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

Economic Implications of Bull and Bear Regimes in UK Stock Returns

This paper presents evidence of persistent `bull' and `bear' regimes in UK stock returns and considers their economic implications from the perspective of an investor's portfolio decisions. We find that the perceived state probability has a large effect on the optimal allocation to stocks, particularly at short investment horizons. If ignored, the presence of such regimes gives rise to welfare costs that are substantial, particularly in the bear state where stock holdings should be significantly reduced. When we extend the return forecasting model to allow for predictability from the lagged dividend yield, we find that both dividend yields and regime switching have strong effects on the optimal asset allocation.optimal asset allocation, regime switching, Bull and Bear Markets, model specification

Term structure of risk under alternative econometric specifications

This paper characterizes the term structure of risk measures such as Value at Risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with student-t errors, two-component GARCH models and a non-parametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term structures across econometric specifications. An out-of-sample forecasting experiment applied to stock, bond and cash portfolios suggests that the best model is asset- and horizon specific but that the bootstrap and regime switching model are best overall for VaR levels of 5% and 1%, respectively.Time-series analysis ; Econometric models

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

Forecasts of U.S. short-term interest rates: a flexible forecast combination approach

This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expectations hypothesis on the forecasting model are found to help at long forecasting horizons.Interest rates ; Forecasting

Predictable dynamics in the S&P 500 index options implied volatility surface

One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the S&P 500 index options IVS. In the first stage we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas et al. (1998). In the second-stage we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the S&P 500 implied volatility surface can be successfully modeled, but also that its movements over time are highly predictable in a statistical sense. We then examine the economic significance of this statistical predictability with mixed findings. Whereas profitable delta-hedged positions can be set up that exploit the dynamics captured by the model under moderate transaction costs and when trading rules are selective in terms of expected gains from the trades, most of this profitability disappears when we increase the level of transaction costs and trade multiple contracts off wide segments of the IVS. This suggests that predictability of the time-varying S&P 500 implied volatility surface may be not inconsistent with market efficiency.Assets (Accounting) ; Prices

Properties of equilibrium asset prices under alternative learning schemes

This paper characterizes equilibrium asset prices under adaptive, rational and Bayesian learning schemes in a model where dividends evolve on a binomial lattice. The properties of equilibrium stock and bond prices under learning are shown to differ significantly compared with prices under full information rational expectations. Learning causes the discount factor and risk-neutral probability measure to become path-dependent and introduces serial correlation and volatility clustering in stock returns. We also derive conditions under which the expected value and volatility of stock prices will be higher under learning than under full information. Finally, we derive restrictions on prior beliefs under which Bayesian and rational learning lead to identical prices and show how the results can be generalized to more complex settings where dividends follow either multi-state i.i.d. distributions or multi-state Markov chains.Assets (Accounting) ; Rational expectations (Economic theory)

Managing international portfolios with small capitalization stocks

In the context of an international portfolio diversification problem, we find that small capitalization equity portfolios become riskier in bear markets, i.e. display negative co-skewness with other stock indices and high co-kurtosis. Because of this feature, a power utility investor ought to hold a well-diversified portfolio, despite the high risk premium and Sharpe ratios offered by small capitalization stocks. On the contrary small caps command large optimal weights when the investor ignores variance risk, by incorrectly assuming joint normality of returns. The dominant factor in inducing such shifts in optimal weights is represented by the co-skewness, the predictable, time-varying covariance between returns and volatilities. We calculate that if an investor were to ignore co-skewness and co-kurtosis risk, he would suffer a certainty-equivalent reduction in utility equal to 300 basis points per year under the steady-state distribution for returns. Our results are qualitatively robust when both European and North American small caps are introduced in the analysis. Therefore this paper offers robust evidence that predictable covariances between means and variances of stock returns may have a first order effect on portfolio composition.Investments, Foreign ; Stocks

Affiliated mutual funds and analyst optimism

Prior studies have shown that investment banking affiliations place pressure on analysts to produce optimistic recommendations on the investment bank’s stock-clients. Our analysis of a large sample of recommendations issued from 1995 through 2003 indicates that a mutual fund affiliation also affects analysts’ research. That is, analysts are likely to look favorably at stocks held by the affiliated mutual funds. Controlling for a variety of factors including the investment banking affiliation, we find that the greater the portfolio weight of a stock for the affiliated mutual funds, the more optimistic the analyst rating becomes when compared to the consensus. Reputation partly restrains the optimism of analyst recommendations. In fact, the presence of other institutional investors as shareholders of the recommended stocks curbs analyst optimism. Nevertheless, from 1999 through 2001, star analysts report the most optimism when they recommend stocks in the portfolios of affiliated mutual funds.Mutual funds ; Investment banking