High-order moments in stock pricing: evidence from the Chinese and US markets

2019 ◽  
Vol 10 (3) ◽  
pp. 323-346
Author(s):  
Yifan Chen ◽  
Zilin Chen ◽  
Huoqing Tang
Keyword(s):  
Asset Pricing ◽  
Stock Markets ◽  
Factor Model ◽  
High Order ◽  
Pricing Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Joint Distributions ◽  
Portfolio Returns ◽  
Fama French Factors

Purpose The purpose of this paper is to introduce an augmented high-order capital asset pricing model (AH-CAPM) as a new risk-based model to price stocks. Design/methodology/approach The AH-CAPM is defined as a linear model with high-order marginal moments and co-moments from the joint distributions of the sorted stock portfolio returns and the market return. Findings The performance of the AH-CAPM is tested in the Chinese and US stock markets. Empirical results show that the high-order marginal moments and co-moments from the joint distributions in AH-CAPM contain the risk and return information implied by the Fama–French factors, indicating it as a better risk measurement. Moreover, the AH-CAPM performs better than the Fama–French three-factor model and the Carhart four-factor model in both the Chinese and US stock markets. Originality/value Overall, this study introduces a new asset pricing model with better measurements to incorporate risk information in the stock market.

scholarly journals Empirical Test of Single Factor and Multi-Factor Asset Pricing Models: Evidence from Non Financial Firms on the Ghana Stock Exchange (GSE)

2015 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
Prince Acheampong ◽  
Sydney Kwesi Swanzy
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Stock Exchange ◽  
Explanatory Power ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Financial Firms ◽  
Portfolio Returns ◽  
Ghana Stock Exchange ◽  
Three Factor

<p>This paper examines the explanatory power of a uni-factor asset pricing model (CAPM) against a multi-factor model (The Fama-French three factor model) in explaining excess portfolio returns on non-financial firms on the Ghana Stock Exchange (GSE). Data covering the period January 2002 to December 2011 were used. A six Size- Book-to-Market (BTM) ratio portfolios were formed and used for the analysis. The paper revealed that, a uni-factor model like the (CAPM) could not predict satisfactorily, the excess portfolio returns on the Ghana Stock Exchange. By using the multi-factor asset pricing model, that is, the Fama-French Three Factor Model, excess portfolio returns were better explained. It is then conclusive enough that, the multi-factor asset pricing model introduced by Fama and French (1992) was a better asset pricing model to explain excess portfolio returns on the Ghana Stock Exchange than the Capital Assets Pricing Model (CAPM) and that there exist the firm size and BTM effects on the Ghanaian Stock market.</p>

scholarly journals Liquidity risk, transaction costs and financial closedness: lessons from the Iranian and Turkish stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sedighe Alizadeh ◽  
Mohammad Nabi Shahiki Tash ◽  
Johannes Kabderian Dreyer
Keyword(s):  
Asset Pricing ◽  
Transaction Costs ◽  
Stock Markets ◽  
Design Methodology ◽  
Liquidity Risk ◽  
Extreme Case ◽  
Pricing Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
The Impact

Purpose This paper aims to study the impact of liquidity risk and transaction costs on stock pricing in Iran, a closed market operating under a financial embargo and compare the results with those of an important neighboring market, namely, Turkey. Design/methodology/approach This study follows Liu et al. (2016) and incorporates liquidity risk and transaction costs into the traditional consumption-based asset-pricing model (CCAPM) from 2009 to 2017. Effective transaction costs are estimated a la Hasbrouck (2009) and liquidity risk according to eight different criteria. Findings According to the results, both liquidity risk and transaction costs are higher in Iran, possibly due to the financial embargo. Thus, relative to Turkey, this paper should expect a higher increase in the CCAPM pricing performance in Iran when accounting for these two variables. The results are in line with this expectation and indicate that adjusting the CCAPM significantly increases its pricing performance in both countries, but relatively more in Iran. Originality/value This study compares liquidity risk and transaction costs in an economy under the extreme case of a financial embargo to an open yet in other important aspects similar economy from the same region.

