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.     

Macroeconomic attention,economic policy uncertainty,and stock volatility predictability

This study adopts the newly constructed macroeconomic attention indices (MAI) and category-specific economic policy uncertainty (EPU) indices to predict stock volatility. Principal component analysis (PCA), scaled PCA (sPCA), and partial least squares (PLS) are used to extract the principal components from indicators. The results show that the combination of MAI and EPU indices can obtain additional information for predicting stock market volatility. In addition, the comprehensive index containing all indicator information (F_t^All) has the strongest short-term forecasting ability, whereas the MAI show the most substantial forecasting ability in long-term forecasting.     

Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China

This study explores the spillovers between economic policy uncertainty (EPU) and stock market realized volatility (RV). The monthly index of Chinese and US EPU and RV are used to analyze the pairwise directional spillovers. We find that RV is a net receiver that is more vulnerable to shocks from U.S. EPU than to shocks from Chinese EPU. We further decompose the RV into good and bad volatility to test the asymmetric spillover effect between the stock market and EPU. The results suggest that EPU has a bigger effect on bad volatility in the stock market throughout most of the sample period. However, we find that good volatility spillovers become larger during periods of stimulated reform, whereas bad volatility spillovers become larger during periods of international disputes. We show that Chinese stock market volatility is sensitive to both U.S. and Chinese EPU and that the spillover is asymmetric in different periods.     

Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach

In this study, we employ the GARCH–MIDAS (Generalised Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling) model to investigate the response of stock market volatility of the BRICS group of countries (Brazil, Russia, India, China, and South Africa) to oil shocks. We utilise the recent datasets of Baumeister & Hamilton (2019), where oil shocks are decomposed into four variants: oil supply shocks, economic activity shocks, oil consumption shocks, and oil inventory shocks. We further decompose each of these shocks into positive and negative shocks, and our findings show heterogeneous response of stock market volatility of the BRICS countries to the alternative oil shocks, including positive and negative shocks. The differing responses across the BRICS countries could be attributed to differences in the economic size, oil production, and consumption profile of the countries, market share distribution across firms, and financial system and regulation efficiency.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Range-based multivariate volatility model with double smooth transition in conditional correlation

This paper proposes a multivariate model named Double Smooth Transition Conditional Correlation Conditional Autoregressive Range (DSTCC-CARR for short). Determined by two transition variables, the correlations smoothly transit from one state to another. Together with the DSTCC-GARCH model, the model is employed to investigate the interdependence between Hong Kong's and international stock markets. It is proved by the empirical analysis that the DSTCC-CARR model is more credible and efficient than the DSTCC-GARCH model. Linkages among Hong Kong's and other world's markets captured by these two models are testified to be consistent with history, and have meaningful interpretations.     

Comparing the impact of Chinese and U.S. economic policy uncertainty on the volatility of major global stock markets

This study compares the impact of Chinese and U.S. economic policy uncertainty (EPU) (proxied by the EPU index) on the volatility of 11 major stock markets. Unlike previous research that only utilizes monthly EPU for such a comparison, this study uses both daily and monthly data to examine the impact within a month as well as over months. In order to provide a detailed analysis, EPU shocks are investigated from a two-sided viewpoint: one considering the effects of EPU indices as exogenous shocks, and the other examining the spillovers from EPU indices as endogenous variables. Meanwhile, the role of global turmoil, such as the 2007–2008 global financial crisis (GFC) and the COVID-19 pandemic, in influencing the impact of Chinese (or U.S.) EPU is highlighted. The results show that the impact of U.S. EPU is reinforced at both daily and monthly frequencies during the GFC, with a greater effect on the European stock markets. After the GFC, the rising influence of Chinese EPU is observed at a monthly frequency in several markets in Asia and elsewhere. Overall, the dynamic spillovers from the EPU indices to stock volatility suggest the dominant role of U.S. EPU in most markets at a daily frequency, while the extent of the spillovers is driven by turbulent events, including the GFC and the COVID-19 pandemic.     

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.     

