FRTB for Structured Products the Impact of an Internal Model Under FRTB Financial Risk FRTB for Structured Products
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Capturing Tail Risk in a Risk Budgeting Model
DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Capturing Tail Risk in a Risk Budgeting Model FILIP LUNDIN MARKUS WAHLGREN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Capturing Tail Risk in a Risk Budgeting Model FILIP LUNDIN MARKUS WAHLGREN Degree Projects in Financial Mathematics (30 ECTS credits) Master's Programme in Industrial Engineering and Management KTH Royal Institute of Technology year 2020 Supervisor at Nordnet AB: Gustaf Haag Supervisor at KTH: Anja Janssen Examiner at KTH: Anja Janssen TRITA-SCI-GRU 2020:050 MAT-E 2020:016 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract Risk budgeting, in contrast to conventional portfolio management strategies, is all about distributing the risk between holdings in a portfolio. The risk in risk budgeting is traditionally measured in terms of volatility and a Gaussian distribution is commonly utilized for modeling return data. In this thesis, these conventions are challenged by introducing different risk measures, focusing on tail risk, and other probability distributions for modeling returns. Two models for forming risk budgeting portfolios that acknowledge tail risk were chosen. Both these models were based on CVaR as a risk measure, in line with what previous researchers have used. The first model modeled re- turns with their empirical distribution and the second with a Gaussian mixture model. The performance of these models was thereafter evaluated. Here, a diverse set of asset classes, several risk budgets, and risk targets were used to form portfolios. Based on the performance, measured in risk-adjusted returns, it was clear that the models that took tail risk into account in general had su- perior performance in relation to the standard model. -
Back-Testing of Expected Shortfall: Main Challenges and Methodologies
© CHAPPUIS HALDER & CO Back-testing of Expected Shortfall: Main challenges and methodologies By Leonard BRIE with Benoit GENEST and Matthieu ARSAC Global Research & Analytics 1 1 This work was supported by the Global Research & Analytics Dept. of Chappuis Halder & Co. Many collaborators from Chappuis Halder & Co. have been involved in the writing and the reflection around this paper; hence we would like to send out special thanks to Claire Poinsignon, Mahdi Kallel, Mikaël Benizri and Julien Desnoyers-Chehade © Global Research & Analytics Dept.| 2018 | All rights reserved Executive Summary In a context of an ever-changing regulatory environment over the last years, Banks have witnessed the draft and publication of several regulatory guidelines and requirements in order to frame and structure their internal Risk Management. Among these guidelines, one has been specifically designed for the risk measurement of market activities. In January 2016, the Basel Committee on Banking Supervision (BCBS) published the Fundamental Review of the Trading Book (FRTB). Amid the multiple evolutions discussed in this paper, the BCBS presents the technical context in which the potential loss estimation has changed from a Value-at-Risk (VaR) computation to an Expected Shortfall (ES) evaluation. The many advantages of an ES measure are not to be demonstrated, however this measure is also known for its major drawback: its difficulty to be back-tested. Therefore, after recalling the context around the VaR and ES models, this white paper will review ES back-testing findings and insights along many methodologies; these have either been drawn from the latest publications or have been developed by the Global Research & Analytics (GRA) team of Chappuis Halder & Co. -
Fundamental Review of the Trading Book (FRTB) | Accenture
FROM THEORY TO ACTION In January 2016, the Basel Committee on Banking Supervision (BCBS) published final rules for the market risk framework for capital requirements. The BCBS proposed the end of 2019 as a compliance deadline for banks with a significant presence in capital markets.1 The new rules – known as Fundamental FTRB encompasses a revised internal Review of the Trading Book or FRTB – are model approach characterized by a shift designed to address Basel 2.5 issues from Value-at-Risk (VAR) to the Expected such as the under-capitalization of the Shortfall (ES) measure of risk, for a better trading book, capital arbitrage between reflection of “tail risk” and capital adequacy banking and trading books, and internal during periods of significant financial risk transfers. Through the FRTB rules, market stress.3 BCBS is seeking, for example, to establish a more objective boundary between the trading book and the banking book, and to eliminate capital arbitrage between the regulatory banking and trading books.2 2 FUNDAMENTAL REVIEW OF THE TRADING BOOK (FRTB) CHALLENGES TO FRTB IMPLEMENTATION Accenture believes that adoption of the FRTB • Additional investment in technology rules presents banks with major challenges infrastructure for risk calculation. in areas related to business operations and infrastructure provisioning. According FRTB rules require banks to strengthen to our analysis and estimates we expect: their existing market risk infrastructure and overall technology capabilities, with • Significant increases (as much as additional computational capacity to 40 percent) in market risk capital support calculations as required under new requirements; capital requirements. Banks should also plan for additional complexity in operations and • Higher costs for rules implementation processes due to changed desk structures programs – ranging from $100 million and should undertake the standardization to $250 million for large banks; of data sources to support these changes. -
