In this report, we first develop a simplified example that illustrates the importance of considering the option ``waiting to invest'' when valuing an investment. This is followed by a short description of other options that could be embedded in an investment opportunity. In order to stress the...

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential...

We propose different extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed by Foster and Nelson (1996). First, despite the difference in information sets we are able to compare the asymptotic distribution of volatility estimators involving data...

Value-at-Risk (VaR) has emerged as the standard tool for measuring and reporting financial market risk. Currently, more than eighty commercial vendors offer enterprise or trading risk management systems which report VaR-like measures. Risk managers are therefore often left with the daunting task...

Using a new dataset of bid and offer quotes for credit default swaps, we investigate the relationship between theoretical determinants of default risk and actual market premia using linear regression. These theoretical determinants are firm leverage, volatility and the riskless interest rate. We...

Policy makers and market participants often consider the forward-looking information in currency option valuations when making assessments about future developments in foreign exchange rates. Option implied volatilities can be used as forecasts of realized volatility and interval and density...

We attempt to answer the following questions: What are the revaluation effects and the impact on performance, volatility, and return correlation from stock market liberalization in emerging markets? These questions have been studied extensively at the market-level but not at the firm level. Our...

We develop a class of ARCH models for series sampled at unequal time intervals set by trade or quote arrivals. Our approach combines insights from the temporal aggregation for GARCH models discussed by Drost and Nijman (1993) and Drost and Werker (1994), and the autoregressive conditional...

We consider sequential data that is sampled from an unknown process, so that the data are not necessarily iid. We develop a measure of generalization for such data and we consider a recently proposed approach to optimizing hyper-parameters, based on the computation of the gradient of a model...

Input/Output Hidden Markov Models (IOHMMs) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or non-linear (using for example...