Investigating Sources of Unanticipated Exposure in Industry Stock Returns

This paper investigates the degree of both foreign exchange rate and interest rate exposure of industry level portfolios in the G7. Our paper draws on the efficient market hypothesis and examines the extent of unexpected foreign exchange (and interest rate) exposure rather than the standard approach of focusing purely on the change in foreign exchange (and interest rate) exposure. The results from our baseline regressions are consistent with those previously found in the literature that there is little evidence of exchange rate exposure in most markets — this is the exchange rate exposure puzzle. The second critical element of our analysis is that we investigate the sources of the exposure and examine the existence of indirect levels of both foreign exchange and interest rate exposure. The findings of exposure to foreign exchange rates and interest rates are extensive for industry sectors in the G7 economies when we take account of the possible channels of influence. Results indicate key differences between countries in terms of the relative importance of these cash flow and discount rate channels.Foreign exchange, exposure, interest rates, stock returns, international finance

Can VAR models capture regime shifts in asset returns? a long-horizon strategic asset allocation perspective

In the empirical portfolio choice literature it is often invoked that through the choice of predictors that may closely track business cycle conditions and market sentiment, simple Vector Autoregressive (VAR) models could produce optimal strategic portfolio allocations that hedge against the bull and bear dynamics typical of financial markets. However, a distinct literature exists that shows that non-linear econometric frameworks, such as Markov switching, are also natural tools to compute optimal portfolios arising from the existence of good and bad market states. In this paper we examine whether and how simple VARs can produce empirical portfolio rules similar to those obtained under a range of multivariate Markov switching models, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock-bond strategic asset allocation problem on US data, we compute the out-of-sample certainty equivalent returns for a wide range of VARs and compare these measures of performance with those typical of non-linear models that account for bull-bear dynamics and characterize the differences in the implied hedging demands for a long-horizon investor with constant relative risk aversion preferences. We conclude that most (if not all) VARs cannot produce portfolio rules, hedging demands, or out-of-sample performances that approximate those obtained from equally simple non-linear frameworks.Econometric models ; Vector autoregression ; Asset pricing ; Rate of return

Equity portfolio diversification under time-varying predictability and comovements: evidence from Ireland, the US, and the UK

We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among short-term interest rates (monetary policy) and stock returns in the Irish, the US and UK markets. We find that two regimes, characterized as bear and bull states, are required to characterize the dynamics of returns and short-term rates. This implies that we cannot reject the hypothesis that the regimes driving the markets in the small open economy are largely synchronous with those typical of the major markets. We compute time-varying Sharpe ratios and recursive mean-variance portfolio weights and document that a regime switching framework produces out-of-sample portfolio performance that outperforms simpler models that ignore regimes. Interestingly, the portfolio shares derived under regime switching dynamics implies a fairly low commitment to the Irish market, in spite of its brilliant unconditional risk-return trade-off.Stock exchanges

What tames the Celtic tiger? portfolio implications from a multivariate Markov switching model

We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among the Irish stock market, one of the top world performers of the 1990s, and the US and UK stock markets. We find that two regimes, characterized as bear and bull states, are required to characterize the dynamics of excess equity returns both at the univariate and multivariate level. This implies that the regimes driving the small open economy stock market are largely synchronous with those typical of the major markets. However, despite the existence of a persistent bull state in which the correlations among Irish and UK and US excess returns are low, we find that state comovements involving the three markets are so relevant to reduce the optimal mean variance weight carried by ISEQ stocks to at most one-quarter of the overall equity portfolio. We compute time-varying Sharpe ratios and recursive mean-variance portfolio weights and document that a regime switching framework produces out-of-sample portfolio performance that outperforms simpler models that ignore regimes. These results appear robust to endogenizing the effects of dynamics in spot exchange rates on excess stock returns.Stock exchanges

