Abstract not found

We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analyze data on financial returns, which are notoriously heavy-tailed. Let pi denote the intractable posterior density that results when this regression model is combined with the standard non-informative prior on the unknown regression coefficients and scale matrix of the errors. Roughly speaking, the posterior is proper if and only if n ≥ d + k, where n is the sample size, d is the dimension of the response, and k is number of covariates. We provide a method of making exact draws from pi in the special case where n = d + k, and we study Markov chain Monte Carlo (MCMC) algorithms that can be used to explore pi when n> d+ k. In particular, we show how the Haar PX-DA technology studied in Hobert and Marchev (2008) can be used to improve upon Liu’s (1996) data augmentation (DA) algorithm. Indeed, the new algorithm that we introduce is theoretically superior to the DA algorithm, yet equivalent to DA in terms of computational complexity. Moreover, we analyze the convergence rates of these MCMC algorithms in the important special case where the regression errors have a Student’s t distribution. We prove that, under conditions on n, d, k, and the degrees of freedom of the t distribution, both algorithms converge at a geometric rate. These convergence rate results are important from a practical standpoint because geometric ergodicity guarantees the existence of central limit theorems which are essential for the calculation of valid asymptotic standard errors for MCMC based estimates.

Abstract Ornstein-Uhlenbeck models are continuous-time processes which have broad applications in finance as, e.g., volatility processes in stochastic volatility models or spread models in spread options and pairs trading. The paper presents a least squares estimator for the model parameter in a multivariate Ornstein-Uhlenbeck model driven by a multivariate regularly varying Lévy process with infinite variance. We show that the estimator is consistent. Moreover, we derive its asymptotic behavior and test statistics. The results are compared to the finite variance case. For the proof we require some new results on multivariate regular variation of products of random vectors and central limit theorems. Furthermore, we embed this model in the setup of a co-integrated model in continuous time. JEL Classifications: C13, C23

Investors and analysts are unable to predict stock price movements consistently so as to beat the market in informationally efficient markets. Still, concerted efforts are being made to earn abnormal returns discerning some anomalous pattern in the stock price movements. Also, the study ofsome structural changes in the market leading to, or removing some anomalous pattern in the stockprices, are ofinterest to investors and analysts. The present study was conceptualised to scrutinise whether anomalous patterns yield abnormal return consistentlyfor any specific day ofthe week even after introduction ofthe compulsory rolling settlement on Indian bourses. Three market series viz., BSE Sensex, Sand P CNX Nifty and Sand P CNX 5.00 were observed on daily basis for ten years viz., i) Pre-rolling settlementperiod, April 1997- December, 2001 and ii) Post-rolling settlement period, January 2002- March 2007 to discern evidences in this regard. Contrary to developed capital markets, the results reported in this study documented lowest (significant) Friday returns in the pre-rolling settlement period as credible evidence for the weekend effect. The findings recorded for post-rolling settlement period were in harmony with those obtained elsewhere in the sense that Friday returns were highest and those on Monday were the lowest. It implied that arbitrage opportunities existed (for different trade settlement cycle on two exchanges, BSE and NSE) have disappeared consequent to the rolling settlement. On the whole, the study noted stock markets moved more rationally and anomalous return pattern noticed earlier could not sustain, in the post rolling settlement period.

82 pages, 8 pictures, 30 tables, 15 appendices

The paper extends the option pricing model of Merlon (1973) with lime-varying volatility of the underlying security. We develop the theoretical option model. Time-varying volatility is constructed liy fitting a lime-polynomial to implied volatility values where the order of.lhc polynomial is approximated by the number of options considered. We then predict the option price one day forward and compare the results with the standard Black and Scholcs model. When applied to PT-SE 100 index European options the new model was found to be more accurate titan the Black and Scholcs.

Using the dollar-value that a mutual fund adds as the measure of skill, we find that the average mutual fund adds about $2 million per year and that this skill persists for as long as 10 years. We further document that investors recognize this skill and reward it by investing more capital with better funds. Better funds earn higher aggregate fees, and there is a strong positive correlation between current compensation and future performance.

1991 The contents of this paper are the author’s sole responsibility. They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members. Copyright Q 1991 Oxford Institute for Energy Studies All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any

None of the views expressed by the author in this article are necessarily shared by Lee Overlay Partners. Some aspects of active portfolio management We propose a framework for considering active portfolio management, consistent with existing literature in the single period environment, but extended to the multi-period environment that most practicing active managers operate in. We derive several active portfolio management results including the Generalised Law of Active Portfolio Management (a generalisation that accounts for correlations and varying information coefficients and volatilities), and the Theory of Active Portfolio Management that suggests that mean/second-moment optimisation is the best active portfolio management approach in a multi-period setting, not mean/variance. In developing these results we uncover some inappropriate practices, and several simplistic practices that are theoretically supported by our framework. 1 Some aspects of active portfolio management 1.

Engle’s footsteps range widely. His major contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and his later stunningly influential work on common trend modeling (cointegration) and volatility modeling (ARCH, short for AutoRegressive Conditional Heteroskedasticity). 2 More generally, Engle’s cumulative work is a fine example of best-practice applied time-series econometrics: he identifies important dynamic economic phenomena, formulates precise and interesting questions about those phenomena, constructs sophisticated yet simple econometric models for measurement and testing, and consistently obtains results of widespread substantive interest in the scientific, policy, and financial communities. Although many of Engle’s contributions are fundamental, I will focus largely on the two most important: the theory and application of cointegration, and the theory and application of dynamic volatility models. Moreover, I will discuss much more extensively Engle’s volatility models and their role in financial econometrics, for several reasons. First, Engle’s Nobel citation was explicitly “for methods of analyzing economic time series with time-varying volatility (ARCH), ” whereas Granger’s was for “for methods of analyzing economic time series with common trends (cointegration). ” Second, the credit for 1 Prepared at the invitation of the Scandinavian Journal of Economics. Financial support from the