Intertemporal Relation Between The Expected Return And Risk: An Evaluation Of Emerging Market

2013 ◽  
Vol 29 (3) ◽  
pp. 809 ◽  
Author(s):  
Zhongyi Xiao ◽  
Peng Zhao
Keyword(s):  
Risk Premium ◽  
Emerging Market ◽  
Multivariate Garch ◽  
Capital Asset Pricing ◽  
Font Size ◽  
Expected Return ◽  
Cross Sectional ◽  
Risky Assets ◽  
Market Portfolio

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; line-height: 11.5pt; layout-grid-mode: char; mso-layout-grid-align: none; mso-line-height-rule: exactly; tab-stops: 266.7pt;" class="MsoNormal"><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-themecolor: text1;">This paper explores the intertemporal relationship between the expected return and risk in Chinese exchange market. We investigate the characterization of time-series variation in conditional variance and capture the cross-sectional correlation among equity portfolios by incorporating multivariate GARCH-M model with dynamic conditional covariance (DCC). Restricting the slope to be the same across risky assets, the risk-return coefficient is estimated to be positive and highly significant. In addition, the estimates of portfolio-specific slopes provide evidence to support the robustness across different portfolio formations. Our findings, in the Intertemporal Capital Asset Pricing Model (ICAPM) framework, reveal that the risk premium induced by the conditional covariation of equity portfolio with the market portfolio remain positive after controlling for risk premia induced by conditional covariation with Fama French benchmark factors (HML and SMB). The SMB factor might provide a significant predictive power to hedge against market risk. However, four indices of alternative investments are not consistently priced in the ICAPM framework.</span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets

2016 ◽  
Vol 41 (3) ◽  
pp. 234-246 ◽  
Author(s):  
Sanjay Sehgal ◽  
Vidisha Garg
Keyword(s):  
South Africa ◽  
Asset Pricing ◽  
South Korea ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Significant Risk ◽  
Residual Variance ◽  
Cross Sectional ◽  
Risk Premiums

Executive Summary Cross-sectional volatility measures dispersion of security returns at a particular point of time. It has received very little focus in research. This article studies the cross-section of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS). The analysis is done in two parts. The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure ( French, Schwert, & Stambaugh, 1987 ). The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model. The study finds that high SV portfolios exhibit low returns in case of Brazil, South Korea, and Russia. The risk premium is found to be statistically significantly negative for these countries. This finding is consistent with Ang et al. and is indicative of hedging motive of investors in these markets. Results for other sample countries are somewhat puzzling. No significant risk premiums are reported for India and China. Significantly positive risk premiums are observed for South Africa and Indonesia. Further, capital asset pricing model (CAPM) seems to be a poor descriptor of returns on systematic risk loading sorted portfolios while FF is able to explain returns on all portfolios except high SV loading portfolio (i.e., P1) in case of South Africa which seems to be an asset pricing anomaly. It is further observed that high UV portfolios exhibit high returns in all the sample countries except China. In the Chinese market, the estimated risk premium is statistically significantly negative. This negative risk premium is inconsistent with the theory that predicts that investors demand risk compensation for imperfect diversification. The remaining sample countries show significantly positive risk premium. CAPM does not seem to be a suitable descriptor for returns on UV sorted portfolios. The FF model does a better job but still fails to explain the returns on high UV sorted portfolio in case of Brazil and China and low UV sorted portfolio in South Africa. The findings are relevant for global fund managers who plan to develop emerging market strategies for asset allocation. The study contributes to portfolio management as well as market efficiency literature for emerging economies.

scholarly journals The Capital Asset Pricing Model: Theory and Evidence

2004 ◽  
Vol 18 (3) ◽  
pp. 25-46 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French
Keyword(s):  
Asset Pricing ◽  
Equity Capital ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Expected Return ◽  
Asset Pricing Model ◽  
Market Portfolio ◽  
Empirical Tests

The capital asset pricing model (CAPM) of William Sharpe (1964) and John Lintner (1965) marks the birth of asset pricing theory (resulting in a Nobel Prize for Sharpe in 1990). Before their breakthrough, there were no asset pricing models built from first principles about the nature of tastes and investment opportunities and with clear testable predictions about risk and return. Four decades later, the CAPM is still widely used in applications, such as estimating the cost of equity capital for firms and evaluating the performance of managed portfolios. And it is the centerpiece, indeed often the only asset pricing model taught in MBA level investment courses. The attraction of the CAPM is its powerfully simple logic and intuitively pleasing predictions about how to measure risk and about the relation between expected return and risk. Unfortunately, perhaps because of its simplicity, the empirical record of the model is poor - poor enough to invalidate the way it is used in applications. The model's empirical problems may reflect true failings. (It is, after all, just a model.) But they may also be due to shortcomings of the empirical tests, most notably, poor proxies for the market portfolio of invested wealth, which plays a central role in the model's predictions. We argue, however, that if the market proxy problem invalidates tests of the model, it also invalidates most applications, which typically borrow the market proxies used in empirical tests. For perspective on the CAPM's predictions about risk and expected return, we begin with a brief summary of its logic. We then review the history of empirical work on the model and what it says about shortcomings of the CAPM that pose challenges to be explained by more complicated models.

