Fixed Income Asset Pricing: Extending the Elton et al. (1995) Four-Factor Model

2016 ◽  
Author(s):  
Andreas G. F. Hoepner ◽  
Marcus A Nilsson
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Fixed Income

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.

scholarly journals The Fama-French Five-Factor Model Plus Momentum: Evidence for the German Market

2020 ◽  
Vol 72 (4) ◽  
pp. 661-684 ◽  
Author(s):  
Philipp Dirkx ◽  
Franziska J. Peter
Keyword(s):  
Asset Pricing ◽  
Preliminary Analysis ◽  
Factor Model ◽  
Five Factor Model ◽  
Explanatory Power ◽  
Market Size ◽  
German Market ◽  
Monthly Data ◽  
Factors Associated ◽  
Country Specific

Abstract We implement the Fama-French five-factor model and enhance it with a momentum factor for the German market using recent monthly data from 2002 to 2019. We construct the factors associated with the market, size, value, profitability, investment, and momentum for the CDAX constituents and examine to what extent this six-factor model captures the return premia in the German market. Our preliminary analysis does not document any significant evidence on the profitability or investment premium. The results on the six-factor model compared with the three-factor model reveal that the additional factors do not add significant explanatory power to the analysis. We conclude that the relevance of the profitability and investment factors within the context of international asset pricing studies cannot be transferred to the country- specific case of the German market.

Is value premium sector-specific? Evidence from India

2020 ◽  
Vol 46 (12) ◽  
pp. 1605-1628
Author(s):  
Vanita Tripathi ◽  
Priti Aggarwal
Keyword(s):  
Asset Pricing ◽  
Portfolio Management ◽  
Factor Model ◽  
Emerging Market ◽  
Value Premium ◽  
Market Model ◽  
Top Down ◽  
Content Type ◽  
The Difference ◽  
The Relationship

PurposeThis paper is an attempt to explore the fact that whether the literature-promised value premium has any sector orientation. The paper tests the relationship between the value premium and Indian sectors: fast-moving consumer goods (FMCG), financials, healthcare, information technology (IT), manufacturing and miscellaneous.Design/methodology/approachThe paper analyses around 210–480 companies listed on BSE-500 for the period of the recent 18 years ranging from March 1999 to March 2017. The paper employed Welch's ANOVA to examine whether the price-to-book market ratio is significantly different across sectors. Two prominent asset pricing models – single factor market model and Fama–French three-factor model – were used to examine the existence of value premium within sectors for full period and two sub-periods.FindingsThe empirical results of the paper suggest that the difference in the P/B ratio both between sectors and within sectors is statistically significant. The results further suggest that the value premium exists within the sectors irrespective of their value-growth orientation.Research limitations/implicationsThe paper is not free from certain limitations. Firstly, due to the non-availability of data in the public domain, the time period before 1999 could not be considered. Secondly, the study has used data pertaining to the Indian stock market only. To add to it, our study has concentrated on BSE-500 companies only; however, the future researchers can include both NSE and BSE companies.Practical implicationsThe paper has important implications for portfolio managers and retail investors following a top-down approach of investing. The portfolio manager can strategically build up the portfolios to concentrate more on the companies belonging to sectors like healthcare, manufacturing and FMCG. Investors following the top-down approach should avoid the underperforming growth stocks belonging to the growth sectors and allocate their funds to value stocks in the growth sector.Originality/valueThe paper is first of its kind to study the relationship between the value premium and Indian sectors. The paper contributes to portfolio management and asset pricing literature for an emerging market.