Fundamental Review of the Trading Book – Interim Impact Analysis
Basel Committee on Banking Supervision Fundamental review of the trading book – interim impact analysis November 2015 This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2015. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN 978-92-9197-293-7 (print) ISBN 978-92-9197-294-4 (online) Fundamental review of the trading book – interim impact analysis (November 2015) Contents 1. Background ....................................................................................................................................................................... 1 2. Key findings ....................................................................................................................................................................... 2 3. Sample of participating banks ................................................................................................................................... 5 4. Analysis of the proposed standardised and internally-modelled approaches ....................................... 7 5. Trading desk structure ................................................................................................................................................ 10 6. P&L attribution test ...................................................................................................................................................... 12 7. Non-modellable risk factors .................................................................................................................................... -
Tail Risk Premia and Return Predictability∗
Tail Risk Premia and Return Predictability∗ Tim Bollerslev,y Viktor Todorov,z and Lai Xu x First Version: May 9, 2014 This Version: February 18, 2015 Abstract The variance risk premium, defined as the difference between the actual and risk- neutral expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attributed to time variation in the part of the variance risk premium associated with the special compensation demanded by investors for bearing jump tail risk, consistent with idea that market fears play an important role in understanding the return predictability. Keywords: Variance risk premium; time-varying jump tails; market sentiment and fears; return predictability. JEL classification: C13, C14, G10, G12. ∗The research was supported by a grant from the NSF to the NBER, and CREATES funded by the Danish National Research Foundation (Bollerslev). We are grateful to an anonymous referee for her/his very useful comments. We would also like to thank Caio Almeida, Reinhard Ellwanger and seminar participants at NYU Stern, the 2013 SETA Meetings in Seoul, South Korea, the 2013 Workshop on Financial Econometrics in Natal, Brazil, and the 2014 SCOR/IDEI conference on Extreme Events and Uncertainty in Insurance and Finance in Paris, France for their helpful comments and suggestions. yDepartment of Economics, Duke University, Durham, NC 27708, and NBER and CREATES; e-mail: [email protected]. zDepartment of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208; e-mail: [email protected]. xDepartment of Finance, Whitman School of Management, Syracuse University, Syracuse, NY 13244- 2450; e-mail: [email protected]. -
Comparative Analyses of Expected Shortfall and Value-At-Risk Under Market Stress1
Comparative analyses of expected shortfall and value-at-risk under market stress1 Yasuhiro Yamai and Toshinao Yoshiba, Bank of Japan Abstract In this paper, we compare value-at-risk (VaR) and expected shortfall under market stress. Assuming that the multivariate extreme value distribution represents asset returns under market stress, we simulate asset returns with this distribution. With these simulated asset returns, we examine whether market stress affects the properties of VaR and expected shortfall. Our findings are as follows. First, VaR and expected shortfall may underestimate the risk of securities with fat-tailed properties and a high potential for large losses. Second, VaR and expected shortfall may both disregard the tail dependence of asset returns. Third, expected shortfall has less of a problem in disregarding the fat tails and the tail dependence than VaR does. 1. Introduction It is a well known fact that value-at-risk2 (VaR) models do not work under market stress. VaR models are usually based on normal asset returns and do not work under extreme price fluctuations. The case in point is the financial market crisis of autumn 1998. Concerning this crisis, CGFS (1999) notes that “a large majority of interviewees admitted that last autumn’s events were in the “tails” of distributions and that VaR models were useless for measuring and monitoring market risk”. Our question is this: Is this a problem of the estimation methods, or of VaR as a risk measure? The estimation methods used for standard VaR models have problems for measuring extreme price movements. They assume that the asset returns follow a normal distribution. -
