This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models’ performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by...

Purpose: This paper aims to investigate whether the assumption of bias-free journalism is violated. If there is systematic news coverage bias inherent in business journalism, certain kinds of companies will have a systematically higher or lower visibility in business news. Such differential...

Purpose – Evaluation of product development projects is quite intuitive and subjective. The purpose of this paper is to analyze decision makers' value perceptions of organizational impact before and after a project to explain how post‐project value perceptions are formed....

Purpose – Comprehensive R&D project valuation calls for internal information on, for example, R&D investments as well as external information such as market projections that relate to the expectations on product's life and revenue cash flows. Given the versatility of information needs, the...

This article examines the differences between various Generalized Autoregressive Conditional Heteroscedastics (GARCH) models in pricing exotic options, given that the models have been calibrated on the same data sets using information on both returns and plain vanilla options. We focused on four...

The geometric Brownian motion is routinely used as a dynamic model of underlying project value in real option analysis, perhaps for reasons of analytic tractability. By characterizing a stochastic state variable of future cash flows, this paper considers how transformations between a state...

This paper compares two distributed computing environments when used to price financial contingent claims with Monte Carlo methods: a PC grid and a scientific computing Linux cluster. The paper also investigates the performances for different distributing strategies. On the basis of our...