Inference Methods for Discretely Observed Continuous-Time Stochastic Volatility Models: A Commented Overview

In this paper an overview of inference methods for continuous-time stochastic volatility models observed at discrete times is presented. It includes estimation methods for both parametric and nonparametric models that are completely or partially observed in a variety of situations where the data might be nonlinear functions of the components of the model and/or contaminated with observation noise. In each case, the main reported methods are presented, making emphasis on underlying ideas, theoretical properties of the estimators (bias, consistency, efficient, etc.), and the viability of their implementation to solve actual problems in finance.     

An axiomatic approach to \boldsymbol{\varepsilon}-contamination

Summary.  Suppose that an economic agent is 100% certain that uncertainty she faces is characterized by a particular probability measure, but that she has a fear that, with 100% chance, her conviction is completely wrong and she is left perfectly ignorant about the true measure in the present as well as in the future. This situation is often called -contamination of confidence. The purpose of this paper is to provide a simple set of behavioral axioms under which the decision-makers preference is represented by the Choquet expected utility with the -contamination of confidence.Received: 25 November 2002, Revised: 15 November 2004, JEL Classification Numbers:   D81. Correspondence to: Hiroyuki OzakiWe are grateful to an anonymous referee. The referees comments greatly improved the exposition of the paper. The work reported here is partially supported by a grant from the Economic and Social Research Insitute, the Cabinet Office, the Government of Japan.     

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Paraná (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.      

Solutions For Some Dynamic Problems With Uncertainty Aversion

In a discounted expected-utility problem, tomorrow's utilities are aggregated across tomorrow's states by the expectation operator. In our problems, this aggregation is accomplished by a Choquet integral of the form ∫ u d P ^α, where α specifies uncertainty aversion. We solve all finite-state problems by either a closed form or a finite-dimensional iteration, and show that uncertainty aversion reduces the perceived return on investment, thereby decreasing the saving rate given elastic preferences and increasing the saving rate given inelastic preferences.
JEL Classification Numbers: C61, D81, D9.     

Conditional implicit mean and the law of iterated integrals

This paper presents a new framework which generalizes the concept of conditional expectation to mean values which are implicitly defined as unique solutions to some functional equation. We call such a mean value an implicit mean. The implicit mean and its very special example, the quasi-linear mean, have been extensively applied to economics and decision theory. This paper provides a procedure of defining the conditional implicit mean and then analyzes its properties. In particular, we show that the conditional implicit mean is in general “biased” in the sense that an analogue of the law of iterated expectations does not hold and we characterize the quasi-linear mean as the only implicit mean which is “unbiased”.     

Adopting sustainable innovation: what makes consumers sign up to green electricity?

This paper investigates what encourages consumers to adopt a green electricity tariff. When people decide to adopt an innovation, such as green electricity, they consider not only functionality, usability, costs and intended outcomes, but also what the innovation means to them, for example, the way it reflects their identity, image, memberships, values and norms. The study reviews the theoretical frameworks of innovation adoption and consumption, and cognitive and normative behaviour, relevant to consumer adoption of pro‐environmental innovations, and develops a research framework. Through focus group discussions, a questionnaire survey with 103 respondents and an interview with 10 people, the study finds that consumers sympathetic to environmental issues do not necessarily adopt green electricity. This is due to lack of strong social norms and personal relevance, inconvenience of switching, uncertainty about the quality of green electricity and lack of accurate information. The implications of these findings for strategy, policy and future research are explored. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.     

WHO BENEFITS FROM CORRODING KEIRETSU?

Abstract. Observations on Japan's industrial distribution system reveal that many producers maintain both keiretsu and non‐keiretsu channels. An intriguing question then is: what economic rationale underlies such an ambivalent policy? This motivated us to present a model of an incomplete keiretsu system. We find that the collapse of a keiretsu is not necessarily a misfortune for the firms remaining therein: they can turn the situation to their advantage if a keiretsu collapses partially. We show why some firms leave their keiretsu while others remain with it, and determine who the winners are.     

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paraná. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.