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Correlation Related Scholarly Compositions
See also:
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News,
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Books,
or
Correlation Home Page.
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| Table of Contents:
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Beyond Correlation: Extreme
Co-movements Between Financial Assets
by Roy Mashal & Assaf Zeevi
Columbia University
June 21, 2002
Abstract
This paper investigates the potential for extreme co-movements
between financial assets by directly testing the underlying
dependence structure .In particular, a t-dependence structure,
derived from the Student t distribution, is used as a proxy t
test for this extremal behavior. Tests in three different
markets (equities, currencies, and commodities) indicate that
extreme co-movements are statistically significant.
Moreover, the “correlation-based ”Gaussian dependence structure,
underlying the multivariate Normal distribution, is rejected
with negligible error probability when tested against the t
-dependence alternative. The economic significance of these
results is illustrated via three examples: co-movements across
the G5 equity markets; portfolio value-at-risk calculations;
and, pricing credit derivatives.
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Correlation Analysis of Financial
Contagion: What One Should Know Before Running a Test
by Giancarlo Corsetti, Marcello Pericoli, & Massimo Sbracia
European University Institute & Bank of Italy
April, 2001
Abstract
This paper builds a general test of contagion in financial
markets based on bivariate correlation analysis - a test that
can be interpreted as an extension of the normal correlation
theorem. Contagion is defined as a structural break in the data
generating process of rates of return. Using a factor model of
returns as theoretical framework, we nest leading contributions
in the literature as special cases of our test...
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Correlation & Dependence in Risk
Management: Properties & Pitfalls
by Paul Embrechts, Alexander McNeil, and Daniel Straumann
July, 1999
Abstract
Modern risk management calls for an understanding of stochastic
de-
pendence going beyond simple linear correlation. This paper
deals with the static
(non-time-dependent) case and emphasizes the copula
representation of depen-
dence for a random vector. Linear correlation is a natural
dependence measure
for multivariate normally and, more generally, elliptically
distributed risks but
other dependence concepts like comonotonicity and rank
correlation should also
be understood by the risk management practitioner...
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The Empirical Relationship
between Average Asset Correlation, Firm Probability of Default
and Asset Size
by Jose A. Lopez
Economic Research Department - Federal Reserve Bank of San
Francisco
June 17, 2002
Abstract
The asymptotic single risk factor (ASRF) approach is a
simplified framework for
determining regulatory capital charges for credit risk and has
become an integral part of how credit risk capital requirements
are to be determined under the second Basel Accord. Within this
approach, a key regulatory parameter is the average asset
correlation. In this paper, we examine the empirical
relationship between the average asset correlation, firm
probability of default and firm asset size measured by the book
value of assets by imposing the ASRF approach within the KMV
methodology for determining credit risk capital requirements.
Using data from year-end 2000, credit portfolios consisting of
U.S., Japanese and European firms are analyzed. The empirical
results suggest that average asset correlation is a decreasing
function of probability of default and an increasing function of
asset size. When compared with the average asset correlations
proposed by the Basel Committee on Banking Supervision in
November 2001, the empirical average asset correlations further
suggest that accounting for firm asset size, especially for
larger firms, may be important. In conclusion, the empirical
results suggest that a variety of factors may impact average
asset correlations within an ASRF framework, and these factors
may need to be accounted for in the final calculation of
regulatory capital requirements for credit risk.
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Evaluating "Correlation
Breakdowns" During Periods of Market Volatility
by Mico Loretan & William B. English
Board of Governers of the Federal Reserve System
February, 2000
Abstract
Financial market observers have noted that during periods of
high market volatility, correlations between asset prices can
differ substantially from those seen in quieter markets. For
example, correlations among yield spreads were substantially
higher during the fall of 1998 than in earlier or later periods.
Such differences in correlations have been attributed either to
structural breaks in the underlying distribution of returns or
to "contagion" across markets that occurs only during periods of
market turbulence...
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On Default Correlation: A Copula
Function Approach
by David X. Li
The RiskMetrics Group
April, 2000
Abstract
This paper studies the problem of default correlation. We first
introduce a random variable called “time-until-default” to
denote the survival time of each defaultable entity or financial
instrument, and define the default correlation between two
credit risks as the correlation coefficient between their
survival times. Then we argue why a copula function approach
should be used to specify the joint distribution of survival
times after marginal distributions of survival times are derived
from market information, such as risky bond prices or asset swap
spreads...
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A Unified Approach to Testing for
Serial Correlation in Stock Returns
by Matthew P. Richardson & Tom Smith
New York University & The Australian National University
July, 1994
Abstract
This article provides a unified approach for testing serial
correlation in stock returns. We describe a general class of
statistics which are linear combinations of consistent
estimators of autocorrelations. As special cases, we show that
this class captures many of the statistics studied in the recent
finance and macroeconomics literature...
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Volatility and Cross Correlation
Across Major Stock Markets
by Latha Ramchand & Raul Susmel
University of Houston - Department of Finance
December 4, 1997
Abstract
Several papers have documented the fact that correlations across
major stock markets are higher when markets are more volatile -
this is done by comparing unconditional correlations over
sub-periods or by using conditional correlations that are time
varying. In this paper we examine the relation between
correlation and variance in a conditional time and state varying
framework. We use a switching ARCH (SWARCH) technique that does
two things.
One, it enables us to model variance as state varying. Two, a
bivariate SWARCH model allows us to go from conditional variance
to state varying covariances and correlations and hence test for
differences in correlations across variance regimes. We find
that the correlations between the U.S. and other world markets
are on average 2 to 3.5 times higher when the U.S. market is in
a high variance state as compared to a low variance regime. We
also find that, compared to a GARCH framework, the portfolio
choices resulting from our SWARCH model lead to higher Sharpe
ratios.
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