Dealing with uncertainty is at the heart of financial risk management and asset pricing. This cumulative dissertation consists of four independent research papers that study various aspects of uncertainty, from estimation and model risk over the volatility risk premium to the measurement of unobservable variables.
In the first paper, a non-parametric estimator of conditional quantiles is proposed that builds on methods from the machine learning literature. The so-called leveraging estimator is discussed in detail and analyzed in an extensive simulation study. Subsequently, the estimator is used to quantify the estimation risk of Value-at-Risk and Expected Shortfall models. The results suggest that there are significant differences in the estimation risk of various GARCH-type models while in general estimation risk for the Expected Shortfall is higher than for the Value-at-Risk.
In the second paper, the leveraging estimator is applied to realized and implied volatility estimates of US stock options to empirically test if the volatility risk premium is priced in the cross-section of option returns. A trading strategy that is long (short) in a portfolio with low (high) implied volatility conditional on the realized volatility yields average monthly returns that are economically and statistically significant.
The third paper investigates the model risk of multivariate Value-at-Risk and Expected Shortfall models in a comprehensive empirical study on copula GARCH models. The paper finds that model risk is economically significant, especially high during periods of financial turmoil, and mainly due to the choice of the copula.
In the fourth paper, the relation between digitalization and the market value of US insurers is analyzed. Therefore, a text-based measure of digitalization building on the Latent Dirichlet Allocation is proposed. It is shown that a rise in digitalization efforts is associated with an increase in market valuations.:1 Introduction
1.1 Motivation
1.2 Conditional quantile estimation via leveraging optimal quantization
1.3 Cross-section of option returns and the volatility risk premium
1.4 Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?
1.5 Estimating the relation between digitalization and the market value of
insurers
2 Conditional Quantile Estimation via Leveraging Optimal Quantization
2.1 Introduction
2.2 Optimal quantization
2.3 Conditional quantiles through leveraging optimal quantization
2.4 The hyperparameters N, λ, and γ
2.5 Simulation study
2.6 Empirical application
2.7 Conclusion
3 Cross-Section of Option Returns and the Volatility Risk Premium
3.1 Introduction
3.2 Capturing the volatility risk premium
3.3 Empirical study
3.4 Robustness checks
3.5 Conclusion
4 Marginals Versus Copulas: Which Account for More Model Risk in Multivariate Risk Forecasting?
4.1 Introduction
4.2 Market risk models and model risk
4.3 Data
4.4 Analysis of model risk
4.5 Model risk for models in the model confidence set
4.6 Model risk and backtesting
4.7 Conclusion
5 Estimating the Relation Between Digitalization and the Market Value of
Insurers
5.1 Introduction
5.2 Measuring digitalization using LDA
5.3 Financial data & empirical strategy
5.4 Estimation results
5.5 Conclusion
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:79809 |
| Date | 05 July 2022 |
| Creators | Fritzsch, Simon |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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