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 time-varying 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.
Complexity in Structured Finance GHENT, ANDRA C.; TOROUS, WALTER N.; VALKANOV, ROSSEN I.
The Review of economic studies,
03/2019, Volume:
86, Issue:
2 (307)
Journal Article
Peer reviewed
We study complexity in the market for securitized products, a market at the heart of the financial crisis of 2007–9. The complexity of these products rose substantially in the years preceding the ...financial crisis. We find that securities in more complex deals default more and have lower realized returns. The worse performance is economically meaningful: a one standard deviation increase in complexity represents an 18% increase in default on AAA securities. However, yields of more complex securities are not higher indicating that investors did not perceive them as riskier. Our results are consistent with complexity obfuscating security quality.
ABSTRACT
The response of corporate bond credit spreads to three exogenous macro shocks—oil supply, investment‐specific technology, and government spending—is large, significant, and a mirror image of ...macroeconomic activity. This countercyclicality is driven largely by credit risk premia and translates into significant return predictability. Equity risk premia exhibit similar responses, providing external validity. Information rigidities and leverage play a key role in the transmission of the shocks. Since causal evidence linking macro shocks to credit markets is scarce and recent work highlights the real effects of credit fluctuations, our findings contribute to understanding the joint dynamics of credit markets and the macroeconomy.
We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients ...of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP- Compustat data set, exploiting the size, value, and momentum anomalies.
We consider various mixed data sampling (MIDAS) regressions to predict volatility. The regressions differ in the specification of regressors (squared returns, absolute returns, realized volatility, ...realized power, and return ranges), in the use of daily or intra-daily (5-min) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare regressions across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily
realized power (involving 5-min absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms models based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5
min) data does not improve volatility predictions. Finally, daily lags of 1–2 months are sufficient to capture the persistence in volatility. These findings hold both in- and out-of-sample.
There is a risk-return trade-off after all Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen
Journal of financial economics,
06/2005, Volume:
76, Issue:
3
Journal Article
Peer reviewed
Open access
This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly ...variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.
We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome. Specifically, our modeling approach allows for MIDAS ...stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find strong evidence that the introduction of MIDAS effects in the volatility equation leads to improved in-sample and out-of-sample density forecasts. Our results also suggest that model combination schemes assign high weight to MIDAS-in-volatility models and produce consistent gains in out-of-sample predictive performance.
Do industries lead stock markets? Torous, Walter; Valkanov, Rossen; Hong, Harrison
Journal of financial economics,
02/2007, Volume:
83, Issue:
2
Journal Article
Peer reviewed
We investigate whether the returns of industry portfolios predict stock market movements. In the US, a significant number of industry returns, including retail, services, commercial real estate, ...metal, and petroleum, forecast the stock market by up to two months. Moreover, the propensity of an industry to predict the market is correlated with its propensity to forecast various indicators of economic activity. The eight largest non-US stock markets show remarkably similar patterns. These findings suggest that stock markets react with a delay to information contained in industry returns about their fundamentals and that information diffuses only gradually across markets. PUBLICATION ABSTRACT
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.
Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the ...variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate—that is, the rent-price ratio in commercial real estate—captures fluctuations in expected returns for apartments and retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on offices are also time-varying. As implied by the present value relation, cap rates marginally forecast office rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data-generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land-use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market and Treasury bills, as well as commercial real estate.
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