Details
Originalsprache | Englisch |
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Qualifikation | Doctor rerum politicarum |
Gradverleihende Hochschule | |
Betreut von |
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Datum der Verleihung des Grades | 11 Dez. 2023 |
Erscheinungsort | Hannover |
Publikationsstatus | Veröffentlicht - 2023 |
Abstract
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Hannover, 2023. 217 S.
Publikation: Qualifikations-/Studienabschlussarbeit › Dissertation
}
TY - BOOK
T1 - Essays on asset pricing factor models
AU - Dang, Thuy Duong
PY - 2023
Y1 - 2023
N2 - This dissertation investigates the utilization of factor models to measure performance in corporate bond markets, identifies an optimal factor model for corporate bond returns, and finally provides a comprehensive analysis of factor pricing and market integration across asset classes. Chapter 1 introduces the main concepts and delivers an overview of the following chapters. Chapter 2 seeks to answer the question "which factor model do investors in corporate bonds use?" by tracking investors' decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. The main result is that all bond factor models are dominated by the simple Sharpe ratio and Morningstar ratings. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio (and to some extent also Morningstar ratings) can be easily manipulated in bond markets, these findings have potentially severe implications for all market participants. Going a step further, Chapter 3 addresses the following important questions, from both an academic and a practitioner's perspective: What are important drivers of corporate bond returns? What should be a benchmark model for pricing and investing in corporate bond markets? The central finding is that factors related to carry, duration, equity momentum, and the term structure are the most important risk factors in corporate bond markets. From a large set of factor candidates for corporate bond returns, we condense an optimal model with a two-step approach. First, we filter out factors that do not systematically move bond prices. Second, we use a Bayesian model selection approach to determine the optimal, parsimonious model. Many prominent factors do not move prices, or are redundant. We document the new model's good performance compared to that of existing models in time-series and cross-sectional tests and analyze the economic drivers of the factors. While Chapter 2 and Chapter 3 focus on corporate bonds, the study conducted in Chapter 4 extends the understanding to a bigger picture of factor pricing and market integration across asset classes. Factor models specializing in one asset class have limited pricing power for other asset classes. Thus, we reject perfect market integration. However, an optimal integrated factor model across asset classes can effectively characterize the returns of multiple asset classes and provide a useful benchmark for multi-asset, multi-factor investing. The optimal model includes several equity and corporate bond factors, suggesting the presence of multiple systematic return drivers. Despite this, there appears to be some degree of cross-market linkages, as the optimal model does not require factors from all asset classes. Finally, Chapter 5 concludes and outlines possible future directions for research.
AB - This dissertation investigates the utilization of factor models to measure performance in corporate bond markets, identifies an optimal factor model for corporate bond returns, and finally provides a comprehensive analysis of factor pricing and market integration across asset classes. Chapter 1 introduces the main concepts and delivers an overview of the following chapters. Chapter 2 seeks to answer the question "which factor model do investors in corporate bonds use?" by tracking investors' decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. The main result is that all bond factor models are dominated by the simple Sharpe ratio and Morningstar ratings. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio (and to some extent also Morningstar ratings) can be easily manipulated in bond markets, these findings have potentially severe implications for all market participants. Going a step further, Chapter 3 addresses the following important questions, from both an academic and a practitioner's perspective: What are important drivers of corporate bond returns? What should be a benchmark model for pricing and investing in corporate bond markets? The central finding is that factors related to carry, duration, equity momentum, and the term structure are the most important risk factors in corporate bond markets. From a large set of factor candidates for corporate bond returns, we condense an optimal model with a two-step approach. First, we filter out factors that do not systematically move bond prices. Second, we use a Bayesian model selection approach to determine the optimal, parsimonious model. Many prominent factors do not move prices, or are redundant. We document the new model's good performance compared to that of existing models in time-series and cross-sectional tests and analyze the economic drivers of the factors. While Chapter 2 and Chapter 3 focus on corporate bonds, the study conducted in Chapter 4 extends the understanding to a bigger picture of factor pricing and market integration across asset classes. Factor models specializing in one asset class have limited pricing power for other asset classes. Thus, we reject perfect market integration. However, an optimal integrated factor model across asset classes can effectively characterize the returns of multiple asset classes and provide a useful benchmark for multi-asset, multi-factor investing. The optimal model includes several equity and corporate bond factors, suggesting the presence of multiple systematic return drivers. Despite this, there appears to be some degree of cross-market linkages, as the optimal model does not require factors from all asset classes. Finally, Chapter 5 concludes and outlines possible future directions for research.
U2 - 10.15488/15782
DO - 10.15488/15782
M3 - Doctoral thesis
CY - Hannover
ER -