			Home About us Contact

Conditional Correlations (conditional + correlation)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	88%

Selected Abstracts

The intertemporal relationship between market return and variance: an Australian perspective

ACCOUNTING & FINANCE, Issue 3 2001
Warren G. Dean
In this paper we investigate the intertemporal relationship between the market risk premium and its conditional variance in an Australian setting. Using a bivariate EGARCH-M model combined with the dynamic conditional correlation (DCC) framework as proposed by Engle (2000), we find evidence of a positive relationship between the market risk premium and its variance and evidence of two distinct interest rate effects. Furthermore, while the bond market's own variance is not priced by investors, we find that the covariance between equity and bond markets is a significant risk factor that is priced in the market. [source]

Average conditional correlation and tree structures for multivariate GARCH models

JOURNAL OF FORECASTING, Issue 8 2006
Francesco Audrino
Abstract We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions without resorting to any variance reduction technique. We back-test the models on a six-dimensional exchange-rate time series using different goodness-of-fit criteria and statistical tests. We collect empirical evidence of their strong predictive power, also in comparison to alternative benchmark procedures.,,Copyright © 2006 John Wiley & Sons, Ltd. [source]

Estimation and hedging effectiveness of time-varying hedge ratio: Flexible bivariate garch approaches

THE JOURNAL OF FUTURES MARKETS, Issue 1 2010
Sung Yong Park
Bollerslev's (1990, Review of Economics and Statistics, 52, 5,59) constant conditional correlation and Engle's (2002, Journal of Business & Economic Statistics, 20, 339,350) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time-varying hedge ratios. In this study, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two (BGARCH) models: (i) adopting more flexible bivariate density functions such as a bivariate skewed- t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC-based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness-of-fit of the estimated models, but do not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:71,99, 2010 [source]

Dynamic hedging with futures: A copula-based GARCH model

THE JOURNAL OF FUTURES MARKETS, Issue 11 2008
Chih-Chiang Hsu
In a number of earlier studies it has been demonstrated that the traditional regression-based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula-based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in-sample and out-of-sample tests, with full flexibility in the distribution specifications, the copula-based GARCH models perform more effectively than other dynamic hedging models. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095,1116, 2008 [source]

Average conditional correlation and tree structures for multivariate GARCH models

Specification Analysis of Affine Term Structure Models

THE JOURNAL OF FINANCE, Issue 5 2000
Qiang Dai
This paper explores the structural differences and relative goodness-of-fits of affine term structure models (ATSMs). Within the family of ATSMs there is a trade-off between flexibility in modeling the conditional correlations and volatilities of the risk factors. This trade-off is formalized by our classification of N -factor affine family into N+ 1 non-nested subfamilies of models. Specializing to three-factor ATSMs, our analysis suggests, based on theoretical considerations and empirical evidence, that some subfamilies of ATSMs are better suited than others to explaining historical interest rate behavior. [source]

Cross-market correlations and transmission of information

THE JOURNAL OF FUTURES MARKETS, Issue 11 2002
Salim M. Darbar
We investigate characteristics of cross-market correlations using daily data from U.S. stock, bond, money, and currency futures markets using a new multivariate GARCH model that permits direct hypothesis testing on conditional correlations. We find evidence that arrival of information in a market affects subsequent cross-market conditional correlations in the sample period following the stock market crash of 1987, but there is little evidence of such a relationship in the precrash period. In the postcrash period, we also find evidence that the prime rate of interest affects daily correlations between futures returns. Furthermore, we find that conditional correlations between currency futures and other markets decline steeply a few months before the crash and revert to normal dynamics after the crash. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:1059,1082, 2002 [source]

Probabilistic observables, conditional correlations, and quantum physics

ANNALEN DER PHYSIK, Issue 7 2010
C. Wetterich
Abstract We discuss the classical statistics of isolated subsystems. Only a small part of the information contained in the classical probability distribution for the subsystem and its environment is available for the description of the isolated subsystem. The "coarse graining of the information" to micro-states implies probabilistic observables. For two-level probabilistic observables only a probability for finding the values one or minus one can be given for any micro-state, while such observables could be realized as classical observables with sharp values on a substate level. For a continuous family of micro-states parameterized by a sphere all the quantum mechanical laws for a two-state system follow under the assumption that the purity of the ensemble is conserved by the time evolution. The correlation functions of quantum mechanics correspond to the use of conditional correlation functions in classical statistics. We further discuss the classical statistical realization of entanglement within a system corresponding to four-state quantum mechanics. We conclude that quantum mechanics can be derived from a classical statistical setting with infinitely many micro-states. [source]