Multifactor models and their consistency with the ICAPM

Can any multifactor model be interpreted as a variant of the Intertemporal CAPM (ICAPM)? The ICAPM places restrictions on time-series and cross-sectional behavior of state variables and factors. If a state variable forecasts positive (negative) changes in investment opportunities in time-series regressions, its innovation should earn a positive (negative) risk price in the cross-sectional test of the respective multifactor model. Second, the market (covariance) price of risk must be economically plausible as an estimate of the coefficient of relative risk aversion (RRA). We apply our ICAPM criteria to eight popular multifactor models and the results show that most models do not satisfy the ICAPM restrictions. Specifically, the “hedging” risk prices have the wrong sign and the estimates of RRA are not economically plausible. Overall, the Fama and French (1993) and Carhart (1997) models perform the best in consistently meeting the ICAPM restrictions. The remaining models, which represent some of the most relevant examples presented in the empirical asset pricing literature, can still empirically explain the size, value, and momentum anomalies, but they are generally inconsistent with the ICAPM.     

On the Use of Multifactor Models to Evaluate Mutual Fund Performance

We show that multifactor performance estimates for mutual funds suffer from systematic biases and argue that these biases are a result of miscalculating the factor premiums. Because the factor proxies are based on hypothetical stock portfolios and do not incorporate transaction costs, trade impact, and trading restrictions, the factor premiums are either over- or underestimated. We argue that factor proxies based on mutual fund returns rather than on stock returns provide better benchmarks to evaluate professional money managers.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

Nonlinear Pricing Kernels, Kurtosis Preference, and Evidence from the Cross Section of Equity Returns

This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and avoiding ad hoc specifications of factors or functional form. Our test results indicate that preference-restricted nonlinear pricing kernels are both admissible for the cross section of returns and are able to significantly improve upon linear single- and multifactor kernels. Further, the nonlinearities in the pricing kernel drive out the importance of the factors in the linear multi-factor model.     

Consumption-based capital asset pricing models: issues and controversies

This paper discusses the issues and controversies surrounding consumption-based capital asset pricing models (CCAPMs). While CCAPMs provide a chance to explain the phenomena observed in stock markets, their viability is jeopardized owing to the weak predictability of the equity premium and risk-free rate puzzles. Even given market frictions and market incompleteness, CCAPMs must test their validity constantly in the face of the formidable challenges of rival models. Measurement error with respect to time aggregation is also regarded as a major threat, causing the low volatility of consumption and eventually resulting in chaining itself to weak return predictability. In addition, the dual choice problem of portfolio and consumption rooted in CCAPMs guides us into how investors accumulate wealth through the financial market to reach the zenith of expected utility. This paper offers insights as well as understanding into the behavior of an agent and market phenomena in the context of a consumption-based economy.     

中国股票市场风险溢价研究

本文通过综合资产定价理论和实证文献研究结论,对1997年到2009年中国股市A股股票的风险溢价的截面差异作了详尽的实证研究。我们构造25个投资组合作为检验资产,进行Fama-MacBeth两步回归法,建立了基于市场风险溢价,账面市值比,盈利股价比,现金流股价比,投资资本比,工业增加值变化率以及回购利率和期限利差的八因素模型。我们的主要发现有以下三点:一是相对于Fama-French三因素模型,我们模型的实证解释力有显著提高;二是与过去的文献不同,我们发现回购利率和期限利差等债市指标对股市风险溢价的截面数据有显著解释能力;三是与基于投资的资产定价理论一致,我们发现投资比率和现金流股价比能显著反映我国股市的风险溢价。     

Portfolio performance of linear SDF models: an out-of-sample assessment

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.     

