In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

In this paper, we present a new strategy for pricing average value options, i.e. options whose payoff depends on the average price of the underlying asset over a fixed period leading up to the maturity date. Such options are of particular interest and importance for thinly-traded assets (e.g. crude oil), since price manipulation is inhibited, and both the investor and issuer enjoy a welcome degree of protection from the vagaries of the market. These options are often implicit in a bond contract, although they also appear in a straightforward form. Our results suggest that the price of an average-value option will always be lower than that of a standard European option. Our pricing strategy involves Monte Carlo simulation with variance reduction elements and offers an enhanced pricing method to both arbitragers and hedgers, as well as to the issuers of such bonds.

A Pricing Method for Options Based on Average Asset Values

We investigate whether investor sentiment affects the cross-section of stock returns in China A-share market.The evidence shows that the sensitivity of stock returns to sentiment changes is different. When an index of investor sentiment takes high values,low tangible assets,high Debt-asset ratio,and non-dividend-paying stocks earn relatively higher returns,when sentiment is low,the aforementioned categories of stocks earn relatively lower returns.When sentiment is high,low-price,unprofitable and high book-market ratio stocks earn relatively higher returns and vice versa,but which is insignificant.The capitalization, volatility and institutional ownership appear to have no significant cross-section effect of investor sentiment on its characteristic Portfolio return.

Investor Sentiment and the Cross-Section of Stock Returns

Scott D. Thomson is deputy executive director, NASSP, Reston, Va. Parents often complain about counseling services in their schools. Teachers frequently criticize &dquo;that counseling department&dquo; as well. Even students-the recipients of counseling-don’t appear to appreciate the service. Typically students rely more upon parents or peers for advice on education or personal matters than upon counselors. What has gone wrong? Counselors are hard-working, dedicated persons with good intentions. Trained and subsidized during the heyday of NDEA, they now form in secondary schools a corps of experienced professionals. Should they not be the most effective and appreciated of school personnel? The villian in this scenario is ambiguity, a lack of preciseness in determining the role and function of counselors. Each group has its own expectations. Whether student or parent or principal or counselor-each has a private definition of what it is counselors are supposed to do. For instance, parents want advice for students on course selection and assistance with college entrance; students want class schedule changes and discussion on advanced education; teachers want behavior

An Introduction: New Opportunities for Counseling Services

This paper estimates constant and dynamic hedge ratios in the New York Mercantile Exchange oil futures markets and examines their hedging performance. We also introduce a Markov regime switching vector error correction model with GARCH error structure. This specification links the concept of disequilibrium with that of uncertainty (as measured by the conditional second moments) across high and low volatility regimes. Overall, in and out-of-sample tests indicate that state dependent hedge ratios are able to provide significant reduction in portfolio risk.

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A Markov regime switching approach for hedging energy commodities

Introduction to Risk Management and Derivatives Introduction to Shipping Markets Statistical Tools for Risk Analysis and Modelling Freight Market Information Forward Freight Agreement Technical Analysis and Freight Trading Strategies Options on Freight Rates Pricing and Risk Management of Option Positions Value-at-Risk in Shipping and Freight Risk Management Financial and Interest Rate Risk Management in Shipping Credit Risk Measurement and Management in Shipping Ship Price Risk and Risk Management Real Options and Optionalities in Shipping

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Shipping Derivatives and Risk Management

Forecasting petroleum futures markets volatility: The role of regimes and market conditions

In this paper we describe a new approach for determining time-varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the “state of the market,” one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out-of-sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649–674, 2004

A Markov regime switching approach for hedging stock indices

The aim of this paper is to investigate, for the first time, the performance of trading strategies based on the combination of technical trading rules and fundamental analysis in the sale and purchase market for dry bulk ships. Using a sample of price and charter rates over the period January 1976 to September 2004, we establish the existence of a long-run cointegrating relationship between price and earnings and use this relationship as an indicator of investment or divestment timing decisions in the dry bulk shipping sector. In order to discount the possibility of data snooping biases and to evaluate the robustness of our trading models, we also perform tests using the stationary bootstrap approach. Our results indicate that trading strategies based on earnings–price ratios significantly out-perform buy and hold strategies in the second-hand market for ships, especially in the market for larger vessels, due to higher volatility in these markets.

Nikos K. Nomikos

Papers

A Markov regime switching approach for hedging energy commodities

Shipping Derivatives and Risk Management

Forecasting petroleum futures markets volatility: The role of regimes and market conditions

A Markov regime switching approach for hedging stock indices

Investment timing and trading strategies in the sale and purchase market for ships