Determinants of market beta: the impacts of firm-specific accounting figures and market conditions

This article examines and extends research on the relation between the capital asset pricing model market beta, accounting risk measures and macroeconomic risk factors. We employ a beta decomposition approach that nests competing models with different business risk proxies and allows to frame cross-model comparison. Because model tests require estimated independent variables resulting in measurement error, we empirically estimate three comparable model specifications with instrumental variable estimators and for the first time provide thorough instrument diagnostics in this setting. Correcting for the heretofore neglected weak instruments problem we find that growth risk (i.e., the risk of firm sales variations that are inconsistent with the market wide trends), is the business risk that explains cross-sectional variations in market beta best.     

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.     

Corrections for Trading Frictions in Multivariate Returns

When observed stock returns are obtained from trades subject to friction, it is known that an individual stock's beta and covariance are measured with error. Univariate models of additive error adjustment are available and are often applied simultaneously to more than one stock. Unfortunately, these multivariate adjustments produce non-positive definite covariance and correlation matrices, unless the return sample sizes are very large. To prevent this, restrictions on the adjustment matrix are developed and a correction is proposed, which dominates the uncorrected estimator. The estimators are illustrated with asset opportunity set estimates where daily returns have trading frictions.     

The market's perception of the riskiness of large U.S. Bank commercial letters of credit

This article improves upon the market discipline studies of commercial letters of credit (CLC) by employing two new capital market tests, in addition to traditional beta and equity risk tests. Option pricing models have been used to calculate implied asset risk. Two implied asset risk measures have been used to examine the riskiness of commercial letters of credit. The results indicate that stockholders view CLCs as reducing bank risk, and debt holders are indifferent about CLCs. A policy conclusion of these findings is that additional capital requirements of CLCs for large commercial banks may be inappropriate.     

The world ex ante risk premium: an empirical investigation

A number of recent papers have focused on testing the linearity restrictions implied by international asset pricing models. The tests, however, have not addressed an additional restriction implied by the models; namely, that the risk premium on the world portfolio is positive. This study provides a direct assessment of this restriction. The evidence indicates that the ex ante world market risk premium can be negative. The results are robust to market proxies that are hedged and unhedged with respect to currency risk. Subperiod analysis indicates that the rejection of the positive risk premium restriction is driven by the first half of the sample period.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

THIN TRADING AND THE ESTIMATION OF BETAS: THE EFFICACY OF ALTERNATIVE TECHNIQUES

Infrequent trading induces biased estimates of the beta risk coefficient. This paper reports on the efficacy of approaches that seek to correct for this bias and documents the extent of thin trading among New Zealand securities. Parameter estimates free of the thin-trading bias are obtained. These are compared with estimates obtained using ordinary least squares (OLS) applied in the conventional manner to nonsynchronous data, with and without bias-correcting procedures. OLS beta estimates are found to be less biased, more efficient, and as consistent when compared with Dimson or Scholes-Williams estimators. Lower beta estimates are associated with lower trading frequencies.     

Systematic Risk and Empirical Research

We show here that risky asset returns generating processes stated in terms of factors which include both accounting and non-accounting based measures of risk (e.g. book to market ratios) imply, under fairly standard regularity conditions, that the Sharpe-Lintner-Black asset pricing model beta is a 'sufficient' statistic in the sense that it captures all important attributes of the returns generating process in a single number. We then derive the parametric relationship between betas based on inefficient index portfolios and betas based on the market or tangency portfolio. We demonstrate that the relationship between risky asset expected returns and betas computed on the basis of inefficient index portfolios is both consistent with the predictions of the Capital Asset Pricing Model and the multi-factor asset pricing models of Fama and French (1992, 1993, 1995 and 1996). The 'trick' is to realise that inefficient index portfolios are composed of the market portfolio and a collection of inefficient but self financing 'kernel' or 'arbitrage' portfolios. It then follows that there is a perfect linear cross sectional relationship between risky asset expected returns, betas based on inefficient index portfolios and the arbitrage portfolios. Hence, if we happen to stumble across variables that span the same subspace as the vectors representing the arbitrage portfolios, it is easy to create the illusion that risky asset expected returns depend on variables other than 'beta'.     

On Stable Factor Structures in the Pricing of Risk: Do Time-Varying Betas Help or Hurt?

There is now considerable evidence suggesting that estimated betas of unconditional capital asset pricing models (CAPMs) exhibit statistically significant time variation. Therefore, many have advocated the use of conditional CAPMs. If we succeed in capturing the dynamics of beta risk, we are sure to outperform constant beta models. However, if the beta risk is inherently misspecified, there is a real possibility that we commit serious pricing errors, potentially larger than with a constant traditional beta model. In this paper we show that this is indeed the case, namely that pricing errors with constant traditional beta models are smaller than with conditional CAPMs.     

Asset liquidity,business risk,and beta

Extant literature posits that because of leverage, equity beta estimates from a single factor capital asset pricing model based on an equity-only market index are biased. We show analytically that this leverage bias is intimately related to the firm's asset structure per se, the firm's asset liquidity (i.e., cash holdings) and business risk. This is mainly because riskless cash holdings and risky real assets jointly determine the relevant risk for asset pricing. We empirically confirm that asset liquidity and business risk can marginally explain the leverage bias in the cross-section of stocks returns.     

Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques

This paper investigates the performance of three different approaches to modelling time-variation in conditional asset betas: GARCH models, the extended market model of Schwert and Seguin (1990) and the Kalman Filter algorithm. Using daily UK industry returns, we find the simple market model beta to be as efficient as the more complicated GARCH type models. However, the Kalman Filter algorithm incorporating a random walk parameterisation dominates all other models under the mean-square error criterion. Finally, we provide strong evidence that a combination of the methods under investigation may lead to considerably more powerful estimators of the time-variation in conditional beta.     

Are the Latent Variables in Time-Varying Expected Returns Compensation for Consumption Risk?

Multibeta asset pricing models are examined using proxies for economic state variables in a framework which exploits time-varying expected returns to estimate conditional betas. Examples include multiple consumption-beta models and models where asset returns proxy for the state variables. When the state variables are not specified, the tests indicate two or three time-varying expected risk premiums in the sample of quarterly asset returns. Conditional betas relative to consumption generate less striking evidence against the model than betas relative to asset returns, but both the consumption and the market variables fail to proxy for the state variables.     

Is co-skewness a better measure of risk in the downside than downside beta?: Evidence in emerging market data

We define three measure of systematic co-skewness risk in a downside framework by extending three downside beta risk measures in the literature. In pricing models in a downside framework it may be sufficient to include a risk measure that accounts for co-semi-variance or co-semi-skewness and not both. Downside risk is appropriate when returns distribution is skewed—a common feature in emerging markets. A cross-sectional analysis provides evidence that downside co-skewness is a better explanatory variable of emerging market monthly returns than downside beta. Our conclusions remain largely unchanged when the analysis is subjected to various robustness checks.     

The performance of model based option trading strategies

This paper analyzes returns to trading strategies in options markets that exploit information given by a theoretical asset pricing model. We examine trading strategies in which a positive portfolio weight is assigned to assets which market prices exceed the price of a theoretical asset pricing model. We investigate portfolio rules which mimic standard mean-variance analysis is used to construct optimal model based portfolio weights. In essence, these portfolio rules allow estimation risk, as well as price risk to be approximately hedged. An empirical exercise shows that the portfolio rules give out-of-sample Sharpe ratios exceeding unity for S&P 500 options. Portfolio returns have no discernible correlation with systematic risk factors, which is troubling for traditional risk based asset pricing explanations.     

隐性杠杆约束、流动性风险和投资者情绪

本文以2003—2019年间开放式主动管理型的股票型和偏股型基金为样本,以持仓占比为权重估算基金投组中A股的总市场风险暴露,检验结果表明,该序列上升反映了基金面临的隐性杠杆约束收紧,刻画了市场的弱流动性。内在逻辑在于,流动性收紧时,投资者难以通过融资直接增加杠杆,更倾向于重仓持有高市场风险头寸的股票而间接实现杠杆。本文发现隐性杠杆约束所刻画的风险在股票或基金收益截面上的无条件定价基本失效,而条件定价则依赖于低市场情绪与弱流动性。分解基金持股的敞口,进一步发现,因中小盘基金在流动性收紧时具有更强的流动性偏好,其持股的市场风险头寸能够更敏锐地捕捉到弱流动性风险。     

Forecasting daily variability of the S&P 100 stock index using historical,realised and implied volatility measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper, we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First, we consider unobserved components (UC-RV) and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility (SV) models and generalised autoregressive conditional heteroskedasticity (GARCH) models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard & Poor's 100 (S&P 100) stock index series for which trading data (tick by tick) of almost 7 years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its p-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts.     

Testing asset pricing models with changing expectations and an unobservable market portfolio

When the assumption of constant risk premiums is relaxed, financial valuation models may be tested, and risk measures estimated without specifying a market index or state variables. This is accomplished by examining the behavior of conditional expected returns. The approach is developed using a single risk premium asset pricing model as an example and then extended to models with multiple risk premiums. The methodology is illustrated using daily return data on the common stocks of the Dow Jones 30. The tests indicate that these returns are consistent with a single, time-varying risk premium.     

Limited investor attention,relative fundamental strength,and the cross-section of stock returns

We explore the underlying reasons for the apparent mispricing of firms based on fundamental information. We document that a relative fundamental strength strategy that buys (sells) firms with strong (weak) fundamentals is highly profitable for up to three years. The results cannot be explained by either price or earnings momentum, are robust to risk adjustments based on standard asset pricing models, and survive a battery of robustness tests. The strategy also works better among small firms, as well as firms with low analyst coverage and a high probability of informed trading. Our empirical findings support the hypotheses of limited investor attention and informed trading.     

系统性定价偏误:中国A股盈余公告后的价格漂移研究

行为金融学的研究表明,投资者情绪引起的定价偏误在各支股票之间具有相关性,从而构成市场上的系统性定价偏误。基于2003年6月至2009年6月中国A股月度交易数据,本文考察了系统性定价偏误与盈余公告后漂移(PEAD)之间的关系。研究结果显示,中国股票市场上的PEAD现象可能由系统性定价偏误引致,因此,将捕捉系统性定价偏误的偏误定价因子引入定价模型能够提升模型对于PEAD的解释力。经过对定价模型调整,季度盈余公告后6个月买人持有异常收益在经济及统计意义上不显著。这一方面表明中国股票市场上存在由投资者情绪造成的系统性定价偏误,另一方面也为盈余公告后漂移的产生原因提供了新的解释。     

A best choice among asset pricing models? The Conditional Capital Asset Pricing Model in Australia

We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that the inclusion of the market for human capital does not save the concept of the time‐independent market beta (it remains insignificant). We find support for the role of a small‐minus‐big factor in pricing the cross‐section of returns and find grounds to disagree with Jagannathan and Wang's argument that this factor proxies for misspecified market risk.