Finanzrisikomanagement: Quo Vadis?

Transcription

1 Finanzrisikomanagement: Quo Vadis? April 24, 2012, Deutsche Bank Berlin Paul Embrechts Humboldt Distinguished Lecturer Director RiskLab, ETH Zurich Department of Mathematics SFI Senior Professor

2 RiskLab Switzerland, founded October 7, 1994

3 (ap) Die drei Schweizer Grossbanken haben laut einer Pressemitteilung eine Interessengemeinschaft im Bereich der Finanzforschung gegründet. Ihr Zweck bestehe darin, mit Hochschulen und Universitäten praxisnahe Forschungen im Finanzbereich zu unternehmen. Dazu haben die Schweizerische Bankgesellschaft, die Schweizerische Kreditanstalt und der Schweizerische Bankverein einen ersten Zusammenarbeitsvertrag mit der «Eidgenössischen Technischen Hochschule» (ETH) abgeschlossen. Unter der Bezeichnung «RiskLab» sollen Konzepte, Modelle und Techniken im globalen Risikomanagement und -monotoring untersucht werden. Hinter jedem Forschungsprojekt soll eine praxisnahe Fragestellung stehen. Neben weiteren Schweizer Banken seien auch nationale und internationale Bankenaufsichtsbehörden eingeladen, bei der Gestaltung des Forschungsprogramms mitzuwirken. Neue Zürcher Zeitung, Zürich, October 7, 1994.

4 Risk Research: Big Banks Found a Research Community (ap) According to a press report the three biggest Swiss banks have founded an interest group in the area of financial research. Its aim is to engage in practice-oriented research in collaboration with universities and other institutions of higher education. The Union Bank of Switzerland, Credit Suisse and the Swiss Bank Corporation, have signed a preliminary working contract with the «ETH Zurich» (the Federal Institute of Technology). A group of researchers, under the name of «RiskLab» will examine concepts, models, and techniques of global risk management and controlling. Each research project will start with a question arising from a practical problem in the real world. In addition to other Swiss banks, national and international regulatory bodies are welcome to participate in the setting up of the research programme. Freely translated from the Neue Zürcher Zeitung, Zürich, 7th October 1994.

5 The aims of RiskLab were/are(!): Promotion of the scientific competence and methodology in the general area of quantitative risk management; Promotion of fundamental and precompetitive applied research in strong connection with practice; Knowledge exchange between academia, the finance, insurance and energy industry; Promotion of the ETH Zurich as one of the leading centres of excellence regarding academic education and research in finance and insurance mathematics.

6 Why then, and why now?

7 The Basel Committee for Banking Supervision 1988: Basel I 1994(!) 2000: Amendment to Basel I (Basel I½), Value-at-Risk (VaR) is born /10: Basel II (Credit and Operational Risk) and Basel II1/2 Currently: Basel III In insurance: SST (1/1/11!), Solvency 1 and 2 (1/1/ ?)

8 What then, and what now?

9 A key RiskLab document (what then?):

10 Embrechts, P. et al. (2001): An academic response to Basel II. Financial Markets Group, London School of Economics. (Mailed as an official response to the Basel Committee) PE website since 2001! et al. = Jón Daníelsson Charles Goodhart Con Keating Felix Muennich Olivier Renault Hyun Song Shin

11 In this official response on Basel II we warned very explicitly for: Poor quality risk measures (Value-at-Risk) Endogeneity of risk, inherent pro-cyclicality Lack of measurement of systemic risk Impossibility of accurate quantitative estimation of regulatory capital at very high quantile levels 99+%, and long horizons 1yr+ (stochastic!) Insufficient quality of rating agencies assessment of default risk for securitized products Industry-wide underestimation of downside/ extreme risk, and - dependence ( correlation )

12 A broader historical perspective on financial innovation ( what now?):

13 Financial «innovation» in a nutshell: (1) Fractional Reserve Banking (2) BSM papers in 1973, creating «The Market» through dynamic replication and implied volatility Bonds Stocks (2) Options Futures Mortgages Cash CDO (3) ABS/CDS (Credit) Derivatives Abacus 2700-AC1 ABS Corr. Trade CDO**2/3 Hybryd CDO Synthetic CDO Money Commodities Goods FRB(1) (3) «2006 saw the creation of one of the most unnatural and destructive financial products that the world has ever seen, the synthetic CDO, it turned the keg of dynamite into the financial equivalent of a nuclear bomb.» («All the devils are there», B. McLean & J. Nocera, Portfolio/Penguin, New York, 2010, Chapter 17)

