Contents of Volume 2, Number 1

June 2006

Trying But Failing to Ride the Bull Market: A Random Coefficient Model to Examine Greek Mutual Funds

Performance

N. Philippas

Abstract: This paper provides an empirical assessment of the performance of Greek mutual fund managers based on a random coefficient model during a strong bull market. Monthly returns for all Greek mutual funds (balanced and growth type) are examined. The conclusions can be summarized as follows: (i) Fund managers do not exhibit superior macroforecasting abilities. (ii) Betas change randomly in many funds (18 out of 34), and (iii) certain fund managers exhibit superior micro-forecasting skills (8 out of 34).

Ranking Portfolios From Multiple Articulated Risk Measures: An Uncertainty Decision Approach

E. Ballestero, D. Pla-Santamaria, I. Gonzalez

Abstract: This paper proposes a performance ranking of efficient portfolios when risk is simultaneously evaluated by a set of measures articulated in an uncertainty decision model. After determining the mean-variance (E-V) efficient frontier of portfolios as a previous classic filter of selection, we introduce a new second stage in which the portfolio ranking is achieved by an uncertainty decision table designed for moderately pessimistic investors. In this table, the states of the world are defined in terms of the different risk measures so that the assessment of each portfolio row results from the articulation of composite profitability-safety performance indices depending on the states. Properties underpinning the rationale of the method are shown. A numerical application to a real world case (portfolio ranking of heuristics used by international brokers) is finally developed.

Forecasting the FTSE 100 Volatility: Information Content of Historical, Implied and Implied Stochastic

Volatilities

L. Kalyvas, P. Kiosse, N. Mylonidis

Abstract: A prevailing parameter in the ex ante measurement of option value is the volatility of the underlying asset. Although the abundant literature has proposed a number of different methods for forecasting the future volatility of the underlying asset, the application of these methods in an empirical setting has not always yielded the expected outcomes in terms of consistent estimates. This paper constitutes a further attempt to address the aforementioned issue, by conducting a comprehensive, comparative empirical analysis, utilising data from the FTSE 100 Index European-style options traded at LIFFE. Estimating OLS regressions, IV2SLS regressions and different specifications of GARCH regressions, we conclude that the implied volatility estimate has information content and predictive ability beyond that contained in both the historical and implied stochastic volatility.

A Critical Examination of Three Methodological Issues - Accounting Information (Ratios) Used, Sampling

Procedures, and Expected Cost of Prediction Errors - Related to Corporate Bankruptcy Prediction Models

and Their Implications to the Model’s Usefulness

D.P. Charalambidis, D.L. Papadopoulos

Abstract: In this paper we examine three methodological issues associated with corporate bankruptcy prediction models. Literature review reveals that there are six groups of accounting ratios used to predict bankruptcy. Despite their shortcomings, ratios and, more specifically, traditional ratios are the most popular input of predictive models while the use of other forms of information, such as qualitative variables, faces major computational and interpretational limitations. In the vast majority of cases, models are based on non-random samples and produce biased predictions. Despite the solutions proposed by the relevant literature, this problem still remains unresolved. Another major issue related to corporate bankruptcy prediction is the expected cost of prediction errors. Although this cost is often difficult to compute, users with different attributes may find different models more useful. Our analysis of the abovementioned issues provides with clear guidelines for further research which will lead to the improvement of corporate bankruptcy prediction models.