Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.     

Natural gas volatility predictability in a data-rich world

This study employs macroeconomic variables and economic indices to forecast natural gas volatility. The out-of-sample results show that the forecasting performance of the macroeconomic variables outperforms the economic indices. Additionally, the forecasting performance of the mixed data sampling model, which combines the least absolute contraction and the selection operator (MIDAS-LASSO), is better than that of other competing models, and it still has a good predictive ability under certain conditions (e.g., business cycles). Our study confirms the superiority of the MIDAS-LASSO model for natural gas volatility forecasting.     

‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables

Aggregate stock return volatility is both persistent and countercyclical. This paper tests whether it is possible to improve volatility forecasts at monthly and quarterly horizons by conditioning on additional macroeconomic variables. I find that several variables related to macroeconomic uncertainty, time-varying expected stock returns, and credit conditions Granger cause volatility. It is more difficult to find evidence that forecasts exploiting macroeconomic variables outperform a univariate benchmark out-of-sample. The most successful approaches involve simple combinations of individual forecasts. Predictive power associated with macroeconomic variables appears to concentrate around the onset of recessions.     

Macroeconomic risks and REITs returns: A comparative analysis

We study the relationship between the excess returns of REITs and volatilities of macroeconomic factors in developing markets (Bulgaria and South Africa) and a ‘benchmark’ developed market (USA). As expected, our results generally indicate that conditional volatilities of macroeconomic risks, extracted through the GARCH (1,1) process, are time-varying. GARCH coefficients are largely significant for excess returns and retained principal components implying conditional time-varying volatility. We use the GMM to examine the linkage between volatilities of macroeconomic variables and REITs returns. The general result here is that macroeconomic risk cannot explain excess returns on REITs. However, we document a positive relationship between variability in REITs returns and the real economy for the US. US REITs portfolio managers and investors should be wary of fluctuations in these variables as they may accentuate volatility in REITs returns.     

Macroeconomic news, announcements, and stock market jump intensity dynamics

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time-varying jump intensity, which is allowed to respond to such information. The day of the announcement, per se, is found to have little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks reveal only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence on macroeconomic variables relevance in explaining jump dynamics and improving volatility forecasts on event days is provided.     

Predictive models for disaggregate stock market volatility

This paper incorporates macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (J Econom 138(1):291–311, 2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, make better predictions than the two benchmark models. Our results show that default return and default yield have a significant impact on stock return volatility.     

Linear-quadratic term structure models – Toward the understanding of jumps in interest rates

We study linear-quadratic term structure models with random jumps in the short rate process where the jump arrival rate follows a stochastic process. Empirical results based on the US data show that incorporating stochastic jump intensity significantly improves model fit to the dynamics of both interest rate and volatility term structure. Our results also show that jump intensity is negatively correlated with interest rate changes and the average size is larger on the downside than upside. Examining the relation between jump intensity and macroeconomic shocks, we find that at monthly frequency, jumps are neither triggered by nor predictive of changes in macroeconomic variables. At daily frequency, however, we document interesting patterns for jumps associated with information shocks.     

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.     

Distribution forecast targeting in an open-economy,macroeconomic volatility and financial implications

In an open-economy faced with parameter uncertainty, this paper uses distribution forecasts to investigate the impact of alternative inflation targeting policies on macroeconomic volatility and their potential implications on financial stability. Theoretically, Domestic Inflation Targeting (DIT) leads to less volatility than Consumer Price Index Inflation Targeting (CPIIT) for several macroeconomic variables and, in particular, for the interest rate. Empirically, a positive relationship between interest rate volatility and financial instability emerges for the US, UK and Sweden since the early 1990s. Bridging theory and empirical evidence, we conclude that the choice of the inflation targeting regime has an important impact on macroeconomic volatility and potential implications for financial stability.     