Abstract: Institutional Portfolio Restrictions, Diverse Investor Opportunity Sets, and Securities Market Equilibrium

1977 ◽  
Vol 12 (4) ◽  
pp. 637-637 ◽  
Author(s):  
David W. Glenn
Keyword(s):  
Securities Market ◽  
Securities Markets ◽  
Expected Return ◽  
Market Prices ◽  
Risky Assets ◽  
Opportunity Sets ◽  
Market Portfolio ◽  
Risk Dimensions ◽  
The Impact ◽  
Two Parameter

This paper utilizes a two-parameter model of segmented securities markets to develop equilibrium implications concerning the impact of statutory investment restrictions upon the market prices and allocation of risky securities. The distinguishing feature of the model is the existence of a subset of securities common to the opportunities of all investors and therefore said to “span” the investor population. These common opportunities are shown to permit intersubset security transactions which integrate the various market segments and lead to the following theorem and tendency concerning equilibrium prices and portfolios:Theorem: In the absence of active barriers against short positions, the equilibrium expected return for any security spanning the investor population is an exact linear function of its contribution to total market risk, irrespective of the number of distinct investor segments that may exist.Tendency: The economic characteristics of the equilibrium risky portfolio for any investor, irrespective of the market segment to which he or she may belong, will approximate the characteristics of the market portfolio of all risky assets in the economy in all relevant risk dimensions.

scholarly journals The Case For Emerging Market Funds

2011 ◽  
Vol 6 (11) ◽  
Author(s):  
Stuart Michelson ◽  
Elena Philipova ◽  
Petra Srotova
Keyword(s):  
Emerging Markets ◽  
Mutual Funds ◽  
Performance Studies ◽  
Emerging Market ◽  
Negative Relationship ◽  
Font Size ◽  
Cross Sectional ◽  
Market Index ◽  
Time Period ◽  
Wide Range

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">This study investigates the performance of open-end actively managed emerging market mutual funds during the time period 1999 to 2005. Our analysis is cross-sectional and time series across a wide range of emerging markets. Previous research includes performance studies of international mutual funds and emerging market funds, but none of the previous studies were as broad nor as specific as the current study. Monthly fund returns are compared to three indices (emerging markets, MSCI, and S&amp;P 500 Index), using annualized returns, Sharpe ratio and Treynor ratio. The results show that the emerging market funds outperform the MSCI Index and the S&amp;P 500 Index, but not the emerging market index. During the study period, an investor would have benefited by either investing in emerging market funds or the emerging market index. There is also a negative relationship between emerging market fund returns and turnover, and a positive relationship between fund returns and size.</span></span></p>

scholarly journals The Systematic Risk and Leverage Effect in the Corporate Sector of Pakistan

2000 ◽  
Vol 39 (4II) ◽  
pp. 951-962
Author(s):  
Muhammad Nishat
Keyword(s):  
High Risk ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Systematic Risk ◽  
Institutional Development ◽  
Relative Price ◽  
Accounting Earnings ◽  
Stock Volatility ◽  
Market Portfolio

Poor corporate financing policies, non-competitive role of institutional development, a tendency towards the underpricing of initial offering resulted in high levered stocks in Karachi stock market (KSE). The KSE is termed as high risk high return emerging market where investors seek high risk premium Nishat (1999). The leverage is the most important factor which determines the firms risk premium [Zimmer (1990)]. Hamada (1969) and Bowman (1979) have demonstrated the theoretical relationship between leverage and systematic risk. Systematic risk of the leverage firm is equal to the without leverage systematic risk of the firm times one plus the leverage ratio (debt equity). Bowman (1979) established that systematic risk is directly related to leverage and the accounting beta (covariability of a firms’ accounting earnings with the accounting earnings of the market portfolio). One explanation of time-varying stock volatility is that leverage changes as the relative price of stocks and bonds change. Schwert (1989) demonstrated how a change in the leverage of the firm causes a change in the volatility of stock returns. Haugen and Wichern (1975) analysed the relationship between leverage and relative stability of stock value based on actuarial science1 and found that the duration of the debt is an important attribute in assessing the effect of leverage on stock volatility. If the leverage is persistent, or changing over time due to the issuance of additional debt, or if the firms are trying to return back the debt, this will change the risk of holding common stock. Kane, Marcus, and McDonald (1985) argued that a well defined metric for the advantage of debt financing is the difference in rates of return earned by optimally levered and unlevered firms, net of a return premium to compensate for potential bankruptcy costs.

scholarly journals Ex-ante equity risk premia: Expectational estimates using stock market returns forecasts in the emerging equity market

2018 ◽  
Vol 65 (4) ◽  
pp. 479-507 ◽  
Author(s):  
Aleksandar Naumoski ◽  
Metodija Nestorovski
Keyword(s):  
Stock Market ◽  
Risk Premium ◽  
Goodness Of Fit ◽  
Emerging Market ◽  
Stock Market Index ◽  
Stock Market Returns ◽  
Goodness Of Fit Tests ◽  
Equity Risk ◽  
Market Portfolio ◽  
Ex Ante

