Oil price uncertainty as a predictor of stock market volatility

In this paper we empirically examine the impact of oil price uncertainty shocks on US stock market volatility. We define the oil price uncertainty shock as the unanticipated component of oil price fluctuations. We find that our oil price uncertainty factor is the most significant predictor of stock market volatility when compared with various observable oil price and volatility measures commonly used in the literature. Moreover, we find that oil price uncertainty is a common volatility forecasting factor of S&P500 constituents, and it outperforms lagged stock market volatility and the VIX when forecasting volatility for medium and long-term forecasting horizons. Interestingly, when forecasting the volatility of S&P500 constituents, we find that the highest predictive power of oil price uncertainty is for the stocks which belong to the financial sector. Overall, our findings show that financial stability is significantly damaged when the degree of oil price unpredictability rises, while it is relatively immune to observable fluctuations in the oil market

Measuring Oil Price Shocks

The role of oil price shocks in US economic activity and inflation is controversial but a key input to current economic policy. To clarify these relations, we employ a more refined measure of oil shocks based on decomposing highly accurate realized volatility estimated using intraday oil futures data. In reconciling prior results, we find that shocks driven by price increases (decreases) are associated with rising (falling) inflation while only a symmetric volatility channel affects economic activity

Oil Price Uncertainty and the Macroeconomy

This paper examines the impact of oil price uncertainty shocks on economic activity. To do so, we define the uncertainty shock as the unanticipated component of oil price fluctuations. We find that this unanticipated component has a significantly negative and long-lasting impact on economic activity, with its cumulative effect on the US macroeconomy being much larger compared to that of popular uncertainty proxies such as stock market volatility and Economic Policy Uncertainty. Unlike our preferred measure of oil price uncertainty, volatility and the price spikes in oil futures prices present only a small and transitory effect on the real economy. Overall, our findings show that the US economy is significantly impaired when the degree of oil price unpredictability rises, while it is relatively immune to predictable fluctuations in the oil market

The impact of ICT diffusion on sovereign cost of debt

We examine the effect of a country’s level of information and communication technologies (ICT) diffusion on its credit rating and cost of debt. ICT diffusion is approximated using the Networked Readiness Index, which is designed to capture a country’s capacity and preparedness to participate in the digital economy. We adopt a modified random effects approach which allows us to distinguish between short and long run effects on a dataset of 65 countries for a time span of ten years. We show that ICT have a significant impact on a country’s credit rating and cost of debt which is robust to the presence of other variables proposed in the literature. The effect is stronger for non-OECD countries, indicating a pathway for developing countries to improve their access to debt markets. Our conclusions are robust to the advent of the recent financial crisis

Stock Market Volatility and Jumps in Times of Uncertainty

In this paper we examine the predictive power of latent macroeconomic uncertainty on US stock market volatility and jump tail risk. We find that increasing macroeconomic uncertainty predicts a subsequent rise in volatility and price jumps in the US equity market. Our analysis shows that the latent macroeconomic uncertainty measure of Jurado et al. (2015) has the most significant and long-lasting impact on US stock market volatility and jumps in the equity market when compared to the respective impact of the VIX and other popular observable uncertainty proxies. Our study is the first to show that the latent macroeconomic uncertainty factor outperforms the VIX when forecasting volatility and jumps after the 2007 US Great Recession. We additionally find that latent macroeconomic uncertainty is a common forecasting factor of volatility and jumps of the intraday returns of S&P 500 constituents and has higher predictive power on the volatility and jumps of the equities which belong to the financial sector. Overall, our empirical analysis shows that stock market volatility is significantly affected by the rising degree of unpredictability in the macroeconomy, while it is relatively immune to shocks in observable uncertainty proxies

Information demand and stock return predictability

Recent theoretical work suggests that signs of asset returns are predictable given that their volatilities are. This paper investigates this conjecture using information demand, approximated by the daily internet search volume index (SVI) from Google. Our results reveal that incorporating the SVI variable in various GARCH family models significantly improves volatility forecasts. Moreover, we demonstrate that the sign of stock returns is predictable contrary to the levels, where predictability has proven elusive in the US context. Finally, we provide novel evidence on the economic value of sign predictability and show that investors can form profitable investment strategies using the SVI

Nonlinear modelling of European football scores using support vector machines

This article explores the linear and nonlinear forecastability of European football match scores using IX2 and Asian Handicap odds data from the English Premier league. To this end, we compare the performance of a Poisson count regression to that of a nonparametric Support Vector Machine (SVM) model. Our descriptive analysis of the odds and match outcomes indicates that these variables are strongly interrelated in a nonlinear fashion. An interesting finding is that the size of the Asian Handicap appears to be a significant predictor of both home and away team scores. The modelling results show that while the SVM is only marginally superior on the basis of statistical criteria, it manages to produce out-of-sample forecasts with much higher economic significance

Nonlinear modelling of European football scores using support vector machines

This article explores the linear and nonlinear forecastability of European football match scores using IX2 and Asian Handicap odds data from the English Premier league. To this end, we compare the performance of a Poisson count regression to that of a nonparametric Support Vector Machine (SVM) model. Our descriptive analysis of the odds and match outcomes indicates that these variables are strongly interrelated in a nonlinear fashion. An interesting finding is that the size of the Asian Handicap appears to be a significant predictor of both home and away team scores. The modelling results show that while the SVM is only marginally superior on the basis of statistical criteria, it manages to produce out-of-sample forecasts with much higher economic significance

The term structure of interest rates as predictor of stock market volatility

We examine the forecasting power of the volatility of the slope of the US Treasury yield curve on US stock market volatility. Consistent with theoretical asset pricing models, we find that the volatility of the slope of the term structure of interest rates has significant forecasting power on stock market volatility for forecasting horizon ranging from 1 up to 12 months. Moreover, the term structure volatility has significant forecasting power when used for volatility predictions of the intra-day returns of S&P500 constituents, with the predictive power being higher for stocks belonging to the telecommunications and financial sector. Our forecasting models show that the forecasting power of yield curve volatility is higher to and absorbs that of Economic Policy Uncertainty and Monetary Policy Uncertainty, showing that the main channel through which the yield curve volatility affects the stock market is not only related with uncertainty about monetary policy actions or policy rates, but also with uncertainty regarding the future cash flows and dividend payments of US equities. Lastly, we show that the forecasting power of term structure volatility significantly increases during the post-2007 Great recession period which coincides with the Fed adopting unconventional monetary policies to stimulate the economy

Information demand and stock return predictability

Recent theoretical work suggests that signs of asset returns are predictable given that their volatilities are. This paper investigates this conjecture using information demand, approximated by the daily internet search volume index (SVI) from Google. Our results reveal that incorporating the SVI variable in various GARCH family models significantly improves volatility forecasts. Moreover, we demonstrate that the sign of stock returns is predictable contrary to the levels, where predictability has proven elusive in the US context. Finally, we provide novel evidence on the economic value of sign predictability and show that investors can form profitable investment strategies using the SVI