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Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 . This paper was accepted by Wei Jiang, finance.

We propose a direct and robust method for quantifying the variance risk premium on financial assets. We show that the risk-neutral expected value of return variance, also known as the variance swap rate, is well approximated by the value of a particular portfolio of options. We propose to use the difference between the realized variance and this synthetic variance swap rate to quantify the variance risk premium. Using a large options data set, we synthesize variance swap rates and investigate the historical behavior of variance risk premiums on five stock indexes and 35 individual stocks.

We argue that one reason why emerging economies borrow short term is that it is cheaper than borrowing long term. This is especially the case during crises, as during these episodes the relative cost of long-term borrowing increases. We construct a unique database of sovereign bond prices, returns, and issuances at different maturities for 11 emerging economies from 1990 to 2009 and present a set of new stylized facts. On average, these countries pay a higher risk premium on long-term than on short-term bonds. During crises, the difference between the two risk premia increases and issuance shifts towards shorter maturities. To illustrate our argument, we present a simple model in which the maturity structure is the outcome of a risk-sharing problem between an emerging economy subject to rollover crises and risk-averse international investors.

This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for jumps in prices and modest evidence for jumps in volatility based on model fit. The evidence points toward economically and statistically significant jump risk premia, which are important for understanding option returns.

This article shows that the \"risk premium\" shock in Smets and Wouters (2007) can be interpreted as a structural shock to the demand for safe and liquid assets such as short-term U.S. Treasury securities. Several implications of this interpretation are discussed.

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time-varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset-specific instruments. The estimator uses simple weighted two-pass cross-sectional regressions, and we show its consistency and asymptotic normality under increasing cross-sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no-arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi-period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time-invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four-factor model capturing market, size, value, and momentum effects.

Volatility is a very important variable in financial research, and so is the volatility risk premium. This article constructs upward, downward and overall volatility risk premiums based on the daily data of Shanghai 50 ETF options and the 5-minute high-frequency data of the Shanghai 50 Index from February 9, 2015 to February 28, 2020. The quantile regression method is used to predict the return of the Shanghai 50 Index in the next 14 days, 30 days, 60 days, 90 days, 180 days, 270 days, and 360 days. Our study finds that the 0.05 quantile has the best prediction effect, and the overall volatility risk premium is better than the upward and downward volatility risk premiums in most of the time, which shows overall risk premium has more information than others.

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.

In this paper, utilizing the Chinese context, we investigate whether and how the regional gambling culture affects bond risk premiums. Using a lottery-based measure of the local gambling culture, our empirical results show that this culture significantly increases the risk premium of local bonds via an increase in corporate risk preferences, including operating, investment and internal control risks. Our findings are robust to various sensitivity tests, including gambling crime measures of the gambling culture and endogeneity tests. In addition, we find that regional crackdowns on gambling, legal environment and corporate information quality effectively all restrain the bond risk premium caused by the local gambling culture. Finally, we find that only the investor-pay credit rating can identify the bond investment risk caused by the local gambling culture, while bonds issued by firms located in high gambling areas are more prone to default.

We study asset prices and portfolio choice with overlapping generations, where the young disregard history to learn from own experience. Disregarding history implies less precise estimates of output growth, which in equilibrium leads the young to increase their investment in risky assets after positive returns, that is, they act as trend chasers. In equilibrium, the risk premium decreases after a positive shock and, therefore, trend chasing young agents lose wealth relative to old agents who behave as contrarians. Consistent with findings from survey data, the average belief about the risk premium in the economy relates negatively to future excess returns and is smoother than the true risk premium.