We estimated the ex-ante equity risk premium for the Republic of Macedonia, which is a young, small and open emerging market. We polled academics and practitioners for their expectations on the stock market index MBI10 as a proxy for market portfolio. The risk premium is the expected MBI10 return relative to a government bond yield. Using the Kolmogorov-Smirnov and Anderson- Darling goodness-of-fit tests we determined the best fitted statistical distribution, and consequently estimated the short-term ERP of 8.55 and long-term average ERP for the next 10 years of 7.76. The estimated ex-ante ERP is higher and similar as it is in the other emerging markets.

Cross-Section of Expected Returns Based on Equity Duration

2019 ◽  
Vol 27 (3) ◽  
pp. 297-327
Author(s):  
Sungjeh Moon ◽  
Joonhyuk Song
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Factor Model ◽  
Explanatory Power ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Expected Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Risk Premiums

We analyze the cross-sectional expected return of KOSPI stocks using equity duration. From 1991 to 2018, we calculate equity durations for the KOSPI listed stocks (including de-listed stocks) and find that the shorter the equity duration, the higher the risk premium. Using the 4-factor model with equity duration added to the benchmark 3-factor model, the explanatory power of the 4-factor model is superior to that of the existing benchmark model in accounting for risk premiums. This is an unusual finding that is not readily explainable by the traditional CAPM or the Fama-French 3-factor model. This can be interpreted that the equity duration is a separate and significant risk factor dissociated from the HML of the 3-factor model.

scholarly journals The Choice Channel of Financial Innovation

2021 ◽  
Vol 13 (2) ◽  
pp. 333-372
Author(s):  
Felipe S. Iachan ◽  
Plamen T. Nenov ◽  
Alp Simsek
Keyword(s):  
Portfolio Choice ◽  
Risk Premium ◽  
Financial Innovation ◽  
The United States ◽  
Risky Assets ◽  
Market Participants ◽  
Portfolio Returns ◽  
Market Portfolio ◽  
Risk Free Rate ◽  
Free Rate

Financial innovation in recent decades has expanded portfolio choice. We investigate how greater choice affects investors’ savings and asset returns. We establish a choice channel by which greater portfolio choice increases investors’ savings—by enabling them to earn the aggregate risk premium or take speculative positions. In equilibrium, portfolio customization (access to risky assets beyond the market portfolio) reduces the risk-free rate. Participation (access to the market portfolio) reduces the risk premium but typically increases the risk-free rate. Empirically, stock market participants in the United States save more than nonparticipants and have increasingly dispersed portfolio returns, consistent with the choice channel. (JEL E21, G11, G12, G51)

Challenges in Estimation of Beta

2020 ◽  
pp. 79-98
Author(s):  
Tarana Azimova
Keyword(s):  
Asset Pricing ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Expected Return ◽  
Asset Pricing Model ◽  
Market Portfolio ◽  
Time Period ◽  
Beta Coefficient ◽  
Substantial Bias ◽  
The Relationship

Capital asset pricing model (CAPM) brings deep intuitive understanding of the relationship between expected return and risk. Unfortunately, the empirical record of the CAPM has not been satisfactory since its commencement. The empirical testing of CAPM is void in most cases due to the use of an inefficient index as a proxy for market portfolio. Plausible tests require a well-diversified market portfolio which so far has been unfeasible to obtain. Lack of validity in empirical records has been caused by complexity in exerting valid estimations of the beta coefficient. This chapter judges which of the indices provides investors the best beta forecast and questions which time period should be selected for beta calculation. This chapter reveals that the choice of return intervals causes variations in beta estimation of the security. Applying higher frequency has an advantage in that it increases the number of observations, but a shortfall is that beta tends to have substantial bias with shorter return intervals used.

scholarly journals Reexamination of Whether Accrual Quality Is a Price Factor

2019 ◽  
Vol 8 (3) ◽  
pp. 103
Author(s):  
May Xiaoyan Bao ◽  
Xiaoyan Cheng ◽  
John Geppert ◽  
David B. Smith
Keyword(s):  
Fixed Effects ◽  
Capital Asset Pricing ◽  
Expected Return ◽  
Cross Sectional ◽  
Accrual Quality ◽  
Vector Autoregressive ◽  
Return Decomposition ◽  
Panel Data Regression ◽  
Data Regression ◽  
Discount Rate News

In this study we investigate whether accrual quality is a factor in capital asset pricing. Our analysis consists of two parts. First, we use a panel data regression that controls for cross-section fixed effects to implement the second stage of the Fama-MacBeth regression (Petersen 2009). In the second part, we use the Campbell (1991) return decomposition and vector autoregressive model (VAR) to decompose the two-stage cross-sectional regressions. This allows us to investigate whether accrual quality is a priced factor in terms of the three components of the return, which include one-period expected return, cash flow news and discount-rate news. 

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