Massimo Guidolin

The Economic Effects of Violent Conflict: Evidence from Asset Market Reactions

This article studies the effects of conflict onset on asset markets applying the event study methodology. The authors consider a sample of 101 internal and inter-state conflicts during the period 1974—2004 and find that a sizeable fraction of them has had a significant impact on stock market indices, exchange rates, oil and commodity prices. This fraction is inconsistent with pure chance, that is, with the selected probability of type-I errors in our tests of statistical significance. The results suggest that, on average, national stock markets are more likely to display positive than negative reactions to conflict onset. When the authors distinguish between internal and inter-state conflicts, they find that the fraction of significant results is higher for international conflicts. When the authors classify events according to the region where they occur, they find that Asia and the Middle East are the regions where conflicts tend to have the strongest effects. Finally, the article reports evidence that abnormal returns would have accrued to investors systematically exploiting conflict onset to implement conflict-driven strategies. Results are robust to selecting a subset of high-intensity conflicts and to expanding the time window over which conflict events are defined. The findings of the article confirm the economic importance of the effects of conflicts on asset markets.

Forecasts of US 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.

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.

Option prices under Bayesian learning: implied volatility dynamics and predictive densities

This Paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolves on a binomial lattice with unknown but recursively updated probabilities, we derive closed-form pricing formulas for European options. Learning is found to generate asymmetric skews in the implied volatility surface and systematic patterns in the term structure of option prices. Data on S&P 500 index option prices is used to back out the parameters of the underlying learning process and to predict the evolution in the cross-section of option prices. The proposed model leads to lower out-of-sample forecast errors and smaller hedging errors than a variety of alternative option pricing models, including Black-Scholes and a GARCH model.

Markov Switching Models in Empirical Finance

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Strategic Asset Allocation and Consumption Decisions under Multivariate Regime Switching

This paper studies optimal asset allocation to stocks, long-term bonds and T-bills and consumption choice in the presence of regime switching in asset returns. Optimal asset allocations vary considerably across four states - both across bonds and stocks and among large and small stocks - and change significantly over time as investors revise their estimates of the current state probabilities. In the crash state investors always allocate more of their portfolio to stocks the longer their investment horizon, while the optimal allocation to stocks declines as a function of the investment horizon in bull markets. Consumption-to-wealth ratios are also found to depend on the underlying state. Welfare costs from ignoring regime switching are substantial, especially when frequent rebalancing is considered. Results are found to be robust to changes in risk aversion, the imposition of short sale restrictions, the inclusion of standard predictor variables such as the dividend yield and to parameter uncertainty

A Yield Spread Perspective on the Great Financial Crisis: Break-Point Test Evidence

We use a simple partial adjustment econometric framework to investigate the effects of the crisis on the dynamic properties of a number of yield spreads. We find that the crisis has caused substantial disruptions revealed by changes in the persistence of the shocks to spreads as much as by in their unconditional mean levels. Formal breakpoint tests confirm that the financial crisis has been over approximately since the Spring of 2009. The financial crisis can be conservatively dated as a August 2007 – June 2009 phenomenon, although some yield spread series seem to point out to an end of the most serious disruptions as early as in December 2008. We uncover evidence that the LSAP program implemented by the Fed in the US residential mortgage market has been effective, in the sense that the risk premia in this market have been uniquely shielded from the disruptive effects of the crisis.

Recursive Modeling of Nonlinear Dynamics in UK Stock Returns

This paper presents results from recursive modeling of nonlinear dynamics in UK stock returns. A specification search suggests a two-state model and we demonstrate the ability of this model to capture time-varying volatility, skew and kurtosis in UK stock returns. An out-of-sample forecasting experiment confirms the strong statistical evidence of nonlinearity and shows that accounting for regimes leads to improved forecasting performance.

Can We Forecast the Implied Volatility Surface Dynamics of Equity Options? Predictability and Economic Value Tests

We examine whether the dynamics of the implied volatility surface of individual equity options contains exploitable predictability patterns. Predictability in implied volatilities is expected due to the learning behavior of agents in option markets. In particular, we explore the possibility that the dynamics of the implied volatility surface of individual equity options may be associated with movements in the volatility surface of S&P 500 index options. We present evidence of strong predictable features in the cross-section of equity options and of dynamic linkages between the implied volatility surfaces of equity options and S&P 500 index options. Moreover, time-variations in stock option volatility surfaces are best predicted by incorporating information from the dynamics in the implied volatility surface of S&P 500 index options. We analyze the economic value of such dynamic patterns using strategies that trade straddle and delta-hedged portfolios, and we find that before transaction costs such strategies produce abnormal risk-adjusted returns.

MARKOV SWITCHING IN PORTFOLIO CHOICE AND ASSET PRICING MODELS: A SURVEY

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.