Testing extensions of Fama &amp; French models: A quantile regression approach

González, María de la O; Jareño, Francisco

doi:10.1016/j.qref.2018.08.004

Cited by 24 publications

(42 citation statements)

References 34 publications

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“…Finally, [3] conclude that other factors may also explain the sensitivity of stock market returns. This latest research proposes to include the risk factors momentum (MOM) and momentum reversal (LTREV) [16] and the liquidity factor negotiated (LIQV) [17] to extend the explanatory power of the Fama and French models.…”

Section: Literature Reviewmentioning

confidence: 88%

“…Although some papers, such as [3,4], have carried out a study based on the Fama and French models, the present study contributes to the literature in the following three ways. First, we add three explanatory factors to the three-and five-factor Fama and French models [1,2]: momentum, reversal momentum and the liquidity factor.…”

Section: Introductionmentioning

confidence: 98%

“…Thus, this paper analyses the consistency of the extensions of the Fama and French three-and five-factor models [1][2][3][4] in the Spanish stock market, approximated through the IBEX-35 market index. Specifically, the aim of this paper is to study the returns of the Spanish companies that currently comprise this index and their variation in response to changes in the risk factors proposed in the aforementioned extensions of the Fama and French models.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Spanish IBEX-35 Companies’ Returns Using Extensions of the Fama and French Factor Models

2020

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This paper studies in depth the sensitivity of Spanish companies’ returns to changes in several risk factors between January 2000 and December 2018 using the quantile regression approach. Concretely, this research applies extensions of the Fama and French three- and five-factor models (1993 and 2015), according to González and Jareño (2019), adding relevant explanatory factors, such as nominal interest rates, the Carhart (1997) risk factor for momentum and for momentum reversal and the Pastor and Stambaugh (2003) traded liquidity factor. Additionally, for robustness, this paper splits the entire sample period into three sub-sample periods (pre-crisis, crisis and post-crisis) to analyse the results according to the economic cycle. The main conclusions of this paper are fourfold: First, these two models have the greatest explanatory power in the extreme quantiles of the return distribution (0.1 and 0.9) and more specifically in the lowest quantile 0.1. Second, the second model, based on the Fama and French five-factor model, shows the highest explanatory power not only in the full period but also in the three sub-periods. Third, the bank BBVA is the company that shows the highest sensitivity to changes in the explanatory factors in most periods because its adjusted R2 is the highest. Fourth, the stage of the economy with the highest explanatory power is the crisis subperiod. Thus, the final conclusion of this paper is that the second model explains best variations in Spanish companies’ returns in crisis stages and low quantiles.

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Section: Literature Reviewmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Analysis of the Spanish IBEX-35 Companies’ Returns Using Extensions of the Fama and French Factor Models

2020

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“…Although previous literature has proposed other factor models, such as the Fama and French (1993) three- and five-factor model (Jareño, 2008; Campos et al., 2016; Jareño et al., 2018), and other procedures (Jammazi et al., 2017), such as the quantile regression (QR) approach (Ballester et al., 2011; Jareño et al., 2016; Ferrando et al., 2017; Sevillano and Jareño, 2018; Umar et al., 2018; González and Jareño, 2019), this research is based on the seminal research of Stone (1974) (Tessaromatis, 2003, Soto et al., 2005, and Jareño, 2006, among others).…”

Section: Methodsmentioning

confidence: 99%

Alternative estimates of the well-known negative relationship between the US interest rate risk and the flow-through capability

Jareño

Tolentino

Cano

2019

Heliyon

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This paper estimates US industries' ability to transmit inflation shocks to the prices of their products and services (flow-through capability, FTC) and the stock duration (interest rate sensitivity) at the sector level. Then, considering the significant differences in ability among industries, we analyze the relationship between FTC and interest rate sensitivity using two alternative methodologies (in both cases). Finally, we find a significant negative relationship between FTC and stock duration, as suggested by previous literature. Thus, industries with high FTC, such as S7 (Finance and Real Estate), S9 (Manufacturing), S11 (Transportation and Warehousing) and S12 (Utilities), may be less sensitive (than expected) to changes in nominal interest rates. In contrast, sectors such as S4 (Retail Trade), S8 (Information) and S10 (Professional and Administrative Services) (with high IRS) may be more sensitive (than expected) to changes in nominal interest rates, indicating a weak ability to transmit inflation shocks to the prices of their products and services.

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“…Studies such as Campos et al (2016), Ferrando et al (2017), Jareño et al (2018) and González and Jareño (2019) highlight the performance of multifactor models by separating the overall study period into sub periods based on certain criteria. We attempt to conduct a similar exercise as a robustness test.…”

Section: Additional Tests Of Factor Redundancy and Robustnessmentioning

confidence: 99%

Multi-factor asset pricing models in emerging and developed markets

Lalwani

Chakraborty

2019

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Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

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Testing extensions of Fama & French models: A quantile regression approach

Cited by 24 publications

References 34 publications

Analysis of the Spanish IBEX-35 Companies’ Returns Using Extensions of the Fama and French Factor Models

Analysis of the Spanish IBEX-35 Companies’ Returns Using Extensions of the Fama and French Factor Models

Alternative estimates of the well-known negative relationship between the US interest rate risk and the flow-through capability

Multi-factor asset pricing models in emerging and developed markets

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