The relationship between covariance risk and size effects in emerging equity markets

2016 ◽  
Vol 42 (3) ◽  
pp. 174-190 ◽  
Author(s):  
Ming-Chieh Wang ◽  
Jin-Kui Ye
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Financial Integration ◽  
Equity Markets ◽  
Pricing Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Portfolio Returns ◽  
Pricing Errors ◽  
The World

Purpose – The purpose of this paper is to examine whether the conditionally expected return on size-based portfolios in an emerging market (EM) is determined by the country’s world risk exposure. The authors analyze the degree of financial integration of 23 emerging equity markets grouped into five size portfolios using the conditional international asset pricing model with both world and domestic market risks. The authors also compare the model’s fitness on the predictability of portfolio returns by using world and EM indices. Design/methodology/approach – This study investigates whether large-cap stocks are priced globally and whether mid- and small-cap stocks are strongly influenced by domestic risk factors. The authors first examine the predictability of large-, mid-, and small-cap stock portfolio returns by using global and local variables, and next compare the model fitness by using world and EM indices on the prediction of size-based stock returns. Finally, the authors test whether the world price of covariance risk is the same for different portfolios. Findings – The authors find that the conditional expected returns of large-cap stocks should be priced by global variables. Mid- and small-cap stocks are influenced by domestic variables rather than global variables, and their returns are priced by local residual risks. The test of the conditional asset pricing model shows that the largest stocks have the smallest mean absolute pricing errors (MAE), and their pricing errors are lower in large markets than in small markets. Third, the EM index offers more predictability for the excess returns of mid- and small-cap stocks than the world market index, but the explanatory power of this index does not increase for large-cap stocks. Originality/value – EMs in the past were known as segment markets, with local risk factors more important than global risk factors, suggesting significant benefits from adding EMs to global portfolios. It would be interesting to examine whether financial integration differs for various firm sizes in the markets.

scholarly journals The influence of the 2008 financial crisis on the predictiveness of risky asset pricing models in Brazil

2016 ◽  
Vol 27 (72) ◽  
pp. 408-420
Author(s):  
Adriana Bruscato Bortoluzzo ◽  
Maria Kelly Venezuela ◽  
Maurício Mesquita Bortoluzzo ◽  
Wilson Toshiro Nakamura
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Pricing Model ◽  
Pricing Models ◽  
Monthly Basis ◽  
Total Period ◽  
Risky Assets ◽  
Asset Pricing Model ◽  
Portfolio Returns ◽  
Fama And French

ABSTRACT This article examines three models for pricing risky assets, the capital asset pricing model (CAPM) from Sharpe and Lintner, the three factor model from Fama and French, and the four factor model from Carhart, in the Brazilian mark et for the period from 2002 to 2013. The data is composed of shares traded on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBOVESPA) on a monthly basis, excluding financial sector shares, those with negative net equity, and those without consecutive monthly quotations. The proxy for market return is the Brazil Index (IBrX) and for riskless assets savings accounts are used. The 2008 crisis, an event of immense proportions and market losses, may have caused alterations in the relationship structure of risky assets, causing changes in pricing model results. Division of the total period into pre-crisis and post-crisis sub-periods is the strategy used in order to achieve the main objective: to analyze the effects of the crisis on asset pricing model results and their predictive power. It is verified that the factors considered are relevant in the Brazilian market in both periods, but between the periods, changes occur in the statistical relevance of sensitivities to the market premium and to the value factor. Moreover, the predictive ability of the pricing models is greater in the post-crisis period, especially for the multifactor models, with the four factor model able to improve predictions of portfolio returns in this period by up to 80%, when compared to the CAPM.

scholarly journals Is Human Capital the Sixth Factor? Evidence from US Data

2019 ◽  
Vol 8 (1) ◽  
pp. 21-55 ◽  
Author(s):  
Rahul Roy ◽  
Santhakumar Shijin
Keyword(s):  
Human Capital ◽  
Asset Pricing ◽  
Factor Model ◽  
Generalized Method Of Moments ◽  
Common Factors ◽  
Return Predictability ◽  
Pricing Model ◽  
Gmm Estimation ◽  
Asset Return ◽  
Asset Pricing Model

Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challengingtask that preoccupied the asset pricing literature for decades. However, there exists no consensus on the integrated asset pricing framework among the financial economists in the contemporaneous asset pricing literature. Thus, we consider and study this research problem that has greater relevance in pricing the risks of an asset. In this backdrop, we develop an integrated equilibrium asset pricing model in an intertemporal (ICAPM) framework. Research Objective/Questions: Broadly we have two research objectives. First, we examine the joint dynamics of the human capital component and common factors in approximating the variation in asset return predictability. Second, we test whether the human capital component is the unaccounted and the sixth pricing factor of FF five-factor asset pricing model. Additionally, we assess the economic and statistical significance of the equilibrium six-factor asset pricing model. Methodology: The human capital component, market portfolio, size, value, profitability, and investment are the pricing factors of the equilibrium six-factor asset pricing model. We use Fama-French (FF) portfolios of 2  3, 5  5, 10  10 sorts, 2  4  4 sorts, and the Industry portfolios to examine the equilibrium six-factor asset pricing model. The Generalized method of moments (GMM) estimation is used to estimate the parameters of variant asset pricing models and Gibbons-Ross-Shanken test is employed to evaluate the performance of the variant asset pricing frameworks. Major Findings: Our approaches led to three conclusions. First, the GMM estimation result infers that the human capital component of the six-factor asset pricing model significantly priced the variation in excess return on FF portfolios of variant sorts and the Industry portfolios. Further, the sensitivity to human capital component priced separately in the presence of the market portfolios and the common factors. Second, the six-factor asset pricing model outperforms the CAPM, FF three-factor model, and FF five-factor model, which indicates that the human capital component is a significant pricing factor in asset return predictability. Third, we argue that the human capital component is the unaccounted asset pricing factor and equally the sixth-factor of the FF five-factor asset pricing model. The additional robustness test result confirms that the parameter estimation of the six-factor asset pricing model is robust to the alternative definitions of the human capital component. Implications: The empirical results and findings equally pose the more significant effects for the decision-making process of the rational investor, institutional managers, portfolio managers, and fund managers in formulating the better investment strategies, which can help in diversifying the aggregate risks.

Revisiting Fama–French’s asset pricing model with an MCB volatility risk factor

2020 ◽  
Vol 21 (3) ◽  
pp. 233-251
Author(s):  
Xiaoying Chen ◽  
Nicholas Ray-Wang Gao
Keyword(s):  
Risk Factor ◽  
Asset Pricing ◽  
Explanatory Power ◽  
Investor Sentiment ◽  
Size Factor ◽  
Pricing Model ◽  
Volatility Risk ◽  
Content Type ◽  
Asset Pricing Model ◽  
Market Factor

Purpose Since the introduction of VIX to measure the spot volatility in the stock market, VIX and its futures have been widely considered to be the standard of underlying investor sentiment. This study aims to examine how the magnitude of contango or backwardation (MCB volatility risk factor) derived from VIX and VIX3M may affect the pricing of assets. Design/methodology/approach This paper focuses on the statistical inference of three defined MCB risk factors when cross-examined with Fama–French’s five factors: the market factor Rm–Rf, the size factor SMB (small minus big), the value factor HML (high minus low B/M), the profitability factor RMW (robust minus weak) and the investing factor CMA (conservative minus aggressive). Robustness checks are performed with the revised HML-Dev factor, as well as with daily data sets. Findings The inclusions of the MCB volatility risk factor, either defined as a spread of monthly VIX3M/VIX and its monthly MA(20), or as a monthly net return of VIX3M/VIX, generally enhance the explanatory power of all factors in the Fama and French’s model, in particular the market factor Rm–Rf and the value factor HML, and the investing factor CMA also displays a significant and positive correlation with the MCB risk factor. When the more in-time adjusted HML-Dev factor, suggested by Asness (2014), replaces the original HML factor, results are generally better and more intuitive, with a higher R2 for the market factor and more explanatory power with HML-Dev. Originality/value This paper introduces the term structure of VIX to Fama–French’s asset pricing model. The MCB risk factor identifies underlying configurations of investor sentiment. The sensitivities to this timing indicator will significantly relate to returns across individual stocks or portfolios.

Evaluating alternative performance benchmarks for Indian mutual fund industry

2017 ◽  
Vol 14 (2) ◽  
pp. 222-250 ◽  
Author(s):  
Sanjay Sehgal ◽  
Sonal Babbar
Keyword(s):  
Asset Pricing ◽  
Mutual Fund ◽  
Factor Model ◽  
Five Factor Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Fund Managers ◽  
Asset Pricing Models ◽  
Performance Benchmarks ◽  
Fama And French

Purpose The purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context. Design/methodology/approach Sample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model. Findings Conditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios. Practical implications A significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking. Originality/value The study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

Dynamic Factors and Asset Pricing

2010 ◽  
Vol 45 (3) ◽  
pp. 707-737 ◽  
Author(s):  
Zhongzhi (Lawrence) He ◽  
Sahn-Wook Huh ◽  
Bong-Soo Lee
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Dynamic Factor ◽  
Pricing Model ◽  
Portfolio Returns ◽  
Dynamic Factors ◽  
Ex Ante ◽  
Out Of Sample ◽  
Competing Models ◽  
Fama And French

AbstractThis study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and conditional versions of the Fama and French (1993) 3-factor model. In particular, the DFPM can explain and better forecast the momentum portfolio returns, which are mostly missed by alternative models.

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