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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 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.

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.

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.

This paper exploits the concept of expectation dependence to propose an alternative representation of the consumption-based capital asset pricing model (C-CAPM). While the first-degree expectation dependence (FED) drives the C-CAPM’s riskiness for a risk-averse investor, the second-degree expectation dependence (SED) is required to account for the downside risk faced by a prudent investor. Theoretical and empirical assessments reveal that the expectation dependence-based C-CAPM can realistically match equity and variance risk premia. The consumption SED risk emerges as a fundamental source of uncertainty driving asset prices.

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 paper uses a tractable stochastic integrated‐assessment model to analyze the influence of climate change on asset returns across time and maturity. Quasi‐analytical, or recursive, formulas allow to price various long‐dated assets, including fixed‐income products, derivatives, and equities. We find that climate risks will increasingly drive down long‐term risk‐free yields, reducing them by about 30 basis points by the end of the century. This decline reflects weaker growth and increased uncertainty, leading to a rise in precautionary savings. We illustrate the concept of climate risk premiums by examining model‐implied prices of long‐term assets vulnerable to sea level rise or temperatures. Climate risk premiums are particularly sensitive to damage assumptions.

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.

This paper introduces the ex-ante estimation of the variance risk premium. The novel methodology proposed is applied to forecast variance risk premium in energy markets, capturing the future degree of aversion of investors towards energy variance risks. We analyze the ex-ante variance risk premium of two energy indices, XLE and USO, during the period that spans from 2011 to 2022, and compare them to that of the SPX, the benchmark for the equity market. In the computation of the ex-ante variance risk premium, simple GARCH and Markov-switching GARCH models are exploited to forecast the realized variance, while variance swap rates are retrieved from the volatility indices VXXLE, OVX, and VIX of the three market indices. We find that the ex-ante variance risk premium succeeds to forecast the imminent periods of financial distress empirically detected in the abrupt surges and plunges of the ex-post variance risk premium. In particular,USO shows higher magnitudes of the variance risk premium than XLE and SPX, predicting that investors require on average higher premiums to bear oil variance risks.

We find that several recently proposed consumption-based models of stock returns, when evaluated using an optimal set of managed portfolios and the associated model-implied conditional moment restrictions, fail to capture key features of risk premiums in equity markets. To arrive at these conclusions, we construct an optimal Generalized Method of Moments (GMM) estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a set of priced risk factors, and we show that there is an optimal choice of managed portfolios to use in testing a null model against a proposed alternative generalized SDF.