The goal of asset pricing research is to find the optimal model that explains the drivers of asset
returns. Historically, this field has predominantly relied on data from the United States, given
the extensive and detailed records of its financial markets. Due to the growing interdependence
of international markets, recent research has shifted towards leveraging large global datasets to
develop universally applicable models. However, empirical evidence suggests that these global
models explain less variation in domestic returns compared to country-specific models.
This thesis investigates the effectiveness of country-specific asset pricing models across a set
of European markets, utilising both classical and Bayesian methods to assess model
performance. The first empirical chapter begins with evaluating the relative performance of
nine asset pricing models in developed European stock markets from 1991-2022.
Asymptotically valid tests of model comparison, developed by Barillas, Kan, Robotti and
Shanken (2020), are conducted, where the extent of model mispricing is gauged by the squared
Sharpe ratio improvement measure of Barillas and Shanken (2017). The findings reveal that
the Fama and French (2018) six-factor model, with both original and updated value factors, are
the top-performing models in most markets. However, variation in the absolute and relative
performance of models across samples suggests that a singular optimal European asset pricing
model does not exist within the classical framework.
To enhance model performance, the second empirical chapter explores the use of serial
correlation in factor returns as conditioning information. Adopting the methodology of Ehsani
and Linnainmaa (2022), this chapter shows that multiple investment factors in the crosscountry dataset are unconditionally minimum-variance inefficient: factor returns are positively
autocorrelated, while risk remains constant regardless of past returns. Using Ferson and
Siegel’s (2001) general framework, 'time-series efficient factors' are constructed by
conditioning factor weights on historical returns to enhance the Sharpe ratios of these factors
across the European markets under consideration. A number of these optimised factors achieve
significantly higher average Sharpe ratios compared to the original factors, while retaining all
the information contained in the original factors. When the model comparison tests of Barillas
et al. (2020) are repeated with these optimised factors, the absolute performance of the lowerperforming models improves, while the relative performance among the models remains
consistent across markets.
In the third and final empirical chapter, the Bayesian framework of Chib, Zeng, and Zhao
(2020) is used to identify the optimal combination of factors from a starting collection of 12
risk factors in each European market. The results indicate that the optimal combinations of
factors are similar to the top-performing models in the classical tests. The optimal model from
the scan either represents a reduced form with one or two fewer factors or an extension of the
top model identified in Chapter Two, with one or two additional factors. This alignment
underscores the robustness of the model selection across different testing methodologies. The
changes in these optimal combinations are then examined under the assumptions of both
normality and multivariate-t distributions on the factor data. Employing the methodology of
Chib and Zeng (2020), the analysis reveals no significant disparities in results when a Studentt distribution is assumed for the factor data. Additionally, the extent to which the efficient factor
transformation impacts the model comparison tests in each market is analysed. The findings
reveal that certain efficient factors are present in the optimal combination of factors across
European markets.
| Date of Award | 24 Jan 2025 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Supervisor | Jonathan Fletcher (Supervisor) & Andrew Marshall (Supervisor) |
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