Conditional Equity Premium Research Articles

Multifactor conditional equity premium model: Evidence from China's stock market

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

Conditional Equity Premium and Aggregate Corporate Investment

AbstractWe document a strong negative relation between aggregate corporate investment and conditional equity premium estimated from direct stock market risk measures. Consistent with the investment‐based asset pricing model, the comovement with conditional equity premium fully accounts for aggregate investment's market return predictive power. Similarly, conditional equity premium is a significant determinant of classic Tobin's q measure, although q has much weaker explanatory power for aggregate investment possibly because of its measurement errors. Moreover, the positive relation between aggregate investment and investor sentiment documented in previous studies reflects the fact that both variables correlate closely with conditional equity premium.

Searching for the Best Conditional Equity Premium Model

Searching for the Best Conditional Equity Premium Model

Aggregate Distress Risk and Equity Returns

The aggregate default probability is significantly priced in equities because of its close relation with uncertainty. Ceteris paribus, the aggregate default probability positively predicts stock market returns, and loadings on its changes correlate negatively with the cross-section of expected stock returns. These findings are consistent with multifactor models in which aggregate uncertainty is a determinant of the conditional equity premium and innovations in aggregate uncertainty are a systematic risk factor. By contrast, credit spreads have negligible explanatory power for expected stock returns. Contrary to the conventional wisdom, credit spreads are poor proxies for aggregate credit risk.

When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance

<ns2:p>We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic and may be potentially disconnected. We solve a representative investor's optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in equilibrium. Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially high uncertainty that is disconnected from lower volatility, rather than for facing volatility as traditionally assumed. Incorporating the possibility of a disconnect between volatility and uncertainty significantly improves portfolio performance, over and above the performance obtained by conditioning on volatility only.</ns2:p>

The Risk-Return Relation Puzzle

A positive stock market variance-return relation is the key building block of extant rational-expectations asset pricing models. Empirical evidence is elusive despite forty years of intensive academic research. The conjecture is also resoundingly refuted by the late 1990s’ dotcom bubble featured by sharp increases in both market prices and variance. Gao and Martin (2021), Lochstoer and Muir (2021), and Nagel and Xu (2021) interpret empirical findings as evidence of irrational expectations. Ai, Han, and Xu (2021) attribute the weak or negative risk-return relation to learning about economic fundamentals with imperfect signals. Guo, Lin, and Pai (2021) argue for two types of fundamental variance that have opposite effects on conditional equity premium and market prices. More research is needed to explain the risk-return relation puzzle prevalent in both U.S. and Asia-Pacific markets.

When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance

We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic and may be potentially disconnected. We solve a representative investor's optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in equilibrium. Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially high uncertainty that is disconnected from lower volatility, rather than for facing volatility as traditionally assumed. Incorporating the possibility of a disconnect between volatility and uncertainty significantly improves portfolio performance, over and above the performance obtained by conditioning on volatility only.

Procyclicality of the comovement between dividend growth and consumption growth

Duffee (2005) shows that the amount of consumption risk (i.e., the conditional covariance between market returns and consumption growth) is procyclical. In light of this “Duffee Puzzle,” I empirically demonstrate that the conditional covariance between dividend growth (i.e., the immediate cash flow part of market returns) and consumption growth is (1) procyclical and (2) a consistent source of procyclicality in the puzzle. Moreover, I solve an external habit formation model that incorporates realistic joint dynamics of dividend growth and consumption growth. The procyclical dividend-consumption comovement entails two new procyclical terms in the amount of consumption risk via cash flow and valuation channels, respectively. These two procyclical terms play an important role in generating a realistic magnitude of consumption risk. In contrast to extant habit formation models, the conditional equity premium no longer increases monotonically when a negative consumption shock arrives because it might lower the amount of risk while increasing the price of risk.

Analyst Forecast Dispersion and Market Return Predictability: Does Conditional Equity Premium Play a Role?

