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AbstractIn this article, we develop one‐ and two‐component Markov regime‐switching conditional volatility models based on the intraday range and evaluate their performance in forecasting the daily volatility of the S&P 500 Index. We compare the performance of the models with that of several well‐established return‐ and range‐based volatility models, namely EWMA, GARCH, and FIGARCH models, the Markov regime‐switching GARCH model, the hybrid EWMA model, and the CARR model. We evaluate the in‐sample goodness of fit and out‐of‐sample forecast performance of the models using a comprehensive set of statistical and economic loss functions. To assess the statistical performance of the models, we use mean error metrics, directional predictive ability tests, forecast evaluation regressions, and pairwise and joint tests; and to appraise the economic performance of the models, we use value at risk coverage tests and risk management loss functions. We show that the proposed range‐based Markov switching conditional volatility models produce more accurate out‐of‐sample forecasts, contain more information about true volatility, and exhibit similar or better performance when used for the estimation of value at risk. Our results are robust to the choice of volatility proxy, estimation sample size, out‐of‐sample evaluation period, and alternative error distributions.

We investigate the cross‐sectional relationship between stock returns and a number of measures of option‐implied beta. Using portfolio analysis, we show that the method proposed by Buss and Vilkov (2012, The Review of Financial Studies, 2525, 3113–3140) leads to a stronger relationship between implied beta and stock returns than other approaches. However, using the Fama and MacBeth (1973, Journal of Political Economy, 8181, 607–636) cross‐section regression methodology, we show that the relationship is not robust to the inclusion of other firm characteristics. We further show that a similar result holds for implied downside beta. We, therefore, conclude that there is no robust relation between option‐implied beta and returns.

Standard models—based exclusively on macro-financial variables—have made little progress in explaining the behaviour of exchange rates. In this paper, we introduce a neglected set of'soft power'factors capturing a country's demographic, institutional, political, and social underpinnings to shed some light on the'missing'determinants of exchange rate volatility over time and across countries. Based on a balanced panel data set comprising 115 countries during the period 1996–2015, the empirical results are generally robust across different estimation methodologies and show a high degree of persistence in exchange rate volatility. After controlling for standard macroeconomic factors, we find that the'soft power'variables—such as an index of voice and accountability, life expectancy, educational attainments,fragility of the banking sector,financial openness, and the share of agriculture relative to services—have a statistically significant influence on the level of exchange rate volatility across countries. In other words, countries with greater 'soft power'(i.e. better institutional quality) tend to experience a lower degree of exchange rate volatility.