In: Journal of forecasting, 2006, vol. 25, no. 8, p. 579–600
We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions...
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In: The journal of derivatives, 2008, vol. 16, no. 2, p. 36-53
In the existing literature on barrier options much effort has been exerted to ensure convergence through placing the barrier in close proximity to, or directly onto, the nodes of the tree lattice. For a variety of barrier option types we show that such a procedure may not be a necessary prerequisite to achieving accurate option price approximations. Using the Kamrad and Ritchken (1991)...
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In: Computational Economics, 2007, vol. 29, no. 2, p. 151-158
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In: Computational economics, 2007, vol. 29, no. 2, p. 151-159
Hedging equations from a method suggested by Barone-Adesi, Engle and Mancini, are presented and discussed. This model assumes the option price is homogeneous and the calculation is model independent, providing delta hedge ratios immediately from market data.
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An investor with quadratic utility invests amounts changing with his perceptions of risk and expected return in a market with changing risk. Optimal investment policies are derived under several hypotheses for expected returns. These policies are combined in a Bayesian framework to yield a policy that performs better than the ‘buy and hold’ policy in our tests, except in the case of the FTSE...
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In: Computational statistics & data analysis, 2006, vol. 51, no. 4, p. 2267-2277
A multivariate methodology based on Functional Gradient Descent to estimate and forecast time-varying expected bond returns is presented and discussed. Backtesting this procedure on US monthly data, empirical evidence of its strong forecasting potential in terms of the accuracy of the predictions is collected. The proposed methodology clearly outperforms the classical univariate analysis used...
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In this paper we propose an algorithm for pricing derivatives written on electricity in an incomplete market setting. A discrete time model for price dynamics which embodies the main features of electricity price revealed by simple time series analysis is considered. We use jointly Binomial and Monte Carlo methods for pricing under a risk-neutral measure of which we prove the existence.
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In: The review of financial studies, 2008, vol. 21, no. 3, p. 1223-1258
We propose a new method for pricing options based on GARCH models with filtered historical innovaions. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model’s flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 Index options shows that our model outperforms other...
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This paper proposes a two factor model for asset pricing. We formulate a model of asset returns that in addition to the traditional market return term includes also the square of the market return to account for risk originating from coskewness with the market portofolio. The quadratic term is able to account for heterogeneities across portfolios. We conclude that the extra term is significant...
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We study implied volatility surfaces when the squared volatility is driven by an inverse gamma process. We derive the first two conditional moments of the integrated volatility over the time to maturity to study theoretical term structure volatility patterns. We find that these patterns are in accordance with the empirical ones. Finally, we discuss some probabilistic properties of the volatility...
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