The intrinsic value of gold: an exchange rate-free price index

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market. Market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies, respectively. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset

Soft power and exchange rate volatility

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordStandard 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 dataset 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

Dynamic Factor Long Memory Volatility

ArticleThis is the author accepted manuscript. The final version is available from Taylor & Francis (Routledge) via the DOI in this record.In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. (2001) and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis

Option-implied betas and the cross section of stock returns

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.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) leads to a stronger relationship between implied beta and stock returns than other approaches. However, using the Fama and MacBeth (1973) 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

Financial market volatility, macroeconomic fundamentals and investor sentiment

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long run component and a transitory short run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications. Out-of-sample forecasting shows that the components of volatility can improve forecasts of macroeconomic fundamentals, and vice versa.Harris and Stoja gratefully acknowledge the support of the Economic and Social Research Council Impact Acceleration Account (grant number ES/M50046X/1)

Corporate Valuation and Dividends: UK Evidence from Panel Unit Root and Cointegration Tests

Accounting-based valuation, Dividends, Panel unit roots, Panel Cointegration, C20, C23, G10,