The estimation of large Vector Autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of...

We conduct an extensive empirical analysis of VIX derivative valuation models over the 2004-2007 bull market and the subsequent financial crisis. We show that existing models yield large distortions during the crisis because of their restrictive volatility mean reverting assumptions. We propose...

This paper studies the returns from investing in index options. Previous research documents significant average option returns, large CAPM alphas, and high Sharpe ratios, and concludes that put options are mispriced. We propose an alternative approach to evaluate the significance of option...

Portfolio choice and the implied asset pricing are usually derived assuming maximization of expected utility. In this Paper, they are derived from risk-value models that generalize the Markowitz-model. We use a behaviourally based risk measure with an endogenous or exogenous benchmark. If the...

King-Fullerton methodology cannot assess the minimum-asset tax (MAT) because it cannot handle uncertainty. We present an alternative based on option pricing, and show how carry-over rules, depreciation conventions and uncertainty affect the MAT burden. Using Brazilian data, we show that: (a)...

The paper analyzes foreign investment and asset prices in a context of uncertainty over future government policy. The model endogenizes the process of learning by foreign investors facing a potentially opportunistic government, which chooses strategically the timing of a policy reversal in order...

We present a pricing model for secondary market debt designed to assess the impact of debt reduction on valuation of remaining claims and to value guarantees in various forms. The technique used, option pricing, accounts explicitly for the sources and natures of risks on secondary market pricing...

In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, since this leads to uncertainty in the predicted option price. We illustrate the...

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those...

We propose a method to produce density forecasts of the term structure of government bond yields that accounts for (i) the possible mispecification of an underlying Gaussian Affine Term Structure Model (GATSM) and (ii) the time varying volatility of interest rates. For this, we derive a Bayesian...