Extracting market expectations has always been an important issue when making national policies and investment decisions in financial markets. In option markets, the most popular way has been to extract implied volatilities to assess the future variability of the underlying with the use of the...

We describe Bayesian models for economic and financial time series that use regressors sampled at higher frequencies than the outcome of interest. The models are developed within the framework of dynamic linear models, which provides a high level of flexibility and allows direct interpretation...

Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with...

The global financial crisis has brought to the forefront the need for executives to better understand the uses and limitations of the structural models frequently employed in the valuation and risk management activities of their firms. The mandate to better manage systemic risk exposure,...

We develop a structural credit risk model in which the unobserved asset volatility of the firm follows a GARCH process, as in Heston and Nandi (2000). We estimate our model using an Expectation Maximization algorithm, and benchmark it using simulated data against the Merton (1974) model, both...

Extracting market expectations has always been an important issue when making national policies and investment decisions in financial markets. In option markets, the most popular way has been to extract implied volatilities to assess the future variability of the underlying with the use of the...