冲击的影响强于对外风险溢出效应,而股票市场的单向对外风险溢出效应强度最大。在波动溢出的基础上,进一步考虑股市波动率指数与其他市场波动率指数进行投资组合的资产配置权重,计算了波动率指数投资组合的最优组合权重和对冲策略。研究结论有助于更好地理解我国金融系统的风险传染机制,对监管机构加强宏观审慎监管、投资者规避投资风险具有重要意义。     

Stock market integration between the UK and the US: Evidence over eight decades

This study investigates how the impact made on stock market integration by macroeconomic determinants such as various measures of convergence and financial volatility, as well as crisis episodes, varies over the period 1935–2015. We gauge how the level of integration between the UK and US stock markets changes across three monetary regimes during this period: pre–Bretton Woods (BW), the BW fixed exchange rate, and the post-BW flexible rates. Our empirical results suggest that integration was strongest under the post-BW regime and weakest under the BW regime. We further demonstrate that stock market integration between the two markets has been driven largely by macroeconomic convergence and financial volatility as well as by crises, especially since the demise of the BW system.     

宏观经济统计数据公布对中国金融市场影响的实证研究

本文分别运用无市场预期和引入市场预期之后的GARCH模型,研究消费者物价指数、固定资产投资增速、消费品零售总额增速、贸易顺差额以及货币供应量这五个宏观经济数据的定期公布对于我国股票市场、债券市场及外汇市场波动的影响。我们发现在股票市场,CPI统计数据的公布加大了日收益率的波动率,而其它经济数据的公布减小了其波动率;债券市场和外汇市场由于市场化程度较低,宏观经济统计数据的公布对其价格行为的影响较小。     

Dynamic and asymmetric impacts of macroeconomic fundamentals on an integrated stock market

A large body of evidence indicates that macroeconomic and financial variables are dynamically interrelated. In an international setup, we analyze the transmission mechanisms of macroeconomic shocks on the stock market of a small open economy in an increasingly integrated world. We use a time-varying vector error correction model (VECM) that allows analysis of asymmetric impacts that depend on the state of the business cycle. A special focus is directed on monetary policy surprises, where we find that foreign shocks exert a strong influence on an integrated stock market, and that the stage of the business cycle heavily affects the signals of the shocks.     

中国股市行业风险与宏观经济之间的风险传导机制

本文采用行业收益率溢出指数度量股市行业风险,并进一步研究中国股市行业风险与宏观经济的相互影响,同时引入股息率和利率两个中介渠道深入挖掘其传导机制。我们运用GARCH-in-Mean模型对股市行业风险和宏观经济变量之间的一阶矩和二阶矩相互关系同时进行分析,结果发现,股市行业风险和宏观经济变量之间水平值和波动率都存在双向影响,对外溢出效应较大的行业起主导作用。此外,股市行业风险对宏观经济变量的影响方面,股息率和利率均起到中介渠道作用;宏观经济变量对股市行业风险的影响方面,只是利率起到中介渠道作用。股市行业风险与宏观经济的传导效应在不同时期差异显著。本文研究结论有助于深刻理解金融与实体经济之间的风险传导机制,对防范系统性风险、防止金融和实体经济“风险共振”以及提升金融服务实体经济能力等具有参考意义。     

中国股市行业风险与宏观经济之间的风险传导机制

本文采用行业收益率溢出指数度量股市行业风险,并进一步研究中国股市行业风险与宏观经济的相互影响,同时引入股息率和利率两个中介渠道深入挖掘其传导机制。我们运用GARCH-in-Mean模型对股市行业风险和宏观经济变量之间的一阶矩和二阶矩相互关系同时进行分析,结果发现,股市行业风险和宏观经济变量之间水平值和波动率都存在双向影响,对外溢出效应较大的行业起主导作用。此外,股市行业风险对宏观经济变量的影响方面,股息率和利率均起到中介渠道作用;宏观经济变量对股市行业风险的影响方面,只是利率起到中介渠道作用。股市行业风险与宏观经济的传导效应在不同时期差异显著。本文研究结论有助于深刻理解金融与实体经济之间的风险传导机制,对防范系统性风险、防止金融和实体经济“风险共振”以及提升金融服务实体经济能力等具有参考意义。     

Financial turbulence,systemic risk and the predictability of stock market volatility

This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility using a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective on market conditions, as they relate directly to portfolio performance metrics from both volatility and co-movement perspectives and, unlike other macro-financial indicators of uncertainty, or risk, can be integrated into diversification models within forecasting and portfolio design settings. Since the data for the two predictors are available at a weekly frequency, and our focus is to produce forecasts at the daily frequency, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models over both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared with the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also in correlation patterns play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes, in that financial indicators, which are directly associated with portfolio diversification performance metrics, can also be utilized for forecasting purposes, with significant implications for dynamic portfolio allocation strategies.     

Asset price volatility,price markups,and macroeconomic fluctuations

A variant of the neoclassical growth model is considered to study the role of innovation, lags in technology adoption, total factor productivity TFP, and price markups as main determinants of asset price volatility. The model confers a prominent role to price markups as opposed to other macroeconomic sources of uncertainty. In the data, price markups are highly correlated with stock market values, whereas other financial measures of profitability exhibit much less volatility and are weakly correlated with stock market values.     

Macroeconomic Determinants of European Stock Market Volatility

In this paper we investigate whether macroeconomic variability can explain time variation in European stock market volatility. We find that unlike the documented case of the USA, in many cases, the time variation in stock market volatility is found to be significantly affected by the past variability of either monetary or real macroeconomic factors. Our findings have important implications for capital and portfolio allocations.     

What drives U.S. financial sector volatility? A Bayesian model averaging perspective

We investigate the driving forces behind the quarterly stock price volatility of firms in the U.S. financial sector over the period from 1990 to 2017. The driving forces represent a set of 28 economic indicators that are routinely used to detect financial instability and crises and correspond to the development of the financial, monetary, real, trade and fiscal sector as well as to the development of the bond and equity markets. The dimensionality and model choice uncertainty are addressed using Bayesian model averaging, which led to the identification of only seven variables that tend to systematically drive the stock price volatility of financial firms in the U.S.: housing prices, short-term interest rates, net national savings, default yield spread, and three credit market variables. We also confirm that our results are not an artefact of volatility associated with market downturns (for negative semi-volatility), as the results are similar even when market volatility is associated with market upsurge (positive semi-volatility). Given the identified drivers, our results provide supporting empirical evidence that dampening credit cycles might lead to decreased volatility in the financial sector.     

机构投资者影响我国股价波动的实证研究

采用 TGARCH 模型对机构投资者与我国股指波动的关系进行研究,实证结果表明：无论是否考虑宏观经济因素对股票市场的影响,机构投资者对我国股票市场波动均产生正向影响。进一步用面板数据模型对机构投资者与上市公司股价波动的关系进行研究,发现机构投资者在不同宏观经济环境下也均未起到稳定上市公司股价波动的作用。