FRTB Standardized Approach for Market Risk
A Bloomberg Professional Services Offering Bloomberg Terminal FRTB Risk & Valuations Standardized Approach for market risk Contents 03 Bloomberg’s FRTB SA Solution 05 An open architecture 06 Why Bloomberg for FRTB 07 Learn more FRTB Standardized Approach for market risk The Fundamental Review of the Trading Book (FRTB) is the biggest global sell-side regulatory change that has taken place in more than two decades. It completely overhauls the framework for market risk as a result of the severe market stress in 2007-08. FRTB was created to ensure that regulatory market risk models deliver reliable capital outcomes and promote consistent implementation of the standards across jurisdictions. The Standardized Approach (SA) is required for all entities regulated under the Basel market risk regime, regardless of whether or not they also run the Internal Models Approach (IMA). The SA is a capital charge consisting of: • Sensitivities-based Method (SBM) — a parametric market risk calculation based on standardized risk factor sensitivities and volatilities and correlations specified by the Basel Committee. • Default Risk Charge (DRC) — a jump-to-default measure for individual issuers as well as securitizations based on standard netting rules to capture hedging effects. • Residual Risk Add-On (RRAO) — an additional charge for non-vanilla instruments whose risk is not captured by either of the two metrics above. FRTB standardized approach The Standardized Approach for Market Risk Sensitivities-based Method: Capital Default Risk Charge (DRC) Residual risk add-on (RRAO) charges for delta, vega and curvature for prescribed risk classes risk factor sensitivities within a Risk weights applied to notional amounts prescribed set of risk classes • Default risk: non-securitization. -
In Defence of Var Risk Measurement and Backtesting in Times of Crisis 1 June, 2020
In Defence of VaR Risk measurement and Backtesting in times of Crisis 1 June, 2020 In Defence of VaR Executive Summary Since the Financial Crisis, Value at Risk (VaR) has been on the receiving end of a lot of criticisms. It has been blamed for, amongst other things, not measuring risk accurately, allowing banks to get away with holding insufficient capital, creating over reliance on a single model, and creating pro-cyclical positive feedback in financial markets leading to ‘VaR shocks’. In this article we do not seek to defend VaR against all these claims. But we do seek to defend the idea of modelling risk, and of attempting to model risk accurately. We point out that no single model can meet mutually contradictory criteria, and we demonstrate that certain approaches to VaR modelling would at least provide accurate (as measured by Backtesting) near-term risk estimates. We note that new Market Risk regulation, the Fundamental Review of the Trading Book (FRTB) could provide an opportunity for banks and regulators to treat capital VaR and risk management VaR sufficiently differently as to end up with measures that work for both rather than for neither. Contents 1 Market Risk VaR and Regulatory Capital 3 2 Can VaR accurately measure risk? 5 3 Regulation and Risk Management 10 4 Conclusion 13 Contacts 14 In Defence of VaR 1 Market Risk VaR and Regulatory Capital Market Risk VaR was developed at JP Morgan in the wake of the 1987 crash, as a way to answer then chairman Dennis Weatherstone’s question “how much might we lose on our trading portfolio by tomorrow’s close?” VaR attempts to identify a quantile of loss for a given timeframe. -
Modeling Financial Sector Joint Tail Risk in the Euro Area
Modeling financial sector joint tail risk in the euro area∗ Andr´eLucas,(a) Bernd Schwaab,(b) Xin Zhang(c) (a) VU University Amsterdam and Tinbergen Institute (b) European Central Bank, Financial Research Division (c) Sveriges Riksbank, Research Division First version: May 2013 This version: October 2014 ∗Author information: Andr´eLucas, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands, Email: [email protected]. Bernd Schwaab, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt, Germany, Email: [email protected]. Xin Zhang, Research Division, Sveriges Riksbank, SE 103 37 Stockholm, Sweden, Email: [email protected]. We thank conference participants at the Banque de France & SoFiE conference on \Systemic risk and financial regulation", the Cleveland Fed & Office for Financial Research conference on \Financial stability analysis", the European Central Bank, the FEBS 2013 conference on \Financial regulation and systemic risk", LMU Munich, the 2014 SoFiE conference in Cambridge, the 2014 workshop on \The mathematics and economics of systemic risk" at UBC Vancouver, and the Tinbergen Institute Amsterdam. Andr´eLucas thanks the Dutch Science Foundation (NWO, grant VICI453-09-005) and the European Union Seventh Framework Programme (FP7-SSH/2007- 2013, grant agreement 320270 - SYRTO) for financial support. The views expressed in this paper are those of the authors and they do not necessarily reflect the views or policies of the European Central Bank or the Sveriges Riksbank. Modeling financial sector joint tail risk in the euro area Abstract We develop a novel high-dimensional non-Gaussian modeling framework to infer con- ditional and joint risk measures for many financial sector firms. -
