The conference is going to take place at Eötvös Loránd University, Budapest, Hungary Pázmány Péter Sétány 1/A, Gömb Aula.
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The regulatory landscape is undergoing considerable changes worldwide. The 2007-2009 Financial Crisis brought into question several aspects of regulation for banking, the so-called Basel and Solvency guidelines. At the same time, demographic and economic developments (e.g. longevity, low interest rates) are causing major problems for the insurance industry, and this mainly, but not exclusively, for life insurance. Added to these we do witness important changes to society at large, also driven by information technology. Besides the obvious social and political changes experienced worldwide, we should add more technologically driven ones like network vulnerability and systemic risk, new products, large data (data science, machine learning), block-chain technology, cyber security. These developments will no doubt have a considerable impact on the financial and insurance industry both at the business as well as at the regulatory level. In this talk I will discuss some of the underlying issues from a more personal perspective as a researcher in Quantitative Risk Management.
Before the onset of the credit crunch in 2007, the difference between London Interbank Offered Rate (LIBOR) and the Overnight Indexed Swap Rate (OIS) was negligible. In the crisis the spreads between the two rates suddenly started to widen and since then it has been evolving randomly. Prices of instruments linked LIBOR rates started to reflect stochastic spreads, a new risk factor emerged. The disconnect between the two rates required the industry to revise the modeling assumptions, pricing formulas and hedging strategies.
This research investigates the impact of the stochastic LIBOR-OIS spreads on future counterparty exposure distributions by comparing the results obtained from a stochastic spread model to the industry wide deterministic spread assumption. We considered the stochastic basis model proposed by Mercurio and Li (2016) with two basis dynamics: the extended Vasicek and extended CIR models. Both models are calibrated to two distinct historical basis time series: i.) to the financial crisis period and ii.) to a more recent and stable period. The analysis focuses on a single tenor, on a single currency and a vanilla Zero Coupon Swap instrument.
The current moderate economic growth complemented with low yields and increased policy related economic uncertainty poses many challenges for the European financial sector. Hence, it is important to assess properly and timely all potential risks for financial stability. Stress test exercises as one of the most complex assessment tools could help to identify the key risks and vulnerabilities and assess their potential impacts on financial systems. It is important to conduct such exercises not only at national level, but also at the European level. Additionally, the increased interconnectedness between banking, insurance and other financial sectors call for developing new methodologies that would allow to conduct cross sectoral stress test exercises.
Tracking and monitoring stress within the financial system is a key component for financial stability and macroprudential policy purposes. Financial stress measures are important as indicators measuring materialised risks, enabling policy makers to take corrective measures in time. This presentation introduces a new measure of contemporaneous stress within the Hungarian financial system named Factor based Index of Systemic Stress (FISS). Its statistical design is a dynamic Bayesian factor method. The main methodological innovation of the FISS is the ability to fully capture information contained in persistent, high-frequency data with the usage of common stochastic trends as factors. The FISS is planned to be a key element of the Hungarian macroprudential toolkit. Apart from its policy use the FISS can also be utilised as a threshold variable for VAR models, aiming to quantify the effects of macroprudential instruments.
We investigate the risk taking incentives of “stressed banks” — the banks that are subject to annual regulatory stress tests in the U.S. since 2011. Stressed banks are subject to more stringent capital requirements compared to other banks depending on the assessed riskiness of their portfolios to a regulatory stress scenario. On one hand, stringent capital requirements provide stressed banks with motives to invest in riskier assets with higher expected returns to offset their increased cost of funding originating from costly equity injections. On the other hand, stressed banks are subject to more invasive monitoring by the regulator, who imposes bank-specific capital requirements on the basis of the assessed risk of their individual portfolios. Monitoring gives incentives to stressed banks to invest in low-risk assets that are less sensitive to the regulatory stress scenario in order to reduce their capital requirement. Our results highlight the importance of regulatory monitoring of banks’ portfolios in parallel to setting more stringent capital requirements.
Driven by regulatory requirements, financial institutions experience the need to improve and expand their stress testing practices. The topic of this presentation is a real-life case study of a financial institution setting up a new framework for stress testing their client portfolios. We will discuss the proposed methodology to arrive at a relevant set of stress tests suited to their use case. This, among other things, entails the definition of historical stress test scenarios as well as the quality assessment of these scenarios.
Comprehensive Capital Analysis and Review (CCAR), the annual stress testing exercise of the Federal Reserve Board (FRB) to be performed by all major US banks, is not only about calculating stress loss and capital requirement. The FRB requires banks to demonstrate that models used for stress loss calculations are appropriate. This may impose significant challenge to modelers. We will see examples of FRB requirements for the assessment of model assumptions and limitations, and benchmarking, and discuss some potential approaches to satisfy these requirements.
Recent results (Acerbi, Szekely 2017) have shown that a backtest of Expected Shortfall (ES) is necessarily approximated, in the sense that it’s unavoidably sensitive to possible errors in the prediction of Value at Risk (VaR). We introduce the backtest for ES which minimizes such sensitivity. The bias is small and prudential: any imperfect VaR prediction results in a more punitive test against ES and the effect is generally negligible for small VaR discrepancies. For this reason the backtest qualifies as an appropriate end-to-end model validation tool for ES based models, notably Basel IV internal models for which the question is still largely open. Furthermore, the ES backtest, as opposed to the common VaR backtest, estimates not only model acceptance probability, but also the prediction discrepancy magnitude. This means that the backtest automatically measures portfolio-specific capital multipliers. For the same reason, a new notion of “realized ES” emerges, analogous to the classical realized variance, which does not exist for VaR.
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