The wide scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonize the electric grid. One challenge to reliably operating the grid is ensuring that sufficient generating capacity is available to meet demand at all hours. By determining an individual...

The Least-Squares Monte Carlo (LSM) algorithm of Longstaff and Schwartz (2001) prices American options with a regression-based early-exercise strategy. This paper analyzes LSM estimator variance to identify two sources: sampling design and stopping time estimation. We examine the effect of...

The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions. The Quasi-Monte Carlo-based conditional pathwise method (QMC-CPW) for options Greeks allows the...

In the Longstaff-Schwartz Least-Squares Monte Carlo (LSM) method for American option pricing, the early-exercise strategy is based on a regression of future option values on current state variables. The dependence between continuation values and future cash flows results in potential model...

Martin and Quinn used item response theory based on the Markov-Chain-Monte-Carlo method to estimate justices’ locations on a one-dimensional scale. Their analysis has been received with interest and doubts by the legal community. This paper explores the Markov Chain Monte Carlo algorithm in...

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized QMC (RQMC) method combines the best of two methods....

This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future...

A common practice in estimating parameters in DSGE models is to Önd a set that when simulated gets close to an average of certain data moments; the modelís simulated performance for other moments is then compared to the data for these as an informal test of the model. We call this procedure...