Volume 12, Issue 2

This manuscript lists questions from a video interview with James Hamilton at the 16th SNDE conference in San Francisco on April 4, 2008. The interview covers topics ranging from nonlinear time series analysis and monetary policy to energy prices.

In this paper, by using a generalized asymmetry measure with the heteroskedasticity autocorrelation consistent estimation method and a long-run variance eliminating method, we propose two generalized symmetry tests in the presence of unknown distributions and serial dependence. The proposed tests encompass existing skewness tests, and generate new symmetry tests that are robust to both the heavy-tails and the serial dependence of stock returns. We also utilize the concept of an augmented distribution to establish an asymmetric distribution family that encompasses Pearson's type-IV distribution, and we use this distribution family and the score test principle to discuss the choice of asymmetry measures for testing symmetry. In this study, we also compare our tests with existing tests using a Monte Carlo simulation and an empirical example, and show that the robust tests outperform existing tests for checking the symmetry of stock returns.

Analyzing comovements in equity markets is important for risk diversification in portfolio management. Copulas have several advantages compared to the linear correlation measure in modeling comovement. This paper introduces a copula ARMA-GARCH model for analyzing the comovement of indexes in German equity markets. The model is implemented with an ARMA-GARCH model for the marginal distributions and a copula for the joint distribution. After goodness-of-fit testing, we find that the skewed Student's t copula ARMA(1,1)-GARCH(1,1) model with Lévy fractional stable noise is superior to alternative models investigated in our study where we model the simultaneous comovement of six German equity market indexes. This model is also suitable for capturing the long-range dependence, tail dependence, and asymmetric correlation observed in German equity markets.

A dynamic hedging strategy based on a bivariate GARCH-jump model augmented with autoregressive jump intensity is proposed to manage currency risk. The GARCH-jump model, capable of capturing volatility clustering and leptokurtosis, provides a comprehensive description of the joint dynamics of the currency spot rate and the futures basis. We find significant common jump components in the currency spot rate and futures basis with jump sizes response asymmetrical to futures basis changes. Our out-of-sample hedging exercises show optimal hedge ratios incorporating information from common jump dynamics substantially reduce the portfolio risk of foreign currencies.

The class of mixture GARCH models introduced by Haas, Mittnik and Paollela (2004) and Alexander and Lazar (2006) provides a better alternative for fitting financial data than various other GARCH models driven by the normal or skewed t-distribution. In this paper we propose different option pricing methodologies when the underlying stock dynamic is modeled by an asymmetric normal mixture GARCH model with K volatility components. Since under GARCH models the market is incomplete there are an infinite number of martingale measures one can use for pricing. For our mixture setting we analyze the impact of three risk-neutral candidates: a generalized local risk neutral valuation relationship, an Esscher transform and an extended Girsanov principle. We investigate the out-of-sample performance of an asymmetric GARCH model with a mixture density of two normals for Call options written on the S&P 500 Index. The performance under all three transformations is quite impressive when compared to the benchmark GARCH model with normal driving noise. The overall improvement is explained not only by the skewness and leptokurtosis exhibited by the innovation mixture distribution, but also by the richer parametrization used in modeling the dynamics of the multi-component conditional volatility.

The purpose of this paper is to study the dynamic interaction between economic growth and unemployment in a general model of economic growth. Labour market disequilibrium is introduced through a non-market real wage clearing modeled by a non linear Phillips Curve. We show that unemployment rate and per capita income dynamics fluctuate along cycles of different periods, and they may even have aperiodic paths. Labour market characteristics are the endogenous source of instability. In particular, as the rigidity of the labour market increases, the possibility of irregular behaviour increases as well. Besides cyclical behaviour, the dynamic analysis points to a positive income growth trend sustained by knowledge accumulation; however, there are periods where per capita income decreases. Moreover, in the same line as wage bargaining models (Layard and Nickell, 1985, 1986), we get the result that the higher workers' bargaining power, the lower both employment rate and per capita production. The paper aims to complement the literature in understanding the origin and nature of fluctuations in economic growth models.