The Dynamic Relations between Market Returns and Two Types of Risk with Business Cycles

Published date01 August 2014
Date01 August 2014
The Financial Review 49 (2014) 593–618
The Dynamic Relations between Market
Returns and Two Types of Risk with
Business Cycles
Xiaoquan Jiang
Florida International University
Bong-Soo Lee
Florida State University
We examine the dynamic relations among market returns, market (MV), and idiosyn-
cratic (IV) around business cycles. Compared to the conventional view, which treats MV and
IV separately, we first find that excess return on the market anticipates negative MV and IV,
suggesting market return’s role as an economic indicator, with the relation stronger in reces-
sions. Second, IV helps predict positive MV, mainly in early part of recessions, suggesting a
dynamic evolution from IV to MV. Third, MV helps predict negative IV, suggesting MV may
substitute IV to some extent.
Keywords: market return, market risk, idiosyncratic risk, business cycle
JEL Classifications: C32, C58, E32, G12
Corresponding author: Florida International University, 11200 SW 8th Street, RB 203B, Miami, FL
33199; Phone: (305) 348-7910; Fax: (305) 348-4245; E-mail:
We would like to thank Bonnie Van Ness (the editor) and two anonymous referees for constructive
and detailed suggestions. Helpful comments were received from the seminar participants at Florida
International University.All remaining shortcomings are our own responsibility.
C2014 The Eastern Finance Association 593
594 X. Jiang and B.-S. Lee/The Financial Review 49 (2014) 593–618
1. Introduction
Prior studies have examined the time series movements of market returns, mar-
ket volatility (MV), idiosyncratic volatility (IV), and macroeconomic activity. These
variables fluctuate over time, particularly over businesscycles (Officer, 1973; Black,
1976; Christie, 1982; French, Schwert and Stambaugh, 1987; Schwert, 1989; Camp-
bell, Lettau, Malkiel and Xu, 2001). However, prior studies tend to treat (MV) and
(IV) separately, and there is little study on the dynamic relations among these vari-
ables in different stages of business cycles. In this paper, we conduct analysis on
the dynamic relations among these variables, particularly around business cycles, to
provide new insights into the stock market variationsand business cycles and to better
understand recent economic recessions.
This paper is motivated by three factors. First, the traditional macroeconomic
business cycle literature tries to explain the persistent aggregate fluctuations ob-
served in macroeconomic time series. However, more recent literature shows that
independent sector-specific disturbances can also have significant aggregate effects.
For example, Horvath (1998, 2000) presents a multi sector dynamic general equilib-
rium model of business cycles with a distinctive feature: aggregate fluctuations are
driven by independent sectoral shocks. His model illustrates that sectoral shocks with
limited interaction among other sectors, characterized by a sparse input-use matrix,
tend to lead to greater aggregate volatility since it reduces substitution possibilities
in production.
Pastor and Veronesi (2009) show that the nature of the risk associated with new
technologies changes over time. Initially, this risk is mostly idiosyncratic due to the
small scale of production and a low probability of a large-scale adoption. The risk
remains idiosyncratic for the technologies that are never adopted on a large scale.
For the technologies that are ultimately adopted, however,the risk gradually changes
from idiosyncratic to systematic. As the probability of adoption increases, the new
technology becomes more likely to affect the old economy, and the representative
agent’swealth, so systematic risk in the economy increases. These studies suggest that
there is a dynamic relation between idiosyncratic and systematic risks, and between
each of the two types of risks and the aggregate economic activity, in particular,
around business cycles. Chun, Kim and Morck (2011) show that U.S. firms’ stock
return volatility over time was driven mainly by a rise and fall in the firm-specific,
rather than systematic, component of volatility. They find evidence supporting the
hypothesis that firm heterogeneity, reflected in firm-specificvolatility, rises as a new
general purpose technology (GPT) propagates across the economy and then ebbs
once the GPT is widespread.
Second, recessions tend to be triggered (or initiated) by some idiosyncratic
(sectoral) shocks. For example, the oil crisis was followed by the recessions in early
1970s and early 1980s, the savings and loans crisis was followed by the recession in
1990, the technology stock bubble burstwas followed by the recession in 2001, and the
recent subprime mortgage crisis and financial crisis (Lehman Brother bankruptcy)

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