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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
Citations
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Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise†
Gerhard Rünstler,Karim Barhoumi,S. Benk,Riccardo Cristadoro,A. Den Reijer,Audrone Jakaitiene,P. Jelonek,António Rua,Karsten Ruth,C. Van Nieuwenhuyze +9 more
TL;DR: This article performed a large-scale forecast evaluation exercise to assess the performance of different models for the short-term forecasting of GDP, resorting to large datasets from ten European countries and found that factor models perform best and models that exploit monthly information outperform models that use purely quarterly data.
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Gold, Oil, and Stocks: Dynamic Correlations
TL;DR: In this paper, the authors employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012.
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Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification
TL;DR: In this article, the authors investigate long-run stock-bond correlation using a model that combines the dynamic conditional correlation model with the mixed-data sampling approach and allows long run correlation to be affected by macro-finance factors (historical and forecasts).
Journal ArticleDOI
Consumption, (Dis) aggregate wealth and asset returns
TL;DR: This paper showed that the residuals of the trend relationship among consumption, financial wealth, housing wealth and labor income (summarized by the variable cday ) should predict better U.S. and U.K. quarterly stock market returns than a variable like cay from Lettau and Ludvigson (2001), which considers aggregate wealth instead.
Hedges and Safe Havens - An examination of Stocks, Bonds, Oil, Gold and the Dollar
TL;DR: This paper investigated the return relations between major asset classes using data from both the US and the UK and found that gold can be regarded as a safe haven against exchange rates in both countries, highlighting its monetary asset role.
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.