Forex Risk: Measurement and Evaluation using Value-at-Risk

Over the past decade the growth of trading activity in financial markets, numerous instances of financial instability, and a number of widely publicised losses on banks' trading books have resulted in a re-analysis of the risks faced, and how they are measured. The most widely advocated approach to have emerged to measure market risk is that of Value-at-Risk (VaR). This methodology was designed in J.P. Morgan to give their chief executive a single figure that would provide a daily summary of the evolving risk of the Banks investment portfolio. VaR methods estimate the distribution of losses/gains on a portfolio of assets/liabilities over a given horizon. From the estimated distribution one can then find, for the loss on the portfolio, a bound that will only be exceeded rarely. This bound is the VaR. The term “rarely” is often taken to refer to an event that occurs one or five times per hundred periods. In actual applications users are free to define “rarely” to suit their own needs. VaR can be calculated in various ways and its value depends on the assumptions made and models used. This paper looks at six different measures of the VaR of an Irish investor holding an equally weighted portfolio of foreign exchange positions in the currencies of Ireland’s major trading partners. The basic data used are daily exchange rates covering the period 1990 to 1998. Daily VaRs for four different holding periods are calculated, using six alternative approaches to estimating the distribution of the underlying risk. The measured VaRs are compared graphically and statistically with actual losses/gains over the period. Recently developed techniques are used to measure the performance and accuracy of the estimates of the VaR estimates. For the portfolios considered here the method based on Exponentially Weighted Moving Averages is superior to the others. This may of course be due to the statistical properties of the FOREX returns being considered. The article provides a framework for the comparison of different measures of VaR. These can be adapted for the evaluation of alternative VaR models for risk control within an organisation. This framework can also serve as an input to the validation of in-house models proposed for the calculation of capital adequacy under the Capital Adequacy Directive.

European Monetary Policy Surprises: The Aggregate and Sectoral Stock Market Response

In this paper we investigate the stock market response to international monetary policy changes in the UK and Germany. Specifically, we analyse the impact of (un)expected changes in UK and German/euro area policy rates on UK and German aggregate and sectoral stock returns in an event study. The decomposition of the (un)expected changes in policy rates are based on futures markets. Overall, our results suggest that, UK monetary policy surprises have a significant negative influence on both aggregate and industry level stock returns in both the UK and Germany. The influence of German/Euro area monetary policy shocks appears insignificant for both countries.

Correlation dynamics between Asia-Pacific, EU and US stock returns

This paper investigates the correlation dynamics in the equity markets of 13 Asia-Pacific countries, Europe and the US using the asymmetric dynamic conditional correlation GARCH model (AG-DCC-GARCH) introduced by Cappiello, Engle and Sheppard (2006). We find significant variation in correlation between markets through time. Stocks exhibit asymmetries in conditional correlations in addition to conditional volatility. Yet asymmetry is less apparent in less integrated markets. The Asian crisis acts as a structural break, with correlations increasing markedly between crisis countries during this period though the bear market in the early 2000s is a more significant event for correlations with developed markets. Our findings also provide further evidence consistent with increasing global market integration. The documented asymmetries and correlation dynamics have important implications for international portfolio diversification and asset allocation.dynamic conditional correlation; asymmetry; international portfolio diversification

Non-linear predictability in stock and bond returns: when and where is it exploitable?

We systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching (predictive) regression models, we also estimate univariate models in which conditional heteroskedasticity is captured through GARCH, TARCH and EGARCH models and ARCH-in mean effects appear in the conditional mean. Although we fail to find a consistent winner/out-performer across all countries and asset markets, it turns out that capturing non-linear effects is of extreme importance to improve forecasting performance. U.S. and U.K. asset return data are “special” in the sense that good predictive performance seems to loudly ask for models that capture non linear dynamics, especially of the Markov switching type. Although occasionally also stock and bond return forecasts for other G7 countries appear to benefit from non-linear modeling (especially of TAR and STAR type), data from France, Germany, and Italy express interesting predictive results on the basis of simpler benchmarks. U.S. and U.K. data are also the only two data sets in which we find statistically significant differences between forecasting models. Results appear to be remarkably stable over time, and robust to the specification of the loss function used in statistical evaluations as well as to the methodology employed to perform pairwise comparisons.Group of Seven countries ; Financial markets