Rossen I. Valkanov

Long-horizon regressions: theoretical results and applications

Abstract I use asymptotic arguments to show that the t-statistics in long-horizon regressions do not converge to well-defined distributions. In some cases, moreover, the ordinary least squares estimator is not consistent and the R2 is an inadequate measure of the goodness of fit. These findings can partially explain the tendency of long-horizon regressions to find “significant” results where previous short-term approaches find none. I propose a rescaled t-statistic, whose asymptotic distribution is easy to simulate, and revisit some of the long-horizon evidence on return predictability and of the Fisher effect.

MIDAS Regressions: Further Results and New Directions

We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper concludes with an empirical section where we provide further evidence and new results on the risk–return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.

The Presidential Puzzle: Political Cycles and the Stock Market

The excess return in the stock market is higher under Democratic than Republican presidencies: 9 percent for the value-weighted and 16 percent for the equal-weighted portfolio. The difference comes from higher real stock returns and lower real interest rates, is statistically significant, and is robust in subsamples. The difference in returns is not explained by business-cycle variables related to expected returns, and is not concentrated around election dates. There is no difference in the riskiness of the stock market across presidencies that could justify a risk premium. The difference in returns through the political cycle is therefore a puzzle.

Forecasting Stock Returns Under Economic Constraints

We propose a new approach to imposing economic constraints on time-series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: Non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates timevarying volatility in the predictive regression framework. Empirically, we find that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance. The Sharpe ratio constraint, in particular, results in considerable economic gains.

Forecasting Real Estate Prices

This chapter reviews the evidence of predictability in U.S. residential and commercial real estate markets. First, we highlight the main methodologies used in the construction of real estate indices, their underlying assumptions and their impact on the stochastic properties of the resultant series. We then survey the key empirical findings in the academic literature, including short-run persistence and long-run reversals in the log changes of real estate prices. Next, we summarize the ability of local as well as aggregate variables to forecast real estate returns. We illustrate a number of these results by relying on six aggregate indexes of the prices of unsecuritized (residential and commercial) real estate and REITs. The effect of leverage and monetary policy is also discussed.

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

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 time varying. 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.

Valuation in US commercial real estate

We consider a log-linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.

Exploiting Property Characteristics in Commercial Real Estate Portfolio Allocation

We use a parametric portfolio approach to estimate optimal commercial real estate portfolio policies. We do so using the NCREIF data set of commercial properties over the sample period 1984:Q2 to 2009:Q1. The richness of this extensive data set and the flexibility of the parametric portfolio approach allow us to consider: (i) a large cross-section of individual properties across various regions and property types; (ii) several property-specific conditioning variables, such as cap rates, leverage, value, and vacancy rates; and (iii) various macro-economic factors. Property-specific conditioning information is found to be economically important even for portfolios that are well-diversified across geographical regions and property types.

Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry

We use a quantile-based measure of conditional skewness or asymmetry of asset returns that is robust to outliers and therefore particularly suited for re calcitrant series such as emerging market returns. We study the following portfolio returns: developed markets, emerging markets, the world, and separately 73 countries. We find that the conditional asy mmetry of returns varies significantly over time. This is true even after taking into account condit ional volatility effects (GARCH) and unconditional skewness effects (TARCH) in returns. Interestingly, we find that the conditional asymmetry in developing countries is negatively correlated with that in emerging markets. This finding has implications for portfolio allocation, given th e fact that the correlation of the returns themselves has been historically high and is increasing. In contrast to conditional volatility fluctuations, which are hard to explain with macroeconomic fundamentals, we find a strong relationship between the conditional skewness and macroeconomic variables. Moreover, the negative relationship between conditional asymmetry across developed and emerging markets can be explained by macroeconomic fundamental factors in the cross-section, as both markets feature opposite responses to those fundamentals. The economic significance of the conditional asymmetry is also demonstrated in an international portfolio allocation set ting.

The Risk-Return Relationship and Financial Crises

The risk-return trade-off implies that a riskier investment should demand a higher expected return relative to the risk-free return. The approach of Ghysels, Santa-Clara, and Valkanov (2005) consisted of estimating the risk-return trade-off with a mixed frequency - or MIDAS - approach. MIDAS strikes a compromise between on the one hand the need for longer horizons to model expected returns and on the other hand to use high frequency data to model the conditional volatility required to estimate expected returns. Using the approach of Ghysels, Santa-Clara, and Valkanov (2005), after correcting a coding error pointed out to us, we find that the Merton model holds over samples that exclude financial crises, in particular the Great Depression and/or the subprime mortgage financial crisis and the resulting Great Recession. We find that a simple flight to safety indicator separates the traditional risk-return relationship from financial crises which amount to fundamental changes in that relationship. For those months or quarters we characterize as flight to safety we find there is no (i.e. neither negative nor positive) risk-return trade-off.