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.

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.

Global risk factors in the returns of listed private equity

2018 ◽  
Vol 35 (2) ◽  
pp. 340-360 ◽  
Author(s):  
Jörg Döpke ◽  
Lars Tegtmeier
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Stock Market ◽  
Cross Section ◽  
Private Equity ◽  
Pricing Model ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Model ◽  
Macroeconomic Risk

PurposeThe purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock marketβs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.Design/methodology/approachFollowing Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.FindingsUsing a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock marketßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.Research limitations/implicationsGiven data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.Practical implicationsInformation about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.Originality/valueTo the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

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.

scholarly journals High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

2020 ◽  
Vol 10 (04) ◽  
pp. 2050017
Author(s):  
Liao Zhu ◽  
Sumanta Basu ◽  
Robert A. Jarrow ◽  
Martin T. Wells
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Factor Model ◽  
Financial Data ◽  
High Dimensional ◽  
Arbitrage Pricing Theory ◽  
Pricing Model ◽  
Arbitrage Pricing ◽  
Asset Pricing Model ◽  
Pricing Theory

The paper proposes a new algorithm for the high-dimensional financial data — the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama–French 5-factor model.

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.

scholarly journals Size premium, value premium and market timing: evidence from an emerging economy

2018 ◽  
Vol 23 (46) ◽  
pp. 266-288
Author(s):  
Syed Haroon Rashid ◽  
Mohsin Sadaqat ◽  
Khalil Jebran ◽  
Zulfiqar Ali Memon
Keyword(s):  
Asset Pricing ◽  
Market Timing ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Content Type ◽  
Asset Pricing Model ◽  
Size Premium ◽  
Fama And French

Purpose This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model. Design/methodology/approach This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy. Findings The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market. Practical implications The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner. Originality/value Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.

scholarly journals The Time-Varying Systematic Risk of Carry Trade Strategies

2011 ◽  
Vol 46 (4) ◽  
pp. 1107-1125 ◽  
Author(s):  
Charlotte Christiansen ◽  
Angelo Ranaldo ◽  
Paul Söderlind
Keyword(s):  
Asset Pricing ◽  
Systematic Risk ◽  
Time Varying ◽  
Uncovered Interest Parity ◽  
Pricing Model ◽  
Carry Trade ◽  
Asset Pricing Model ◽  
Pricing Errors ◽  
Interest Parity ◽  
Foreign Exchange Volatility

AbstractWe explain the currency carry trade (CT) performance using an asset pricing model in which factor loadings are regime dependent rather than constant. Empirical results show that a typical CT strategy has much higher exposure to the stock market and is mean reverting in regimes of high foreign exchange volatility. The findings are robust to various extensions. Our regime-dependent pricing model provides significantly smaller pricing errors than a traditional model. Thus, the CT performance is better explained by a time-varying systematic risk that increases in volatile markets, suggesting a partial resolution of the uncovered interest parity puzzle.

Sign in / Sign up

Export Citation Format

Share Document