A Markov Model of Heteroskedasticity, Risk, and Learning in the Stock Market

Turner, Christopher M.; Startz, Richard; Nelson, Charles R.

doi:10.3386/w2818

Cited by 154 publications

(173 citation statements)

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“…We use the variance rather than the standard deviation as this is the customary specification in the risk-return literature(Merton 1980, French, Schwert and Stambaugh 1987, Engle, Lilien and Robins 1987, Ghysels, Santa-Clara and Valkanov 2004. Using the standard deviation gives very similar results (omitted for brevity).24 For example, seeCampbell (1987),French, Schwert and Stambaugh (1987),Turner, Startz and Nelson (1989),Baillie and DeGennaro (1990),Campbell and Hentschel (1992),Glosten, Jagannathan and Runkle (1993) andWhitelaw (1994).…”

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confidence: 99%

The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

2004

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We estimate the cross-sectional dispersions of returns and growth in rents for commercial real estate using data on U.S. metropolitan areas over the sample period 1986 to 2002. The cross-sectional dispersion of returns is a measure of the risk faced by commercial real estate investors. We document that for apartments, offices, industrial and retail properties, the cross-sectional dispersions are timevarying. Interestingly, their time series fluctuations can be explained by macroeconomic variables such as the term and credit spreads, inflation, and the short rate of interest. The cross-sectional dispersions also exhibit an asymmetrically larger response to negative economics shocks, which may be attributable to credit channel effects impacting the availability of external debt financing to commercial real estate investments. Finally, we find a statistically reliable positive relation between commercial real estate returns and their cross-sectional dispersion, suggesting that idiosyncratic fluctuations are priced in the commercial real estate market. IntroductionTime variation in equity risk and its relation to the state of the economy has been widely documented at both the firm and market levels (see, for example, Bollerslev, Chou, and Kroner (1992), Ghysels, Harvey, and Renault (1996), Campbell, Lettau, Malkiel, and Xu (2001) and Goyal and Santa-Clara (2003) among others). Comparatively little, however, is known about risk dynamics in the commercial real estate market and their link to prevailing economic conditions. * UCLA Anderson School and SAFE Center University of Verona, 110 Westwood Plaza, Los Angeles, CA 90095-1481, e-mail: alberto.plazzi.2010@anderson.ucla 1This scarcity of empirical facts is not due to a lack of investor interest. To the contrary, the commercial real estate market is large and represents a significant fraction of total U.S. wealth.For example, at year end 2004, estimates put the value of the U.S. commercial real estate market at $8 trillion as compared to the U.S. stock market's value of almost $18 trillion.We investigate risk dynamics in the commercial real estate market by evaluating the cross-sectional dispersions of commercial real estate returns and rental growth rates across U.S. metropolitan areas.The cross-sectional dispersion of commercial real estate returns is a more appropriate measure of the risk inherent in commercial real estate because it captures idiosyncratic fluctuations associated with this asset that are unlikely to be diversified away. Unlike equities, the commercial real estate market is characterized by the buying and selling of individual assets, the value of each property representing a non-trivial share of most investors' portfolios. This being the case, the volatility of aggregate real estate indices will not accurately reflect the risk exposure of a typical investor holding commercial real estate. Also, as commercial real estate tends to be affected by geographic, demographic, urban, and other local economic factors, the dispersion of commercial real es...

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The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

2004

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“…For example, suppose that the semi-ranges are random walks: the semi-ranges are last period's values plus independent drawings from identically distributed zero-mean random variables, e(t) and fit). 'Turner, Startz, and Nelson (1989) provide integrated evidence on the two relations.…”

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confidence: 90%

Variance, Return, and High‐low Price Spreads

Lin

Rozeff

1994

J of Financial Research

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We report three new findings that rely upon the high‐low price range as an estimate of stock return variance. The predictability of variance is associated with persistence in high prices and with correlated shocks to high and low prices. Excess stock returns are positively related to anticipated variance and inversely related to unanticipated variance. Lagged squared residuals in GARCH(1,1) models have no incremental explanatory power in the presence of forecasts of conditional volatility generated from high‐low price spread models.

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“…Haugen, Talmor, and Torous (1991) and Campbell and Hentschel (1992) also show a positive correlation between risk premia and concurrent changes in expected returns of stocks that ultimately drop in price. However, other studies find instead a negative correlation between volatility and returns (Poterba and Summer, 1988;Turner, Startz, and Nelson, 1989;Gloston, Jagannathan, and Runkle, 1993). See Nam, Pyun, and Avard (2001) for further discussion.…”

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confidence: 95%

Risk-Adjusted Long-Term Contrarian Profits: Evidence from Non-S&P 500 High-Volume Stocks

Wongchoti

Pyun

2005

Financial Review

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Can trading volume help unravel the long-term overreaction puzzle? With portfolios of non-S&P 500 NYSE stocks, we show that (1) both the high-and low-volume (abnormal volume) contrarian portfolios earn a much higher market-adjusted excess return than the normal-volume contrarian portfolio, (2) however, when leverage-induced risk is factored in, excess returns from contrarian portfolios with normal-and low-volume stocks are insignificant, (3) only excess returns from high-volume contrarian stocks are significant and cannot be explained by the timevarying risk and return framework, and (4) such high-volume, risk-adjusted excess returns arise mainly from winner (glamour) stocks.We are grateful to an anonymous referee and to Steve Ferris (the Advisory Editor) for their helpful comments. We also acknowledge the useful comments and support received from

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A Markov Model of Heteroskedasticity, Risk, and Learning in the Stock Market

Cited by 154 publications

References 1 publication

The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

Variance, Return, and High‐low Price Spreads

Risk-Adjusted Long-Term Contrarian Profits: Evidence from Non-S&P 500 High-Volume Stocks

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