14 And I «forgot» to include the ATM: Paul Volcker, former Federal Reserve Chairman: «The only innovation has been the ATM machine» (sic), and «I wish someone would give me one shred of neutral evidence that financial innovation has led to economic growth one shred of evidence» well: ATM machine:

15 The QRM landscape:

16 The three dimensions of RM Dimension 1: Scope Micro: a portfolio, a trader, a client,... Meso: industrial entity, a firm, a country,... Macro: worldwide systems, global networks,... Dimension 2: Time Short (HFT): << 1 year, intra-day Medium (Solvency 2/Basel III): ~ 1 year Long (Social/Life Insurance): >> 1 year Dimension 3: Level Quantitative versus Qualitative

17 Some important challenges: Macro-economics and social insurance Volumes (Ultra-) High Frequency Trading Networks and inter-dependencies In some pictures!

18 Macro-Economics and Social Insurance

19 A picture tells a 1000 words: Japan

20 And the USA:

21 And the consequences for education and research!

22 Volumes

23 Some things we need(ed) to know! 1 tri $ = $ World GDP = 58 tri $, US GDP = 14.5 tri $ (US deficit = 1.35 tri $, debt = 13.6 tri $) Nominal amount CDS (6/10) = 30 tri $ Nominal amount of OTC (6/10) = 583 tri $ CDO volume 2006: 2.7 tri $ 1/2007: in the US, about 12 AAA-rated companies, and about AAA-rated securitization instruments, etc etc

24 (Ultra-) High Frequency Trading

25 An early warning of things to come? The Flash-Crash of May 6, 2010! /9.2% in 5 Hence new Risk Management challenges!

26 From «Not so fast», Buttonwood, The Economist, 2011, August 6-12, p. 55 NASDAC 1 trade/250 micro sec HFT ~ 2/3-3/4 total Wall St volume Smaller spreads, liquidity sales pitch HFT and best capital allocation? Average holding time of stocks 4 months «Boundary play» (PE), «spoof orders» Positive correlation (volume, volatility) Potential dislocation between share prices and economic fundamentals...

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28 1 Lidschlag, 10'000 Aufträge - an der schnellsten Börse der Welt Mit der Einführung von X-stream INET und Co-Location reduziert SIX Swiss Exchange die Latenz auf 37 Mikrosekunden und erhöht ihre Attraktivität für alle Marktteilnehmer. 1 Lidschlag, 10'000 Aufträge - an der schnellsten Börse der Welt

29 «Die Teilnehmer profitieren nicht nur von der schnelleren Ausführung ihrer Aufträge und höheren Volumen, sondern können auch Investitionsentscheide schneller umsetzen. Das erlaubt ein effizienteres Risikomanagement, was sowohl für Handelsteilnehmer als auch für die Endanleger von Vorteil ist.»

30 Networks and Inter-Dependencies

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32 What role does (Financial/Insurance) Mathematics have to play in this «what now?»

33 Mathematics is of key importance for understanding and clarifying models and prices used in finance, insurance and economics making heuristic methods mathematically precise, and asking for clear, unambiguous definitions! highlighting model conditions and restrictions on applicability working out numerous explicit examples leading the way for stress testing and robustness properties and it would be bad if the current crisis would induce a shying away from mathematics!

34 Financial Mathematics has been very successful in explaining today s prices, but not in predicting tomorrows (Hans Buehlmnn, ETH Zurich) In a certain way, FM has not been developed with that latter goal in mind (PE)

35 RiskLab QRM Research - Examples (1997, 2005, 2007)

36 Before the crisis, and after/during: Academic!

37 A nice example of the importance of QRM and the more mathematical research done, e.g. Zurich, Berlin :

38 But mathematics is just one small piece of the complex QRM puzzle! It definitely helps in better understanding the strengths and limitations of the Q in QRM.

39 (Some) Lessons Hopefully Learned: 1) Beware of new paradigms 2) There is no such thing as a free lunch 3) Question (excessive) gains Ask why? 4) Ask: What would it take to break the product? 5) Watch out for (and understand) volume, also AAA 6) Balance between qualitative and quantitative, between rational and irrational (or behavioral) 7) Multidisciplinarity 8) Complexity no easy fixes (mathematics) 9) Better education ( QRM and )!!!! 10) Learn from neighboring fields

40 An example of current mathematical QRM research:

41 Oberwolfach Meeting on the Mathematics and Statistics of QRM, 29/1-4/2, 2012

42 Some topics discussed at the conference that week: Rethinking the methodological basis of (financial) economics: the firm, the bank, the household Model uncertainty Algorithmic trading (Re-)Insurance networks Risk aggregation and dependence modelling Modelling endogeneous risk Statistics of extreme events Integrating behavioural elements into the models

43 Thank You!