Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models.Value at Risk, nonparametric quantile regression, risk management, extreme value theory, monotonization, CAViaR

Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models

Predicting extreme VaR

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models

Financial Network Systemic Risk Contributions

We propose the systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the systemic risk beta as the time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.Systemic risk contribution, systemic risk network, Value at Risk, network topology, two-step quantile regression, time-varying parameters

Bank Business Models at Zero Interest Rates

We propose a novel observation-driven dynamic finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Student's $t$ distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classified reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1--2015Q4, we identify six business model components and discuss how these adjust to post-crisis financial developments. Specifically, bank business models adapt to changes in the yield curve

Networking the Yield Curve: Implications for Monetary Policy

PublicFinanceIn this working paper, authors Tatevik Sekhposyan, Tatjana Dahlhaus, and Julia Schaumburg introduce a flexible, time-varying network model to trace the propagation of interest rate surprises across different maturities. First, the authors develop a novel econometric framework that allows for unknown, potentially asymmetric contemporaneous spillovers across panel units, and establish the finite sample properties of the model via simulations. Second, this innovative framework is employed to jointly model the dynamics of interest rate surprises and to assess how various monetary policy actions, for example, short-term, long-term interest rate targeting and forward guidance, propagate across the yield curve. Findings show that the network of interest rate surprises is indeed asymmetric, and defined by spillovers between adjacent maturities. Spillover intensity is high, on average, but shows strong time variation. Forward guidance is an important driver of the spillover intensity. Pass-through from short-term interest rate surprises to longer maturities is muted, yet there are stronger spillovers associated with surprises at medium- and long-term maturities. The authors illustrate how our proposed framework helps our understanding of the ways various dimensions of monetary policy propagate through the yield curve and interact with each other

Quantile methods for financial risk management

In dieser Dissertation werden neue Methoden zur Erfassung zweier Risikoarten entwickelt. Markrisiko ist definiert als das Risiko, auf Grund von Wertrückgängen in Wertpapierportfolios Geld zu verlieren. Systemisches Risiko bezieht sich auf das Risiko des Zusammenbruchs eines Finanzsystems, das durch die Notlage eines einzelnen Finanzinstituts entsteht. Im Zuge der Finanzkrise 2007–2009 realisierten sich beide Risiken, was weltweit zu hohen Verlusten für Investoren, Unternehmen und Steuerzahler führte. Vor diesem Hintergrund besteht sowohl bei Finanzinstituten als auch bei Regulierungsbehörden Interesse an neuen Ansätzen für das Risikomanagement. Die Gemeinsamkeit der in dieser Dissertation entwickelten Methoden besteht darin, dass unterschiedliche Quantilsregressionsansätze in neuartiger Weise für das Finanzrisikomanagement verwendet werden. Zum einen wird nichtparametrische Quantilsregression mit Extremwertmethoden kombiniert, um extreme Markpreisänderungsrisiken zu prognostizieren. Das resultierende Value at Risk (VaR) Prognose- Modell für extremeWahrscheinlichkeiten wird auf internationale Aktienindizes angewandt. In vielen Fällen schneidet es besser ab als parametrische Vergleichsmodelle. Zum anderen wird ein Maß für systemisches Risiko, das realized systemic risk beta, eingeführt. Anders als bereits existierende Messgrößen erfasst es explizit sowohl Risikoabhängigkeiten zwischen Finanzinstituten als auch deren individuelle Bilanzmerkmale und Finanzsektor-Indikatoren. Um die relevanten Risikotreiber jedes einzelnen Unternehmens zu bestimmen, werden Modellselektionsverfahren für hochdimensionale Quantilsregressionen benutzt. Das realized systemic risk beta entspricht dem totalen Effekt eines Anstiegs des VaR eines Unternehmens auf den VaR des Finanzsystems. Anhand von us-amerikanischen und europäischen Daten wird gezeigt, dass die neue Messzahl sich gut zur Erfassung und Vorhersage systemischen Risikos eignet.This thesis develops new methods to assess two types of financial risk. Market risk is defined as the risk of losing money due to drops in the values of asset portfolios. Systemic risk refers to the breakdown risk for the financial system induced by the distress of individual companies. During the financial crisis 2007–2009, both types of risk materialized, resulting in huge losses for investors, companies, and tax payers all over the world. Therefore, considering new risk management alternatives is of interest for both financial institutions and regulatory authorities. A common feature of the models used throughout the thesis is that they adapt quantile regression techniques to the context of financial risk management in a novel way. Firstly, to predict extreme market risk, nonparametric quantile regression is combined with extreme value theory. The resulting extreme Value at Risk (VaR) forecast framework is applied to different international stock indices. In many situations, its performance is superior to parametric benchmark models. Secondly, a systemic risk measure, the realized systemic risk beta, is proposed. In contrast to exististing measures it is tailored to account for tail risk interconnections within the financial sector, individual firm characteristics, and financial indicators. To determine each company’s relevant risk drivers, model selection techniques for high-dimensional quantile regression are employed. The realized systemic risk beta corresponds to the total effect of each firm’s VaR on the system’s VaR. Using data on major financial institutions in the U.S. and in Europe, it is shown that the new measure is a valuable tool to both estimate and forecast systemic risk