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The Determinants of Stock and Bond Return Comovements
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In this article, the authors identify the economic factors employing structural and non-structural vector autoregressive models for economic state variables such as interest rates, (expected) inflation, output growth and dividend payouts.Abstract:
We study the economic sources of stock-bond return comovement and its time variation using a dynamic factor model. We identify the economic factors employing structural and non-structural vector autoregressive models for economic state variables such as interest rates, (expected) inflation, output growth and dividend payouts. We also view risk aversion, and uncertainty about inflation and output as additional potential factors. Even the best-fitting economic factor model fits the dynamics of stock-bond return correlations poorly. Alternative factors, such as liquidity proxies, help explain the residual correlations not explained by the economic models.read more
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The stock-bond nexus and investors’ behavior in mature and emerging markets: Evidence from long-term historical data
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The Real Explanation of Nominal Bond-Stock Puzzles
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References
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A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
ReportDOI
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Journal ArticleDOI
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.
TL;DR: In this article, the parameters of an autoregression are viewed as the outcome of a discrete-state Markov process, and an algorithm for drawing such probabilistic inference in the form of a nonlinear iterative filter is presented.
Posted Content
A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Journal ArticleDOI
Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.