Re-examination of the historical equity risk premium in Australia

In light of the ongoing debate over the value of the equity risk premium, its increasing use in the regulatory setting, and the impact of dividend imputation on the premium, this paper presents a timely new look at the historical equity risk premium in Australia, and provides an improved understanding of the historical record. We document concerns about data quality that become increasingly important the further back in time one looks. In particular, there are sufficient question marks over the quality of data prior to 1958 to warrant any estimates based thereon to be treated with caution. Accordingly, we present a new set of estimates of the historical equity risk premium corresponding to periods of increasing data quality but of decreasing sample size. Relative to bonds (bills), the equity premium has averaged 6.3 per cent (6.8 per cent) per annum over 1958–2005, which is a period of relatively good data quality. Together with other results in the paper, the findings reveal a historical estimate that is substantially less than widely cited historical studies would otherwise indicate. We reconcile prior evidence through documenting a dividend adjustment that has typically been overlooked. We also provide estimates that incorporate an adjustment for imputation credits.     

Displacement risk and asset returns

We study asset-pricing implications of innovation in a general-equilibrium overlapping-generations economy. Innovation increases the competitive pressure on existing firms and workers, reducing the profits of existing firms and eroding the human capital of older workers. Due to the lack of inter-generational risk sharing, innovation creates a systematic risk factor, which we call “displacement risk.” This risk helps explain several empirical patterns, including the existence of the growth-value factor in returns, the value premium, and the high equity premium. We assess the magnitude of displacement risk using estimates of inter-cohort consumption differences across households and find support for the model.     

The importance of the volatility risk premium for volatility forecasting

In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major energy markets. Using regression models and statistical loss functions, we find compelling evidence to suggest that the risk premium adjusted implied volatility significantly outperforms other models, including its unadjusted counterpart. Our main finding holds for different choices of volatility estimators and competing time-series models, underlying the robustness of our results.     

经济周期、前景理论与BHS资产定价模型修正

行为资产定价研究引起了国内外的广泛关注,但由于现实资本市场中诸多异象的存在,导致人们对经典资产定价模型的质疑。经济周期波动会对投资者的理性产生显著影响,而非理性投资可能引发金融危机。据此,引入经济周期这一独特视角,剖析前景理论对资产定价的作用机理;提出相关假设,对BHS模型进行修正,以适应经济周期影响下的行为资产定价问题研究。     

Disappointment aversion and the equity premium puzzle: new international evidence

Drawing upon the seminal study of Ang, Bekaert, and Liu [2005. “Why Stock May Disappoint?” Journal of Financial Economics 76 (3): 471–508], we incorporate disappointment aversion (DA, that is, aversion to outcomes that are worse than prior expectations) within a simple theoretical portfolio-choice model. Based on the results of this model, we then empirically address the portfolio allocation problem of an investor who chooses between a risky and a risk-free asset using international data from 19 countries. Our findings strongly support the view that DA leads investors to reduce their exposure to the stock market (i.e. DA significantly depresses the portfolio weights on equities in all cases considered). Overall, our study shows that in addition to risk aversion, DA plays an important role in explaining the equity premium puzzle around the world.     

Cross‐region and cross‐sector asset allocation with regimes

Cross‐region and cross‐sector asset allocation decisions are one of the most fundamental issues in international equity portfolio management. Equity returns exhibit higher volatilities and correlations, and lower expected returns, in bear markets compared to bull markets. However, static mean–variance analysis fails to capture this salient feature of equity returns. We accommodate the nonlinearity of returns using a regime switching model across both regions and sectors. The regime‐dependent asset allocation potentially adds value to the traditional static mean–variance allocation. In addition, optimal allocation across sectors provide greater benefits compared to international diversification, which is characterized by higher returns, lower risks, lower correlations with the world market and a higher Sharpe ratio.     

Extreme downside risk and market turbulence

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali et al. [Is there an intertemporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 2009, 44, 883–909] with a Markov switching mechanism. We show that the risk-return relationship identified by Bali et al. (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility.     

Relationship between franking credits and the market risk premium: a comment

This paper examines two arguments presented in Gray and Hall (2006). First, that the generally used estimate of 0.06 for the market risk premium within the Officer version of the capital asset pricing model (CAPM) and the generally used estimate of 0.50 for the parameter ‘gamma’ within the Officer framework are jointly inconsistent with evidence concerning the market risk premium in the standard version of the CAPM. Second, that the first two of these parameter estimates are also jointly inconsistent with the observed cash dividend yield on the Australian market. To resolve these problems, Gray and Hall recommend setting gamma to zero. The present paper shows that the first argument does not account for the fact that imputation induces a reduction in the market risk premium as defined in the standard version of the CAPM. The present paper also shows that both arguments identify a problem that characterizes only parts of the Officer framework, and these parts are not generally used in Australia. Therefore, rather than suggesting that gamma should be zero, Gray and Hall's analysis identifies parts of the Officer framework that should be avoided.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.