Hedging and Evaluating Tail Risks Via Two Novel Options Based on Type II Extreme Value Distribution
S S symmetry Article Hedging and Evaluating Tail Risks via Two Novel Options Based on Type II Extreme Value Distribution Hang Lin 1,2,* , Lixin Liu 1 and Zhengjun Zhang 2 1 School of Statistics, University of International Business and Economics, Beijing 100029, China; [email protected] 2 Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA; [email protected] * Correspondence: [email protected] Abstract: Tail risk is an important financial issue today, but directly hedging tail risks with an ad hoc option is still an unresolved problem since it is not easy to specify a suitable and asymmetric pricing kernel. By defining two ad hoc underlying “assets”, this paper designs two novel tail risk options (TROs) for hedging and evaluating short-term tail risks. Under the Fréchet distribution assumption for maximum losses, the closed-form TRO pricing formulas are obtained. Simulation examples demonstrate the accuracy of the pricing formulas. Furthermore, they show that, no matter whether at scale level (symmetric “normal” risk, with greater volatility) or shape level (asymmetric tail risk, with a smaller value in tail index), the greater the risk, the more expensive the TRO calls, and the cheaper the TRO puts. Using calibration, one can obtain the TRO-implied volatility and the TRO-implied tail index. The former is analogous to the Black-Scholes implied volatility, which can measure the overall symmetric market volatility. The latter measures the asymmetry in underlying losses, mirrors market sentiment, and provides financial crisis warnings. Regarding the newly proposed TRO Citation: Lin, H.; Liu, L.; Zhang, Z. -
Impact Study on the Proposed Frameworks for Market Risk and CVA Risk
Basel Committee on Banking Supervision Instructions: Impact study on the proposed frameworks for market risk and CVA risk July 2015 This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2015. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN 978-92-9197-190-9 (print) ISBN 978-92-9197-191-6 (online) Contents 1. Introduction ...................................................................................................................................................................... 4 1.1 General ................................................................................................................................................................................ 4 1.2 Fundamental Review of the Trading Book (FRTB).............................................................................................. 5 1.3 Credit Valuation Adjustment (CVA) ......................................................................................................................... 7 2. General Info ....................................................................................................................................................................... 8 2.1 Panel A: General bank data ......................................................................................................................................... 9 2.2 Panel B: Breakdown of total accounting CVA and DVA ................................................................................. -
Bloomberg FRTB Solutions Overview FRTB
A Bloomberg Professional Services Offering Services Professional Bloomberg Bloomberg FRTB solutions overview FRTB The Basel Committee on Banking Supervision (BCBS) has set new global standards for Minimum Capital Requirements for Market Risk. Banks must implement the final version of this rule, also known as the Fundamental Review of the Trading (FRTB), by January 1, 2022. Given the many infrastructure and workflow changes needed to implement FRTB, banks must move quickly in order to meet the new requirements in time. Bloomberg’s suite of solutions offer data and tools to help ease the burden of FRTB implementation and facilitate ongoing compliance. Regulatory data for FRTB Risk analytics for Standardized Approach FRTB introduces a number of challenges requiring regulation Comprehensive market risk workflow enabling banks specific data sets in addition to the reference and pricing data to calculate and report regulatory capital in accordance necessary to perform the calculations that FRTB requires. with the current BCBS market risk requirements. Bloomberg provides data sets to meet both the Standardized • Integrated position feeds from Bloomberg OMS and Internal Models Approaches. • Mapping to SA risk classes and risk factors • FRTB SA bucketing data • Delta, Vega, Curvature, Jump to Default • FRTB RFET transparency data • Risk bucket assignment and aggregation • Full capital with BCBS risk weights and correlations Reference, pricing and derivatives data FRTB requires banks to fundamentally change the way they Risk analytics for internal models approach look at data if they are to meet the challenges in obtaining Basel-compliant market risk models powered by consistency, alignment, and understanding of provenance. Bloomberg pricing and data to help clients pass the Bloomberg delivers data-sets for all asset classes including: Risk Factor Eligibility Test and P&L Attribution Tests and avoid capital surcharges.