Cover

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Title Page, Copyright

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Contents

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pp. v-vi

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Introduction

Philip Hans Franses, Herman K. van Dijk

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pp. vii-viii

The Econometric Institute Lecture Series deals with topics in econometrics that have important policy implications. The lectures cover a wide range of topics and are not confined to any one area or subdiscipline. Leading international scientists in the fields of econometrics in which applications play a major role are invited to give three-day lectures on a topic to which they have contributed significantly. ...

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1 Correlation Economics

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pp. 1-14

Today there are almost three thousand stocks listed on the New York Stock Exchange. NASDAQ lists another three thousand. There is yet another collection of stocks that are unlisted and traded on the Bulletin Board or Pink Sheets. These U.S.-traded stocks are joined by thousands of companies listed on foreign stock exchanges to make up a universe of publicly traded equities. ...

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2 Correlations in Theory

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pp. 15-28

Correlations measure the linear relationships between two random variables. In this chapter we will discuss a variety of ways to measure correlations in particular settings and will then discuss more general measures of dependence. The standard definition introduced by Pearson emphasizes this linearity. ...

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3 Models for Correlation

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pp. 29-42

Since Engle (1982) introduced the idea that volatility could be modeled and forecast with univariate econometric time series methods, an enormous literature has developed that explores these methods to model multivariate covariance matrices. Probably the first attempts to estimate a multivariate GARCH model were by Engle et al. (1984) and by Bollerslev et al. (1988). ...

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4 Dynamic Conditional Correlation

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pp. 43-58

There are three general steps in the specification and estimation of a DCC model. First, the volatilities must be estimated to construct standardized residuals or volatility-adjusted returns. This is often called “DEGARCHING” the data. Then the quasi-correlations must be estimated in a dynamic fashion based on these standardized residuals. ...

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5 DCC Performance

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pp. 59-73

Our first task is to discuss a Monte Carlo experiment reported in Engle (2002a). In this case the true correlation structure is known. Several estimators can compete to approximate it. The data generating processes are deterministic time series processes that can be approximated by the conditional covariance estimators. ...

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6 The MacGyver Method

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pp. 74-79

The problem of estimating correlation matrices for large systems might appear to have been solved in previous chapters. However, there are three reasons to believe that we do not yet have a full solution. First, the evaluation of the log likelihood function requires inversion of matrices, Rt , which are full n × n matrices, for each observation. ...

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7 Generalized DCC Models

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pp. 80-87

The great advantage of DCC models is the parsimony of parameterization. For the simple mean-reverting DCC model with correlation targeting, there are only two unknown parameters in the correlation process, no matter how many variables are being modeled. ...

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8 FACTOR DCC

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pp. 88-102

To model large numbers of asset returns, the profession has always turned to factor models. This is a natural development as the investigator seeks to discover the small number of important factors that influence a large number of returns. ...

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9 Anticipating Correlations

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pp. 103-121

The goal of this book is to develop methods to anticipate correlations. Thus far, the book has developed descriptions of correlations and why they change as well as models that can track the changes. The task of anticipating correlations seems far more formidable. ...

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10 Credit Risk and Correlations

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pp. 122-129

One of the most interesting developments in the financial marketplace over the last decade has been the growth in the volume and diversity of credit derivatives. These are contracts that provide insurance against defaults and therefore allow investors to manage their credit risk. ...

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11 Econometric Analysis of the DCC Model

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pp. 130-136

In this chapter we turn to more rigorous econometric topics. The asymptotic properties of estimates of the DCC model are developed. The novel pieces are the analysis of correlation targeting and some alternatives, and the asymptotic distribution of DCC incorporating the two-step methodology and correlation targeting. ...

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12 Conclusions

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pp. 137-140

Financial decision making and risk analysis requires accurate and timely estimates of correlations today and how they are likely to evolve in the future. New methods for analyzing correlations have been developed in this book and compared with existing methods. ...

References

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pp. 141-150

Index

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pp. 151-154