Volatility in an era of reduced uncertainty: Lessons from Pax Britannica

Although it has been well established that financial volatility is related to news and macroeconomic shocks, less emphasis has been placed on the importance of underlying economic and political stability. In this paper we study the behavior of consol returns since 1729 and identify a greater-than-50% decline in volatility from the end of the Napoleonic Wars in 1815 until the First World War. News events and macroeconomic variables cannot account for this extended period of reduced volatility. Underlying political stability under Pax Britannica seems to be a more likely explanation.     

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.     

Central bank communication and exchange rate volatility: a GARCH analysis

We examine the effects of the Czech National Bank communication, macroeconomic news and interest rate differential on exchange rate volatility using generalized autoregressive conditional heteroscedasticity model. Our results suggest that central bank communication has a calming effect on exchange rate volatility. The timing of central bank communication seems to matter, too, as financial markets respond more to the communication before the policy meetings than after them. Next, macroeconomic news releases are found to reduce exchange rate volatility, while interest rate differential seems to increase it.     

Measuring and explaining the volatility of capital flows to emerging countries

This paper analyzes the determinants of the volatility of the various types of capital inflows into emerging countries. After calculating a proxy of the volatility of FDI, portfolio and bank inflows, we use a panel data model to study their relationship with a broad set of explanatory variables. Our results highlight the difficulties policy-makers face in stabilizing capital flows. Thus, we show that since 2000 global factors beyond the control of emerging economies have become increasingly significant relative to country-specific drivers. However, we identify some domestic macroeconomic and financial factors that appear to reduce the volatility of certain capital flows without increasing that of others.     

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.     

Finance and consumption volatility: Evidence from India

The main objective of this paper is to explore the determinants of private consumption growth volatility in India, focusing on the role of financial sector policies. Using data for India over the period 1950-2005, the results show that the implementation of financial repressionist policies is strongly associated with lower consumption volatility. The results remain robust after controlling for a wide range of macroeconomic shocks and variables. The presence of a threshold effect implies that the benefits of financial reforms in reducing consumption volatility can only be reaped when the financial system becomes sufficiently liberalized. The results also indicate that the presence of a more open financial system may serve to dampen fluctuations in private consumption.     

Treasury Bond Volatility and Uncertainty about Monetary Policy

We show that dispersion‐based uncertainty about the future course of monetary policy is the single most important determinant of Treasury bond volatility across all maturities. The link between Treasury bond volatility and uncertainty about macroeconomic variables is much stronger than for the more traditional time series measures of macroeconomic volatility and adds beyond the information contained in lagged bond market volatility. Uncertainty about monetary policy subsumes the uncertainty about future inflation (consumer price index and the deflator) and economic activity (unemployment, real and nominal gross domestic product and industrial production). In addition, causality clearly runs one way: from monetary policy uncertainty to Treasury bond volatility.     

Scheduled domestic and US macroeconomic news and stock valuation in Europe

This paper investigates whether investors on European stock markets regard news announcements about domestic and US macroeconomic variables as an important source of information when valuing stocks. To assess the importance of scheduled domestic and US macroeconomic news announcements, implied volatilities are analyzed on the German and Finnish stock markets. The results show that the US employment report and the Federal Open Market Committee (FOMC) meeting days have a significant impact on implied volatility on both European markets. The domestic news announcements have no effect on implied volatility on either of the markets. The results indicate that the US macroeconomic news announcements are valuable sources of information on European stock markets while domestic news releases seem to be unimportant.     

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。     

Modelling sovereign credit spreads with international macro-factors: The case of Brazil 1998–2009

This paper develops a macro-finance model of the Brazilian economy and its sovereign debt markets that allows for domestic and international macroeconomic influences as well as swings in investor confidence. It finds significant evidence of common trends in the US and Brazilian economies and bond markets as well as spillover effects from US inflation and business cycles to the Brazilian economy. The US Fed Funds rate influences Brazilian sovereign spreads, as do Brazilian inflation and policy rates. The Brazilian confidence factor dominates the behavior of the spreads during periods of crisis and we find that it also has a powerful effect on the level and volatility of macroeconomic variables. These results suggest that the macro-finance approach could throw light upon the behavior of other economies that are troubled by sovereign risk.     

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.