The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data

We develop a general form logarithmic vector multiplicative error model (log-vMEM). The log-vMEM improves on existing models in two ways. First, it is a more general form model as it allows the error terms to be cross-dependent and relaxes weak exogeneity restrictions. Second, the log-vMEM specification guarantees that the conditional means are non-negative without any restrictions imposed on the parameters. We further propose a multivariate lognormal distribution and a joint maximum likelihood estimation strategy. The model is applied to high frequency data associated with a number of NYSE-listed stocks. The results reveal empirical support for full interdependence of trading duration, volume and volatility, with the log-vMEM providing a better fit to the data than a competing model. Moreover, we find that unexpected duration and volume dominate observed duration and volume in terms of information content, and that volatility and volatility shocks affect duration in different directions. These results are interpreted with reference to extant microstructure theory.     

Comparative measures of performance for U.S.-based international equity mutual funds

This paper compares an international two-index model to an International Arbitrage Pricing Theory (IAPT) two-factor model to evaluate the performance of 37 U.S.-based international mutual funds over the 1985–1993 period. Results from the index model confirm prior research that international funds perform as well as the market proxy. In contrast, the IAPT model implies superior investment performance by the international funds. Moreover, the two models produce different relative performance rankings. Intertemporal comparisons of the models indicate that the multifactor IAPT model better reflects the international equity return-generating process.     

Chinese IPO activity,pricing, and market cycles

We examine the activity, pricing, and market cycles of 1,380 Chinese A share IPOs over the period 1991–2005 and find initial underpricing of 238%. The government restrictions on IPO offer price and quota allocation cause pricing structural breaks and attribute more than half of initial underpricing. A multifactor model that includes firm’s characteristics, excess demand for IPO shares, and the government restrictions explains cross-sectional initial returns, after controlling for industrial differences and stock market conditions. In addition, monthly IPO volume and average initial return are highly correlated. A VAR model indicates that initial return leads IPO volume by 6 months.     

A STATE-SPACE APPROACH TO ESTIMATE AND TEST MULTIFACTOR COX-INGERSOLL-ROSS MODELS OF THE TERM STRUCTURE

The objective of this paper is to estimate and test multifactor versions of the Cox-Ingersoll-Ross (CIR) model of the nominal term structure of interest rates. The proposed state-space approach integrates time series and cross-sectional aspects of the CIR model, is consistent with the underlying economic model, and can use information from all available points of the term structure. We recover estimates of the underlying factors that are consistent with the assumptions about the stochastic processes and compare them with factors obtained from standard factor analysis. We perform thorough diagnostic checking and thereby provide new evidence regarding conclusions about the adequacy of the CIR model. We present empirical results for U.S. Treasury market data. Although the specification of multifactor CIR models is sufficiently flexible for the shape of the term structure, we find strong evidence against the adequacy of the CIR model.     

Autoregressive stochastic volatility models with heavy-tailed distributions: A comparison with multifactor volatility models

This paper examines two asymmetric stochastic volatility models used to describe the heavy tails and volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student's t-distribution (ARSV-t), and the second is the multifactor stochastic volatility (MFSV) model. In order to estimate these models, the analysis employs the Monte Carlo likelihood (MCL) method proposed by Sandmann and Koopman [Sandmann, G., Koopman, S.J., 1998. Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics 87, 271–301.]. To guarantee the positive definiteness of the sampling distribution of the MCL, the nearest covariance matrix in the Frobenius norm is used. The empirical results using returns on the S&P 500 Composite and Tokyo stock price indexes and the Japan–US exchange rate indicate that the ARSV-t model provides a better fit than the MFSV model on the basis of Akaike information criterion (AIC) and the Bayes information criterion (BIC).     

Entry and regulation: evidence from health care professions

In many countries, pharmacies receive high regulated markups and are protected from competition through geographic entry restrictions. We develop an empirical entry model for pharmacies and physicians with two features: entry restrictions and strategic complementarities. We find that the entry restrictions have directly reduced the number of pharmacies by more than 50%, and also indirectly reduced the number of physicians by about 7%. A removal of the entry restrictions, combined with a reduction in the regulated markups, would generate a large shift in rents to consumers, without reducing the availability of pharmacies. The public interest motivation for the current regime therefore has no empirical support.     

