We provide a general valuation approach for capital budgeting decisions involving the modularization of a system. Within the framework developed by Baldwin and Clark (2000), we implement an approach using a numerical procedure based on the Least Squares Monte Carlo method proposed by Longstaff...

We propose a methodology for computing single and multi-asset European option prices, and more generally expectations of scalar functions of (multivariate) random variables. This new approach combines the ability of Monte Carlo simulation to handle high-dimensional problems with the efficiency...

Our paper addresses the correction of the aggregation bias in linear rational expectations models when there is some unobserved micro-parameter heterogeneity and only macro data are available. Starting from Lewbel (1994), we propose two new consistent estimators, which rely on a exible...

This study yields a contribution to a better understanding of the interest rate sensitivity of real estate and should enable a more sophisticated interest rate risk management, especially for insurance companies and pension funds. This is achieved by modelling the whole life of a typical but...

Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or the fractional...

We propose a technique to avoid spurious detections of jumps in high-frequency data via an explicit thresholding on available test statistics. We prove that it eliminates asymptotically all spurious detections. Monte Carlo results show that it performs also well in finite samples. In Dow Jones...

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse...

We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with pair-copula constructions, and nest many standard models as special cases. The loss...