By Gustavo M. de Athayde, Renato G. Flôres Jr. (auth.), Christian L. Dunis (eds.)

ISBN-10: 1461369746

ISBN-13: 9781461369745

ISBN-10: 1461543894

ISBN-13: 9781461543893

Advances in Quantitative Asset Management comprises chosen articles which, for the main half, have been awarded on the `Forecasting monetary Markets' convention. `Forecasting monetary Markets' is a global convention on quantitative finance that's held in London in could each year. considering that its inception in 1994, the convention has grown in scope and stature to turn into a key overseas assembly aspect for these drawn to quantitative finance, with the participation of prestigious educational and study associations from worldwide, together with significant primary banks and quantitative fund managers.
The editor has selected to be aware of advances in quantitative asset administration and, for that reason, the papers during this publication are equipped round significant issues: advances in asset allocation and portfolio administration, and modelling probability, go back and correlation.

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We comment briefly on the use of such a univariate Student model in section 7 and 8. 7. EMPIRICAL STUDY The empirical study is based on monthly returns data of S&P500 stocks from February 1993 to April 1998 inclusive. Returns are measured in US$ terms. The stocks used were those securities present in the S&P500 at the end of April 1998 and which had been in the index continuously since February 1993. This gave a set of 454 stocks. For consistency, the market portfolio was taken to be a capitalisation weighted index based on these 454 securities.

In particular, we use vector notation where it is appropriate. The letter K, with appropriate subscripts, is used to denote normalising constants in the definition of various probability density functions. 2. GENERAL BACKGROUND AND THE MULTIVARIATE STUDENT MODEL The underlying model, on which most of modem fmance in conventionally based, is to assume that the returns on a set of fmancial assets over a single, but unspecified, time period follow a multivariate normal distribution. If the returns on N assets: {Ri, i = 1, 2, ...

While it is always true that the expected value of the error term Ei must be zero if the CAPM itself is true, it is not necessarily the case that this model will satisfy the statistical assumptions of regression that are a necessary prerequisite to estimating betas using ordinary least squares (OLS). To use OLS it is necessary that the error terms are normally distributed and are independent of market return. It is well known that departures from these assumptions can seriously reduce the accuracy of estimates computed using OLS.

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Advances in Quantitative Asset Management by Gustavo M. de Athayde, Renato G. Flôres Jr. (auth.), Christian L. Dunis (eds.)

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