Asymmetric volatility spillover between oil-importing and oil-exporting countries' economic policy uncertainty and China's energy sector

While the relationship between economic policy uncertainty(EPU) and energy market is of great interest to economist, previous research dose not differentiate the effect from oil-importing countries to oil-exporting countries' EPU on the a country's energy sector. In this paper, we address this issue by testing the effect of importer and exporter's EPU on the largest oil-importing country, China, as oil-importing affected greatly by the economic policy. TVP-FAVAR model is applied to obtain the factors and time-varying coefficients of 21 countries' EPU monthly indexes and energy stock realized volatility. We find that the Chinese energy sector's stock volatility is positively related to EPU shocks and that bad volatility has a stronger impact than good volatility. Second, the volatility spillover from oil-exporting countries' EPU on the Chinese energy sector is stronger than that from oil-importing countries' EPU, with a stronger effect for bad volatility than for good volatility. Finally, The bad volatility spillover and spillover asymmetry is stronger during the crisis periods, such as the debt crisis, energy contention, oil price turbulence, or limited production agreement, both symmetric and asymmetric spillovers increase. Our findings have potentially important implications for the regulators and investors on Chinese oil market with different types of countries' EPU.     

Insurance and economic policy uncertainty

Just as the world has witnessed the increased importance of the insurance sector over the past few decades, it has also witnessed a sharp rise in risks and uncertainties. Surprisingly, studies analyzing the relationship between economic policy uncertainty and the insurance sector are almost non-existent. Another major limitation of insurance literature is the choice of methodology. Most studies on the insurance sector do not take into consideration issues of heterogeneity and cross-sectional dependence, and are therefore subject to errors. To address the identified gaps, this research investigates the impact of economic policy uncertainty on insurance premiums in a panel of 15 countries over the period 1998–2016 by employing heterogeneous panel estimation techniques with cross-sectional dependence. The Durbin-Hausman cointegration tests of Westerlund (2008) confirm that a long-run relationship exists between the variables. Findings from the error correction based panel estimations show that the insurance sector is not immune to the effects of economic policy uncertainty. Economic policy uncertainty raises insurance premiums both in the short and long run, although the long-run impact is greater than the short-run impact. In addition, economic policy uncertainty exerts a bigger influence on non-life insurance premium than on life insurance premium, indicating that the economic risks covered by non-life insurance are more sensitive to uncertainty than the mortality and longevity risks covered by life insurance. Our findings further show that national income, education, population, financial development and institutional quality all raise insurance premiums, while inflation lowers insurance premiums.     

The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach

In this study, we examine the role of global economic conditions in the predictability of gold market volatility using alternative measures. Based on the available data frequency for the relevant series, we adopt the GARCH-MIDAS approach which allows for mixed-data frequencies. We find that global economic conditions contribute significantly to gold market volatility, albeit with mixed outcomes. While the results also lend support to the safe-haven properties of the gold market, the outcome can be influenced by the choice of measure for global economic conditions. For completeness, we extend the analyses to other precious metals (palladium, platinum, rhodium and silver) and find that the global economic conditions forecast the return volatility of the gold market better than these other precious metals. Our results are robust to multiple forecast horizons and offer useful insights on the plausible investment choices in the precious metals market.     

Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index

In this paper, we aim to improve the predictability of aggregate stock market volatility with industry volatilities. The empirical results show that individual industry volatilities can provide useful predictive information, while the predictive contribution is limited. We further consider the spillover index between industry volatilities and find it displays strong predictive power for stock market volatility. Based on the portfolio exercise, we find that a mean-variance investor can achieve sizeable economic gains by using volatility forecasts of the spillover index. In addition, we conduct three extended analyses and further demonstrate the superior performance of the spillover index. Also, our results show robustness to a series of alternative settings. Finally, we investigate why the spillover index performs better and answer what information it contains. The results show that the spillover index can reflect and explain investor sentiments that are related to stock market volatility.     

Economic policy uncertainty and volatility of treasury futures

Review of Derivatives Research - This paper investigates the relation between Treasury futures market volatility and economic policy uncertainty using GARCH-MIDAS. We formulated models with the...     

