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...

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...

In this paper we use Monte Carlo simulation to investigate the impact of effect size heterogeneity on the results of a meta-analysis. Specifically, we address the small sample behaviour of the OLS, the fixed effects regression and the mixed effects meta-estimators under three alternative...

Skewed data is the main issue in statistical models in healthcare costs. Data transformation is a conventional method to decrease skewness, but there are some disadvantages. Some recent studies have employed generalized linear models (GLMs) and Cox proportional hazard regression as alternative...

We investigate, if it pays off for a company to invest into complex swing option algorithms. We first introduce least squares Monte Carlo as a complex valuation algorithm and explain in detail how it works. Using a simulation study and two backtest scenarios we compare the output of this method...

In this paper, we propose a modification of the three-pass regression filter (3PRF) to make it applicable to large mixed frequency datasets with ragged edges in a forecasting context. The resulting method, labeled MF-3PRF, is very simple but compares well to alternative mixed frequency factor...

Background: This article investigates the Least-Squares Monte Carlo Method by using different polynomial basis in American Asian Options pricing. The standard approach in the option pricing literature is to choose the basis arbitrarily. By comparing four different polynomial basis we show that...

Although independent unobserved heterogeneity--variables that affect the dependent variable but are independent from the other explanatory variables of interest--do not affect the point estimates or marginal effects in least squares regression, they do affect point estimates in nonlinear models...