Prior studies found that analyst forecast dispersion predicts future market returns. Some prior studies attribute this predictability to the short-sale constraints in the market according to the overpricing theory. Using the U.S. data from 1981 to 2014, we find that the return predictive power of aggregate dispersion only exists prior to 2005. The investor sentiment index, as a proxy of short-sale constraints used by many studies, can only explain the dispersion effect prior to 2005. The investor sentiment index and other proxies such as institutional ownership and put options cannot explain the significant weakening of the dispersion effect after the global financial crisis. We argue that the dispersion-return relation is partly driven by the correlation between dispersion and conditional equity premium. Our evidence suggests that the short-sale constrained stocks do not experience a higher dispersion effect, which is contrary to what the overpricing theory predicts.

Uncovering China's Stock Market Risk Return Relation: Crazy Casino Punters or Risk Averse Investors?

We investigate the risk-return relation using data from China. Because it is segmented from the rest of the global financial market, China’s market allows us to shed new light on this important relation of which previous empirical evidence is inconclusive. Market variance and scaled market prices jointly forecast excess market returns. The relation with conditional equity premium is positive for market variance and negative for scaled market prices, although the two predictors correlate positively with each other. These findings are similar to those documented in the U.S. market; they suggest a multi-factor conditional equity premium model.

On the Stock Market Variance-Return or Price Relations: A Tale Of Two Variances

Stock market variance-return or price relations are sometimes negative and sometimes positive. We explain these puzzling findings using a model with two (fear and euphoria) variances. Conditional equity premium depends positively on fear variance and negatively on euphoria variance. Market prices, which correlate negatively with discount rates, decrease with fear variance and increase with euphoria variance. Because market variance is the sum of fear and euphoria variances, its relation to conditional equity premium or market prices can be negative or positive, depending on the relative importance of two variances. Our empirical results support model's key assumptions and main implications.

Equity premium estimates from economic fundamentals under structural breaks

This article compares three estimates of the conditional equity premium using dividend and earnings growth rates to measure the expected rate of capital gain. The premia are estimated using a theory-informed Bayesian model that admits structural breaks. The equity premium fell from 8.16% in 1951 to 1.15% in 1985. Approximately half of this decline was reversion of a high conditional premium to the long run mean and the remainder resulted from a decline in the expected stock return. The decline in the expected stock return was largely driven by the Fed Accord (1951) and the Fed's ‘monetarist policy experiment’ (1979–1982).

Conditional Equity Premium and Aggregate Investment: Is the Stock Market a Sideshow?

We document a strong negative relation between aggregate corporate investment and conditional equity premium estimated from direct stock market risk measures. Consistent with the investment-based asset pricing model, the comovement with conditional equity premium fully accounts for aggregate investment’s market return predictive power. Similarly, conditional equity premium is a significant determinant of classic Tobin’s q measure, although q has much weaker explanatory power for aggregate investment possibly because of its measurement errors. Moreover, the positive relation between aggregate investment and investor sentiment documented in previous studies reflects the fact that both variables correlate closely with conditional equity premium.

What Drives the Dispersion Effect: Investor Sentiment or Conditional Equity Premium?

What Drives the Dispersion Effect: Investor Sentiment or Conditional Equity Premium?

Market Illiquidity and Conditional Equity Premium

We examine the time-series relation between aggregate bid-ask spreads and conditional equity premium. We document that average marketwide relative effective bid-ask spreads forecast aggregate market returns only when controlling for average idiosyncratic variance. This control allows us to document the otherwise elusive relation between illiquidity and returns. The reason is that idiosyncratic variance correlates positively with spreads but has a negative effect on conditional equity premium, causing an omitted variable bias. Our results are robust to standard return predictors, alternative illiquidity measures, and out-of-sample tests. These findings are important because they provide strong support for the literature's conjecture that marketwide liquidity is an important asset pricing risk factor.

