On the predictability of stock prices: A case for high and low prices

This paper contributes to technical analysis (TA) literature by showing that the high and low prices of equity shares are largely predictable only on the basis of their past realizations. Moreover, using their forecasts as entry/exit signals can improve common TA trading strategies applied on US equity prices. We propose modeling high and low prices using a simple implementation of a fractional vector autoregressive model with error correction (FVECM). This model captures two fundamental patterns of high and low prices: their cointegrating relationship and the long-memory of their difference (i.e., the range), which is a measure of volatility.     

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are easily estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is rather robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, but they are smaller with multivariate approaches, including factor-based ones.     

Regulatory Contracts and Cost Efficiency: Stochastic Frontier Evidence from the Italian Local Public Transport

The main objective of this paper is to investigate the way subsidization mechanisms affect the cost efficiency of public transit systems, taking into account the role played by the environmental characteristics of each network. A cost frontier model is estimated for a seven-year panel of 44 Italian transit companies run under two different regulatory schemes (cost-plus or fixed-price), using the approach proposed by Kumbhakar et al. (1991), Huang and Liu (1994) and Battese and Coelli (1995). The main evidence is that, given network characteristics, transit operators with high-powered incentive contracts (fixed-price subsidies) exhibit lower distortions from the minimum costs. Environmental conditions (network speed levels) also have a significant impact on inefficiency differentials and influence the efficacy of incentive regulation. Overall, these results highlight a scope for transport policy to increase X-efficiency. Furthermore, they stress the importance of incentive theory and modern regulatory economics for the production analysis of regulated utilities.     

ON THE (POLITICAL) ORIGIN OF ‘CORPORATE GOVERNANCE’ SPECIES

Although economies, business practices and living standards have converged since WWII, corporate structures continue to differ among the advanced economies of the world. Looking at the diversity of corporate structures of large-sized firms around the world (and over time) would fascinate Charles Darwin. This work develops a critical review of the literature on political determinants of corporate governance through the Darwinian theory (including some Lamarckian aspects). As Darwin, in his work On the Origin of Species, explicates the diversity of species of tortoises, finches and iguanas of the Galapagos Islands, so Darwinism may contribute in understanding the origin and the persistence of corporate diversity. In particular, this paper takes into account politics-driven variations, their inheritances, and the subsequent selection of advantageous ‘corporate’ attributes.     

Forecasting large datasets with Bayesian reduced rank multivariate models

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large‐scale Bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two‐step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank Bayesian VAR of Geweke ( 1996 ). We find that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast and for key variables such as industrial production growth, inflation, and the federal funds rate. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the way to using large‐scale reduced rank models for empirical analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Thresholds,news impact surfaces and dynamic asymmetric multivariate GARCH

Dynamic Asymmetric Multivariate GARCH (DAMGARCH) is a new model that extends the Vector ARMA‐GARCH (VARMA‐GARCH) model of Ling and Mc Aleer (2003) by introducing multiple thresholds and time‐dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset‐specific shocks and common innovations by partitioning the multivariate density support. This article presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi‐maximum likelihood estimators. The article also presents an empirical example to highlight the usefulness of the new model.     

Foundations of neo-Bayesian statistics

We study an axiomatic model of preferences, which contains as special cases Subjective Expected Utility, Choquet Expected Utility, Maxmin and Maxmax Expected Utility and many other models. First, we give a complete characterization of the class of functionals representing these preferences. Then, we show that any such functional can be represented as a Choquet integral

where is the canonical mapping from the space of bounded Σ-measurable functions into the space of weak^*-continuous affine functions on a weak*-compact, convex set of probability measures on Σ. Conversely, any preference relation defined by means of such functionals satisfies the axioms of the model we study. Different properties of the capacity give rise to different models. Our result shows that the idea of Choquet integration is general enough to embrace all the models mentioned above. In doing so, it widens the range of applicability of well-known procedures in robust statistics theory such as the Neyman–Pearson lemma for capacities [P.J. Huber, V. Strassen, Minimax tests and the Neyman–Pearson lemma for capacities, Ann. Statist. 1 (1973) 251–263], Bayes' theorem for capacities [J.B. Kadane, L. Wasserman, Bayes' theorem for Choquet capacities, Ann. Statist. 18 (1990) 1328–1339] or of results like the Law of Large numbers for capacities [F. Maccheroni, M. Marinacci, A strong law of large numbers for capacities, Ann. Probab. 33 (2005) 1171–1178].     

A SCIENTIFIC CLASSIFICATION OF VOLATILITY MODELS

Abstract.  Modeling volatility, or predictable changes over time and space in a variable, is crucial in the natural and social sciences. Life can be volatile, and anything that matters, and which changes over time and space, involves volatility. Without volatility, many temporal and spatial variables would simply be constants. Our purpose is to propose a scientific classification of the alternative volatility models and approaches that are available in the literature, following the Linnaean taxonomy. This scientific classification is used because the literature has evolved as a living organism, with the birth of numerous new species of models.     

Forecast Bias and MSFE Encompassing

We show that the standard condition for MSFE encompassing is no longer valid when the forecasts to be compared are biased. We propose a simple modification of such a condition and of tests for its validity. The theoretical results are illustrated by an empirical example on inflation and deficit forecasts, key variables for the formulation of monetary and fiscal policy.     

Classic rational bubbles and representativeness

The aim of this work is to describe a model of representative bubbles with infinitely lived agents that is accessible to a general audience. In particular, we shall compare this formalization of behavioral bubble dynamics with the classic rational one. The key role of the transversality condition for rational models will be clear, and we will discuss the necessity of its fulfillment when agents are not rational and arbitrageurs are faced with limited arbitrage possibilities. We shall analyze in detail what kind of nonrationality is taken into account and how it triggers bubbly dynamics. It will be plain that representative bubbles can explain the presence of bubbles even when rational models are not able to detect them.