Perspectives on research issues in consumer behavior

What happens when an academic researcher becomes a policymaker? Recently, President Anthony Santomero shared some thoughts on this topic with members for the Downtown Economists Club in New York City. In particular, he talked about several conundrums he's encountered since moving from the academy to the central bank. We've reprinted his speech "The Complexities of Monetary Policy" in this issue of the Business ReviewConsumer credit

A fuzzy real option approach for investment project valuation

[[abstract]]The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project.[[incitationindex]]SCI[[booktype]]紙

Convertible Bonds: Risks and Optimal Strategies

Within the structural approach for credit risk models we discuss the optimal exercise of the callable and convertible bonds. The Vasi˘cekâmodel is applied to incorporate interest rate risk into the firmâs value process which follows a geometric Brownian motion. Finally, we derive pricing bounds for convertible bonds in an uncertain volatility model, i.e. when the volatility of the firm value process lies between two extreme values.Convertible bond, game option, uncertain volatility, interest rate risk

The Econometrics of Option Pricing

In this survey, we review econometric models for conducting statistical inference on option price data. We limit our review to European options on a stock index as well as to statistical methods which have been specifically developped for options. Emphasis is put on the synthesis of the various models used in the literature. We start with discrete-time models based on the unifying principle of stochastic discount factor. We cover multinomial trees as well as risk neutral valuation in a conditionally log-normal setting. Extensions to mixtures of log-normals lead to stochastic volatility models, including models with leverage effect. We characterize implications of such models for volatility smiles and show that they are fully similar to the ones derived from continuous-time stochastic volatility models. We then review usual continuous-time models, in particular affine jump-diffusion models or models with several nonlinear factors, as well as extensions with Levy processes or long memory in volatility. We analyze in this context implicit state methods, both parametric (maximum likelihood) and semiparametric (method of moments). We conclude with a review of nonparametric methods which are used to extract pricing probability measures: canonical, implied binomial trees, and seminonparametric approaches (kernels, neural networks and splines). Extraction of preferences based on these measures are also discussed. Dans ce survol, nous passons en revue les modèles économétriques adaptés à l'inférence statistique sur données de prix d'options. Nous nous limitons aux options de type européen sur un indice de marché d'actions. Seules sont explicitées les techniques d'inférence statistique qui ont connu des développements spécifiques pour les données de prix d'options. L'accent est mis sur la modélisation. On commence par une synthèse des modèles en temps discret à partir du principe unificateur de facteur d'actualisation stochastique. Ceci nous permet de couvrir tant les modèles d'arbres multinomiaux que la valorisation risque neutre dans un contexte de log-normalité conditionnelle. L'extension aux mélanges de lois log-normales conduit aux modèles de volatilité stochastique, y compris les modèles avec effet de levier. Nos caractérisons les implications en termes de sourire de volatilité et montrons qu'elles sont pleinement similaires à celles d'un modèle de volatilité stochastique en temps continu. Nous passons ensuite aux modèles usuels en temps continu, notamment les modèles de diffusion avec sauts ou avec plusieurs facteurs non-linéaires, ainsi que les extensions avec processus de Lévy ou mémoire longue dans la volatilité. Nous abordons dans ce contexte les méthodes avec états implicites, à la fois paramétriques (maximum de vraisemblance) ou semiparamétriques (méthode des moments). Enfin, nous passons en revue les méthodes nonparamétriques qui permettent d'extraire directement les mesures de probabilité d'évaluation : canoniques, arbres binomiaux impliqués et approches semi-nonparamétriques (noyaux, réseaux de neurones et splines). Les implications en termes d'extraction des préférences sont aussi discutées.Stock PriceDynamics, Multivariate Jump-DiffusionModels, Latent variables, Stochastic Volatility, Objective and Risk Neutral Distributions, Nonparametric Option Pricing, Discretetime Option Pricing Models, Risk Neutral Valuation, Preference-free Option Pricing, Dynamique des prix d'actions, modèles de diffusion-sauts à plusieurs variables, variables latentes, volatilité stochastique, distributions objective et risque neutre, modèles nonparamétriques d'évaluation des options, modèles d'évaluation des options en temps discret, évaluation risque neutre, évaluation des options sans paramètres de préférence

Application of stochastic programming to management of cash flows with FX exposure

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 23/06/2006.In this thesis we formulate a model for foreign exchange (FX) exposure management and multi-currency cash management taking into consideration random fluctuations of exchange rates and net revenues of a multinational firm (MNF). The central decision model used in this thesis is a scenario-based stochastic programming (SP) recourse model. A critical review of alternative scenario generation methods is given followed by analysis of some desirable properties of the scenario tree. The application of matching statistical moments of a probability distribution to generate a multiperiod scenario tree for our problem is described in detail. A four-stage SP decision model is formulated using the random parameter values. This model evaluates currency / cash flows hedging strategies, which provide rolling decisions on the size and timing of the forward positions. We compute an efficient frontier from which an investor can choose an optimal strategy according to his risk and return preferences. The flexibility of the SP model allows an investor to analyse alternative risk-return trading strategies. The model decisions are investigated by making comparisons with decisions based purely on the expected value problem. The investigation shows that there is a considerable improvement to the "spot only" strategy and provides insight into how these decisions are made. The contributions of the thesis are summarised below. (i) The FX forward scenario trees are derived using an arbitrage-free pricing strategy and is in line with modem principles of finance. (ii) Use of the SP model and forward contracts as a tool for hedging decisions is novel. (iii) In particular smoothing of the effects in exchange rates and the smoothing of account receivables are examples of innovative modelling approaches for FX management