The Bias in Two-Pass Regression Tests of Asset-Pricing Models in Presence of Idiosyncratic Errors with Cross-Sectional Dependence

2019 ◽  
Vol 22 (02) ◽  
pp. 1950012
Author(s):  
Thomas Gramespacher ◽  
Armin Bänziger
Keyword(s):  
Asset Pricing ◽  
Risk Premium ◽  
Measurement Errors ◽  
Factor Model ◽  
Pricing Models ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Asset Pricing Models ◽  
Slope Estimator ◽  
Regression Tests

In two-pass regression-tests of asset-pricing models, cross-sectional correlations in the errors of the first-pass time-series regression lead to correlated measurement errors in the betas used as explanatory variables in the second-pass cross-sectional regression. The slope estimator of the second-pass regression is an estimate for the factor risk-premium and its significance is decisive for the validity of the pricing model. While it is well known that the slope estimator is downward biased in presence of uncorrelated measurement errors, we show in this paper that the correlations seen in empirical return data substantially suppress this bias. For the case of a single-factor model, we calculate the bias of the OLS slope estimator in the presence of correlated measurement errors with a first-order Taylor-approximation in the size of the errors. We show that the bias increases with the size of the errors, but decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical data commonly used in asset-pricing tests.

Long-term prior return patterns in stock and sector returns in India

2014 ◽  
Vol 11 (2) ◽  
pp. 192-210
Author(s):  
Sanjay Sehgal ◽  
Sakshi Jain
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Risk Model ◽  
Factor Model ◽  
Size Factor ◽  
Content Type ◽  
Long Run ◽  
Portfolio Managers ◽  
Sector Returns

Purpose – The purpose of this paper is to analyze long-term prior return patterns in stock returns for India. Design/methodology/approach – The methodology involves portfolio generation based on company characteristics and long-term prior return (24-60 months). The characteristic sorted portfolios are then regressed on risk factors using one factor (capital asset pricing model (CAPM)) and multi-factor model (Fama-French (FF) model and four factor model involving three FF factors and an additional sectoral momentum factor). Findings – After controlling for short-term momentum (up to 12 months) as documented by Sehgal and Jain (2011), the authors observe that weak reversals emerge for the sample stocks. The risk model CAPM fails to account for these long-run prior return patterns. FF three-factor model is able to explain long-term prior return patterns in stock returns with the exception of 36-12-12 strategy. The value factor plays an important role while the size factor does not explain cross-section of average returns. Momentum patterns exist in long-term sector returns, which are stronger for long-term portfolio formation periods. Further, the authors construct sector factor and observe that prior returns patterns in stock returns are partially absorbed by this factor. Research limitations/implications – The findings are relevant for investment analysts and portfolio managers who are continuously tracking global markets, including India, in pursuit of extra normal returns. Originality/value – The study contributes to the asset pricing and behavioral literature from emerging markets.

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.

Minimum-Relative-Entropy Calibration of Asset-Pricing Models

1998 ◽  
Vol 01 (04) ◽  
pp. 447-472 ◽  
Author(s):  
Marco Avellaneda
Keyword(s):  
Asset Pricing ◽  
Relative Entropy ◽  
Smooth Curve ◽  
Contingent Claims ◽  
Fixed Income ◽  
Pricing Models ◽  
Forward Rates ◽  
Asset Pricing Models ◽  
Minimum Relative Entropy ◽  
Input Prices

We present an algorithm for calibrating asset-pricing models to the prices of benchmark securities. The algorithm computes the probability that minimizes the relative entropy with respect to a prior distribution and satisfies a finite number of moment constraints. These constraints arise from fitting the model to the prices of benchmark prices are studied in detail. We find that the sensitivities can be interpreted as regression coefficients of the payoffs of contingent claims on the set of payoffs of the benchmark instruments. We show that the algorithm has a unique solution which is stable, i.e. it depends smoothly on the input prices. The sensitivities of the values of contingent claims with respect to varriations in the benchmark instruments, in the risk-neutral measure. We also show that the minimum-relative-entropy algorithm is a special case of a general class of algorithms for calibrating models based on stochastic control and convex optimization. As an illustration, we use minimum-relative-entropy to construct a smooth curve of instantaneous forward rates from US LIBOR swap/FRA data and to study the corresponding sensitivities of fixed-income securities to variations in input prices.

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