Dividends and economic policy uncertainty: International evidence

We provide the first international evidence on the impact of economic policy uncertainty (EPU) on dividend policy. Using data from 19 countries, we find that a high level of EPU is positively associated with dividend payout. This evidence is robust to using alternative dividend payout measures, to controlling for other sources of uncertainty, and to addressing endogeneity. We further find that the effect of EPU on dividend policy is moderated by firms' free cash flows and governance quality, and by the quality of country-level indicators of shareholder protection, disclosure, enforcement, and creditor protection. Collectively, our novel evidence suggests that dividends help mitigate agency problems during high-EPU periods.     

Dynamic and frequency-domain spillover among economic policy uncertainty,stock and housing markets in China

This study examines the dynamic characteristics of information spillover effect among economic policy uncertainty (EPU), stock and housing markets in China's first-, second- and third-tier cities. To measure return and volatility spillovers over time and across frequencies simultaneously, the researchers utilize the time-frequency connectedness network approach developed by Baruník and Křehlík (2018). The empirical findings suggest that return and volatility spillovers are stronger in the longer period (more than 3 months) than in the shorter period (1 to 3 months). In the short term, second and third-tier cities are net transmitters of information spillovers, while in the long term, first-tier cities, EPU, and stock markets are the net information transmitters. Furthermore, the long-term information from the EPU and stock market affect most of the real estate markets for different tier cities. Additionally, market segmentation reveals the city-specific characteristics of China's real estate market, especially the close connections between first-tier cities and the stock market. These results have important empirical implications for real estate policymakers and investors when they make related short or long-term decisions.     

Economic policy uncertainty and stock price crash risk

We examine the impact of economic policy uncertainty (EPU) on firm-specific crash risk. Based on a large sample of Chinese listed firms over the period from 2000 to 2017, we provide empirical evidence that firms are more likely to experience stock price crashes when EPU increases. Cross-sectionally analysis further reveals that the impact of EPU on stock price crash risk is stronger for firms whose returns are more sensitive to EPU. More specifically, young stocks, small stocks, high volatility stocks, and growth stocks, which have higher valuation uncertainty per se, are more sensitive to EPU and are more affected by EPU in terms of crash risk. We further show that EPU is significantly and positively associated with aggregated stock price crash risk at the market level.     

Economic policy uncertainty and stock price crash risk

This paper studies the impact of economic policy uncertainty on stock price crash risk using data from China. We develop a new index to measure Chinese economic policy uncertainty and find that economic policy uncertainty has a remarkable positive effect on stock price crash risk. However, the effect reverses later. The results also indicate that the positive effect of economic policy uncertainty on stock price crash risk is more prominent for state‐owned enterprises. Moreover, this effect is more prominent for firms with higher information asymmetry and firms with greater disagreement among investors, indicating that economic policy uncertainty affects crash risk through two mechanisms: managers’ concealment of bad news and investors’ heterogeneous beliefs.     

Forecasting stock price with the residual income model

The main purpose of this paper is to demonstrate a method to forecast stock price using analyst earnings forecasts as essential signals of firm valuation. The demonstrated method is based on the residual income model (RIM), with adjustment for autocorrelation. Over the past decade, the RIM has been widely accepted as a theoretical framework for equity valuation based on fundamental information from financial reports. This paper shows how to implement the RIM for forecasting and how to address autocorrelation to improve forecast accuracy. Overall, this paper provides a method to forecast stock price that blends fundamental data with mechanical analyses of past time series.     

Economic policy uncertainty and environmental governance company volatility: Evidence from China

This study primarily investigates whether China’s economic policy uncertainty (EPU) can predict the environmental governance index volatility, which selects companies regarding environmental protection such as sewage treatment, solid waste treatment, air treatment, and energy saving. Empirical results reveal that China’s EPU index can predict the environmental governance index volatility. Furthermore, even during periods of fluctuating volatility and the COVID-19 pandemic, China’s EPU index can reliably forecast the environmental governance index volatility. This paper tries to provide new evidence regarding the connection between EPU and environmental governance companies’ stock volatility.