What Drives Aggregate Investment: Investor Sentiment or Time-Varying Equity Premium?

In Cochrane’s (1991) investment-based asset pricing model, aggregate investment is driven by conditional equity premium; Arif and Lee (2014), however, find that it is primarily influenced by investor sentiment or gauges of irrational exuberance. We reconcile these conflicting views by showing that the positive relation between aggregate investment and investor sentiment documented by Arif and Lee (2014) reflects the fact that both variables move closely with conditional equity premium. Our results cast doubt to (1) the conjecture that stock market mispricing is an important driver of aggregate investment and (2) the conventional wisdom that investor sentiment measures irrationality.

Funding Shortfall Risk and Asset Prices in General Equilibrium

Institutional investors, such as pensions and insurers, are typically constrained to hold enough wealth to be able to make their contractually promised payments to fund beneficiaries. This creates an additional risk in the economy, namely the risk of funding-shortfall. We seek to explore the optimal asset allocation strategies for institutions facing this risk, and its effects on asset prices. The constraint introduces two distinct regions in the economy, characterising unconstrained and constrained regions, with the possibility of transitioning from the constrained to unconstrained regime, which leads to a two-factor asset pricing model. The funding-shortfall risk increases the conditional equity premium and Sharpe ratio, which evolve counter-cyclically, but decreases the conditional volatility of equity returns, which evolves cyclically. The constrained institution may optimally an under-diversified portfolio, and simultaneously increases its demand for the riskfree and higher-risk assets relative to medium-risk assets, inducing a bubble-like behaviour in the prices of higher-risk assets. The dynamics of contractually promised payments affect the dynamics of conditional moments of asset return distributions, and may lead to predictability. The term structure of interest rates is predominantly upward sloping, but can change shape upon shocks to the growth rate of aggregate dividend relative to the growth rate of minimum payouts. Implied volatility exhibits a time-varying volatility smile, and the term structure of implied volatility can be both upward or downward sloping, depending on the relative growth rates of aggregate dividends and promised institutional payouts. These results may have implications for the design of optimal regulatory requirements.

Good Jumps, Bad Jumps, and Conditional Equity Premium

We uncover signicant eects of jump risk on conditional equity premium. Realized volatility due to negative or \bad (positive or \good) jumps in stock market prices predicts a rising (falling) near-term equity premium. The forecasting power of signed jump risk measures remains statistically signicant even when we control for variance risk premium that Drechsler and Yaron (2011) attribute to jump risk. Our novel empirical ndings are broadly consistent with an extended Drechsler and Yaron model that also allows stochastic volatility of volatility to aect variance risk premium and conditional equity premium as in Bollerslev, Tauchen, and Zhou (2009).

The conditional equity premium, cross-sectional returns and stochastic volatility

Bansal and Yaron (2004) demonstrate, by calibration, that the Consumption-based Capital Asset Pricing Model (CCAPM) can be rescued by assuming that consumption growth rate follows a stochastic volatility model. They show that the conditional equity premium is a linear function of conditional consumption and market return volatilities, which can be estimated handily by various Generalized Autoregressive Conditonal Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models. We find that conditional consumption and market volatilities are capable of explaining cross-sectional return differences. The Exponential GARCH (EGARCH) volatility can explain up to 55% variation of return and the EGARCH model augmented with cay^ — a cointegrating factor of consumption, labor income and asset wealth growth — greatly enhances model performance. We proceed to test another hypothesis: if Bansal and Yaron estimator is an unbiased estimator of true conditional equity premium, then the instrumental variables for estimating conditional equity premium should no longer be significant. We demonstrate that once the theoretical conditional risk premium is added to the model, it renders all instrumental variables redundant. Also, the model prediction is consistent with observed declining equity premium.

Good Jumps, Bad Jumps, and Conditional Equity Premium

Good Jumps, Bad Jumps, and Conditional Equity Premium