2,088 research outputs found
How do Regimes Affect Asset Allocation?
International equity returns are characterized by episodes of high volatility and unusually high correlations coinciding with bear markets. We develop models of asset returns that match these patterns and use them in asset allocation. First, the presence of regimes with different correlations and expected returns is difficult to exploit within a framework focused on global equities. Nevertheless, for all-equity portfolios, a regime-switching strategy dominates static strategies out-of-sample. Second, substantial value is added when an investor chooses between cash, bonds and equity investments. When a persistent bear market hits, the investor switches primarily to cash. There are large market timing benefits because the bear market regimes tend to coincide with periods of relatively high interest rates.
Risk, Return and Dividends
We characterize the joint dynamics of dividends, expected returns, stochastic volatility, and prices. In particular, with a given dividend process, one of the processes of the expected return, the stock volatility, or the price-dividend ratio fully determines the other two. For example, together with dividends, the stock volatility process fully determines the dynamics of the expected return and the price-dividend ratio. By parameterizing one or more of expected returns, volatility, or prices, common empirical specifications place strong, and sometimes counter-factual, restrictions on the dynamics of the other variables. Our relations are useful for understanding the risk-return trade-off, as well as characterizing the predictability of stock returns.
Do Demographic Changes Affect Risk Premiums? Evidence from International Data
We examine the link between equity risk premiums and demographic changes using a very long sample over the twentieth century for the US, Japan, UK, Germany and France, and a shorter sample covering the last third of the twentieth century for fifteen countries. We find that demographic variables significantly predict excess returns internationally. However, the demographic predictability found in the US by past studies for the average age of the population does not extend to other countries. Pooling international data, we find that, on average, faster growth in the fraction of retired persons significantly decreases risk premiums. This demographic predictability of risk premiums is strongest in countries with well-developed social security systems and lesser-developed financial markets.
Do demographic changes affect risk premiums? Evidence from international data
We examine the link between equity risk premiums and demographic changes using a very long sample over the whole twentieth century for the US, Japan, UK, Germany and France, and a shorter sample covering the last third of the twentieth century for fifteen countries. We find that demographic variables significantly predict excess returns internationally. However, the demographic predictability found in the US by past studies for the average age of the population does not extend to other countries. Pooling international data, we find that, on average, faster growth in the fraction of retired persons significantly decreases risk premiums. This demographic predictability of risk premiums is stronger for countries with well-developed social security systems and lesser-developed financial markets. JEL Classification: G12, G15, J10, P46demography, international predictability, Population aging, risk premiums, social security
A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables
This paper describes the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, we investigate how macro variables affect bond prices and the dynamics of the yield curve. The setup accommodates higher order autoregressive lags for the macro factors. The macro variables are augmented by traditional unobserved term structure factors. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed. Models that incorporate macro factors forecast better than traditional term structure models with only unobservable factors. Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.
How to Discount Cashflows with Time-Varying Expected Returns
While many studies document that the market risk premium is predictable and that betas are not constant, the dividend discount model ignores time-varying risk premiums and betas. We develop a model to consistently value cashflows with changing risk-free rates, predictable risk premiums and conditional betas in the context of a conditional CAPM. Practical valuation is accomplished with an analytic term structure of discount rates, with different discount rates applied to expected cashflows at different horizons. Using constant discount rates can produce large mis-valuations, which, in portfolio data, are mostly driven at short horizons by market risk premiums and at long horizons by time-variation in risk-free rates and factor loadings.
Testing Conditional Factor Models
Using nonparametric techniques, we develop a methodology for estimating conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The tests can be performed for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.
Downside Risk and the Momentum Effect
Stocks with greater downside risk, which is measured by higher correlations conditional on downside moves of the market, have higher returns. After controlling for the market beta, the size effect and the book-to-market effect, the average rate of return on stocks with the greatest downside risk exceeds the average rate of return on stocks with the least downside risk by 6.55% per annum. Downside risk is important for explaining the cross-section of expected returns. In particular of the profitability of investing in momentum strategies can be explained as compensation for bearing high exposure to downside risk.
What does the yield curve tell us about GDP growth?
A lot, including a few things you may not expect. Previous studies find that the term spread forecasts GDP but these regressions are unconstrained and do not model regressor endogeneity. We build a dynamic model for GDP growth and yields that completely characterizes expectations of GDP. The model does not permit arbitrage. Contrary to previous findings, we predict that the short rate has more predictive power than any term spread. We confirm this finding by forecasting GDP out-of-sample. The model also recommends the use of lagged GDP and the longest maturity yield to measure slope. Greater efficiency enables the yield-curve model to produce superior out-of-sample GDP forecasts than unconstrained OLS at all horizons.
Downside Risk
Economists have long recognized that investors care differently about downside losses versus upside gains. Agents who place greater weight on downside risk demand additional compensation for holding stocks with high sensitivities to downside market movements. We show that the cross-section of stock returns reflects a premium for downside risk. Specifically, stocks that covary strongly with the market when the market declines have high average returns. We estimate that the downside risk premium is approximately 6% per annum. The reward for bearing downside risk is not simply compensation for regular market beta, nor is it explained by coskewness or liquidity risk, or size, book-to-market, and momentum characteristics.
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