Panayiotis Theodossiou

Predicting Shifts in the Mean of a Multivariate Time Series Process: An Application in Predicting Business Failures

Abstract A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firms slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

A conditional-SGT-VaR approach with alternative GARCH models

This paper proposes a conditional technique for the estimation of VaR and expected shortfall measures based on the skewed generalized t (SGT) distribution. The estimation of the conditional mean and conditional variance of returns is based on ten popular variations of the GARCH model. The results indicate that the TS-GARCH and EGARCH models have the best overall performance. The remaining GARCH specifications, except in a few cases, produce acceptable results. An unconditional SGT-VaR performs well on an in-sample evaluation and fails the tests on an out-of-sample evaluation. The latter indicates the need to incorporate time-varying mean and volatility estimates in the computation of VaR and expected shortfall measures.

Stochastic behaviour of the Athens stock exchange

The stochastic behaviour of stock prices on the Athens Stock Exchange in Greece is investigated. The methodology employed is Nelsons (1991) exponential GARCH-M model, which allows shocks to have an asymmetric impact on volatility. The findings suggest that both the first and the second moments of the distribution of returns are time-dependent, and as such cannot be modelled as white-noise processes. Specifically, volatility is an asymmetric function of past shocks in the sense that positive shocks have a greater impact on volatility than negative shocks. When returns are measured in dollar terms, the estimated risk premium is positive and significant, i.e. returns are positively related to volatility. These findings are in contrast to those discovered by other studies for the US stock prices, e.g. Pagan and Schwert (1990) and Nelson (1991).

Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit.

Finitely Repeated Games with Finite Automata

The paper studies the implications of bounding the complexity of the strategies players may select, on the set of equilibrium payoffs in repeated games. The complexity of a strategy is measured by the size of the minimal automation that can implement it. A finite automation has a finite number of states and an initial state. It prescribes the action to be taken as a function of the current state and a transition function changing the state of the automaton as a function of its current state and the present actions of the other players. The size of an automaton is its number of states. The main results imply in particular that in two person repeated games, the set of equilibrium payoffs of a sequence of such games, G(n), n =1, 2, ..., converges as n goes to infinity to the individual rational and feasible payoffs of the one shot game, whenever the bound on one of the two automata sizes is polynomial or subexponential in n and both, the length of the game and the bounds of the automata sizes are at least n. A special case of such result justifies cooperation in the finitely repeated prisoner¹s dilemma, without departure from strict utility maximization or complete information, but under the assumption that there are bounds (possibly very large) to the complexity of the strategies that the players may use.

Skewed Generalized Error Distribution of Financial Assets and Option Pricing

This article provides a mathematical and empirical investigation of the reasons for the presence of skewness and kurtosis in financial data. The results indicate that this phenomenon is triggered by higher-order moment dependencies in the data, such as asymmetric and conditional volatility. Moreover, the article develops and tests successfully a skewed extension of the generalized error distribution (SGED), which is then used to model European call option prices. Under the standard assumptions of risk neutrality, normality of log-returns, and absence of arbitrage opportunities, the SGED model yields as special cases several well-known models for pricing options on stocks, stock indices, currencies, and currency futures.

Some Flexible Parametric Models for Partially Adaptive Estimators of Econometric Models

This paper discusses three families of flexible parametric probability density functions: the skewed generalized t, the exponential generalized beta of the second kind, and the inverse hyperbolic sin distributions. These families allow quite flexible modeling the first four moments of a distribution and could be considered in modeling a wide variety of economic problems. We illustrate their use in a simple regression model with a simulation study that demonstrates that the use of the flexible distributions may result in significant efficiency gains relative to more conventional regression procedures, such as ordinary least squares or least absolute deviations regression, without a suffering from a large efficiency loss when errors are Gaussian.

Skewness and the Relation Between Risk and Return

The relationship between risk and return has been one of the most important and extensively investigated issues in the financial economics literature. The theoretical results predict a positive relation between the two. Nevertheless, the empirical findings so far have been contradictory. Evidence presented in this paper shows that these contradictions are the result of negative skewness in the distribution of portfolio excess return and the fact that the estimation of intertemporal asset pricing models are based on symmetric log-likelihood specifications.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2201 . This paper was accepted by Jerome Detemple, finance .

Robust estimation with flexible parametric distributions: estimation of utility stock betas

The distributions of stock returns and capital asset pricing model (CAPM) regression residuals are typically characterized by skewness and kurtosis. We apply four flexible probability density functions (pdfs) to model possible skewness and kurtosis in estimating the parameters of the CAPM and compare the corresponding estimates with ordinary least squares (OLS) and other symmetric distribution estimates. Estimation using the flexible pdfs provides more efficient results than OLS when the errors are non-normal and similar results when the errors are normal. Large estimation differences correspond to clear departures from normality. Our results show that OLS is not the best estimator of betas using this type of data. Our results suggest that the use of OLS CAPM betas may lead to erroneous estimates of the cost of capital for public utility stocks.

Beta Estimation with Stock Return Outliers: The Case of U.S. Pharmaceutical Companies

Efficient estimation of the equity cost of public corporations is an essential component of computing the required rate of return of real investment projects, and therefore the basis for a rational investment policy. The accepted methodology relies on the CAPM model to define the return risk premium, and the OLS method to estimate the beta risk coefficient required for calculating the premium. This study challenges the use of the OLS method for this task by demonstrating its vulnerability to the impact of stock return outliers caused by large, unpredictable, company-specific events. That impact is verified on a sample of U.S. pharmaceutical companies by comparing the OLS estimation performance with that of our proposed method based on Huber’s Robust M (HRM) estimator, a related statistical method that follows a mixed return model identifying regular and outlier return components. Using the HRM-estimated beta as a benchmark, we demonstrate that (1) outliers can substantially bias the OLS beta, (2) the bias is negatively correlated with company size, and (3) the size of the bias is often moderated but not eliminated by extending the estimation period. The latter finding suggests that a robust method like HRM is preferable where estimators ought to represent the behavior of the majority of historical data despite the presence of outliers. The risk of trusting the OLS beta is especially high when estimation must rely on a small sample.