Determinants and Value of Enterprise Risk Management: Empirical Evidence From the Literature

The development of an enterprise risk management (ERM) program enables companies to manage corporate risks in a holistic manner as opposed to the silo‐based perspective in traditional risk management frameworks. One main question in this regard is what factors drive the implementation of an ERM system in companies and whether ERM programs can actually create value once implemented. This article addresses these questions by conducting a comparative assessment of empirical evidence from the literature regarding the determinants of ERM and its value once implemented. In doing so we are able to illustrate differences in model specifications and the underlying data. Our literature study shows that particularly the company size and the level of institutional ownership are significantly positively related to the implementation of ERM in most empirical studies and, furthermore, that ERM generally has a (significant) positive impact on corporate value and performance (to a different extent and depending on the focus of the studies). However, geographic and/or industrial restrictions regarding the underlying data sets partly limit the generalization of the empirical results.     

Australian retail fund performance persistence

The present study extends the Australian fund performance persistence literature through the use of five performance metrics: raw returns, the Sharpe ratio, the single‐factor model and two multifactor models, the Carhart (1997) model and the Gruber (1996) model, in analysis of Australian retail fund performance over the period 1991–2000. Analysis suggests that performance persistence is sensitive to fund objective and appears to be driven by inadequate adjustment for risk.     

Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets

We study the ability of three-factor affine term-structure models to extract conditional volatility using interest rate swap yields for 1991–2005 and Treasury yields for 1970–2003. For the Treasury sample, the correlation between model-implied and EGARCH volatility is between 60% and 75%. For the swap sample, this correlation is rather low or negative. We find that these differences in model performance are primarily due to the timing of the swap sample, and not to institutional differences between swap and Treasury markets. We conclude that the ability of multifactor affine models to extract conditional volatility depends on the sample period, but that overall these models perform better than has been argued in the literature.     

The quality premium with leverage and liquidity constraints

This research analyzes the causes of the quality premium, one of the most intriguing and successful investment strategies in equity markets. While previous research has argued that psychological biases explain the performance of the quality minus junk factor, our paper analyzes a leverage constraint explanation within a rational risk-based framework. The quality factor is multidimensional in nature, which suggests that a combination of risk, frictions, and behavioral biases is a reasonable explanation. Once we incorporate margin requirements and liquidity restrictions, we find that tighter conditions result in a higher intercept and a lower slope for the empirically implemented capital asset pricing model when using 10 quality-sorted portfolios. Our paper shows that, indeed, not only behavioral biases explain quality, but also market frictions account for its performance.     

An empirical test of the variance gamma option pricing model

In this paper, we test the three-parameter symmetric variance gamma (SVG) option pricing model and the four-parameter asymmetric variance gamma (AVG) option pricing model empirically. Prices of the Hang Seng Index call options, which are of European style, are used as the data for the empirical test. Since the variance gamma option pricing model is developed for the pricing of European options, the empirical test gives a more conclusive answer than previous papers, which used American option data to the applicability of the VG models. The present study uses a large number of intraday option data, which span over a period of 3 years. Synchronous option and futures data are used throughout the study. Pairwise comparisons between the accuracy of model prices are carried out using both parametric and nonparametric methods.The conclusion is that the VG option pricing model performs marginally better than the Black–Scholes (BS) model. Under the historical approach, the VG models can moderately iron out some of the systematic biases inherent in the BS model. However, under the implied approach, the VG models continue to exhibit predictable biases and its overall performance in pricing and hedging is still far less than desirable.     

Bullish/bearish/neutral strategies under short sale restrictions

This study investigates the effects of short sale restrictions by extending the model of Dridi and Germain (2004) and infers informed traders’ strategies and the relation between order imbalance and price thereunder. The results are generally in line with the empirical evidence documented in the literature and are summarized as follows: First, seller-initiated trading incurs a greater price reaction. Second, short sale restrictions shift the skewness of asset returns. Third, the restrictions can stimulate investors to acquire information or increase each individual trader's order flow under the bullish and neutral signals as well as the bearish signal, which is yet to be explored empirically.