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กะเทาะแก่นการเงินเหนือเมฆ วิศวกรรมการเงินกับบทเรียนที่ควรเรียน ( แถมบางเรื่องที่จริง ๆ แล้วไม่ค่อย เกี่ยว ) จากวิกฤติการเงินโลกครั้งล่าสุด ดร. พูมใจ นาคสกุล.

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Presentation on theme: "กะเทาะแก่นการเงินเหนือเมฆ วิศวกรรมการเงินกับบทเรียนที่ควรเรียน ( แถมบางเรื่องที่จริง ๆ แล้วไม่ค่อย เกี่ยว ) จากวิกฤติการเงินโลกครั้งล่าสุด ดร. พูมใจ นาคสกุล."— Presentation transcript:

1 กะเทาะแก่นการเงินเหนือเมฆ วิศวกรรมการเงินกับบทเรียนที่ควรเรียน ( แถมบางเรื่องที่จริง ๆ แล้วไม่ค่อย เกี่ยว ) จากวิกฤติการเงินโลกครั้งล่าสุด ดร. พูมใจ นาคสกุล ทีมแบบจำลองเชิงปริมาณและวิศวกรรมการเงิน (Quantitative Models & Financial Engineering) ๒๙ กรกาคม ๒๕๕๓

2 2 ที่จะคุยกันวันนี้  บทนำ / ถือโอกาสแนะนำตัวทีม ‘Quant’ –  = barely,  = weakly,  = strongly,  = definitely.  วิกฤติการเงินโลกครั้งล่าสุด ‘Subprime Mortgage  Global Financial’ Crisis – เรื่องมันใหญ่ขนาดไหนเชียว ? – ( เรา / ท่าน ) คิดว่าอะไรเกิดขึ้น ?  วิศวกรรมการเงิน – คืออะไรกันนะ ? เกี่ยวหรือไม่เกี่ยว ? – CDO, CDO 2, CDS, ABCP, SIV …  ๘ บทเรียน

3 3 บทบาทหน้าที่ – ทีมแบบจำลองเชิงปริมาณ และวิศวกรรมการเงิน  ศูนย์ศึกษา / หน่วยวิจัย – 2551 ระเบียบวิธีตีราคาตราสารอนุพันธ์ Collateralized Debt Obligation (CDO) แบบ ‘semi-bespoke’ – 2552 Gaussian Slug Copula – ระเบียบวิธีจำลองแบบ Nonlinear Dependency ในตลาดทุน – 2552 Relative Numeraire Risk (RNR) – การสร้าง Benchmark Portfolio สำหรับการบริหารเงินสำรอง – 2553 Entropic Eigenvector Centrality (EEC) – Network Model เพื่อ วิเคราะห์ Systemic Importance ของ สง.  ที่ปรึกษาทางเทคนิคภายใน – แนวคิด ‘Quant Clinic’  งานสนับสนุน HR/KM ของ ธปท. – 8 Strategically Critical Knowledge (Gap) Areas

4 4 โครงสร้าง – ทีมแบบจำลองเชิงปริมาณและ วิศวกรรมการเงิน  Quantitative Models (QM) – Modelling  a system f( ) which takes in x to produce y – Optimisation  maximise/minimise f(x) such that x ‘feasible’ – Optimal Control  keeping system state s(t) ‘stable’ over time t  Financial Engineering (FE) – What do we mean by (financial) engineering? What about (financial) risk management? – Financial product  design, costing, productionize, delivery, maintenance, risk mitigation … – What do we mean by (financial) derivatives? What about pricing methodology? – Given one randomness dW(t) and two random variables x(t), y(t)  if we knew x(0), can we determine y(0)?

5 5 โครงสร้าง – ทีมแบบจำลองเชิงปริมาณและ วิศวกรรมการเงิน  Quantitative Models (QM)  [qm1] Optimisation Algorithms & Network Modelling  [qm2] Nonparametric, Semiparametric & Bayesian Inference  [qm3] Extremal, Chaotic & Cybernetic Stability  [qm4] Game Theory & Information Asymmetry   Financial Engineering (FE)  [fe1] Volatility Surface & Path Dependency  [fe2] Term Structure & Market Models  [fe3] Default Correlation & Copula Functionals  [fe4] Levy Process & Stochastic Dynamics 

6 6 วิกฤติการเงินโลก – เรื่องมันใหญ่ขนาดไหน เชียว ?  Boom “Low interest rates and large inflows of foreign funds created easy credit conditions for a number of years … fueling a housing market boom and encouraging debt- financed consumption … USA home ownership … from 64% in 1994 ( … since 1980) to … high of 69.2% in 2004 … Between 1997 and 2006, the price of the typical American house increased by 124% … two decades … 2001 … home price to 3.1 times … household income … 4.6 in 2006 … While … consumers were saving less … borrowing and spending more... Household debt … $705 billion at yearend 1974, 60% of disposable personal income, to $7.4 trillion at yearend 2000, and finally to $14.5 trillion in midyear 2008, 134% … 2008 … typical USA household owned 13 credit cards, with 40% … carrying a balance, up from 6% in 1970.” – [http://en.wikipedia.org/wiki/Subprime_mortgage_crisis]http://en.wikipedia.org/wiki/Subprime_mortgage_crisis

7 7 วิกฤติการเงินโลก – เรื่องมันใหญ่ขนาดไหน เชียว ?  Bust “USA subprime mortgages … estimated at $1.3 trillion as of March 2007 … Between … share … relative to total originations ranged from 18%-21%, versus less than 10% in and during 2007 … third quarter of 2007, subprime ARMs … 6.8% of USA mortgages outstanding … accounted for 43% of the foreclosures … 16% … either 90-days delinquent or … foreclosure … January 2008 … delinquency … risen to 21% and by May 2008 … 25% … August 2008, 9.2% of all U.S. mortgages outstanding were either delinquent or in foreclosure … September 2009… 14.4% … Ten states accounted for 74% of the foreclosure filings during 2008; the top two (California and Florida) represented 41% …” – [http://en.wikipedia.org/wiki/Subprime_mortgage_crisis]http://en.wikipedia.org/wiki/Subprime_mortgage_crisis

8 8 วิกฤติการเงินโลก – ( เรา / ท่าน ) คิดว่าอะไร เกิดขึ้น ?  The ‘Usual Suspects’ – CDS/CDO/CDO 2 …  complex derivatives too complex? – Basel2/IAS39 …  risk-based supervision, acct. std. failed us? – Greenspan/Bush …  unchecked market capitalism, war spending?  The ‘Not So Usual’ – US$ being the world’s ultimate numeraire currency  unchecked US deficits, Chinese reserves keeping US interest rates artificially low, household spending and ‘investment’ high, and the financial system generally over geared (leveraged). – Not ‘CDO/Basel2/Greenspan’ as such, but the intellectual environment which  their universal/overwhelming adoption/popularity in those preceeding years! – 2 paradoxes: losses to both buyers & sellers, low & high CDO tranches! – Wherein lies complexity: products? regulations? financial network?

9 9 วิกฤติการเงินโลก – ( เรา / ท่าน ) คิดว่าอะไร เกิดขึ้น ? “During May 2010, Warren Buffett and Paul Volcker separately described questionable assumptions or judgments underlying the U.S. financial and economic system that contributed to the crisis. These assumptions included: 1) Housing prices would not fall dramatically; 2) Free and open financial markets supported by sophisticated financial engineering would most effectively support market efficiency and stability, directing funds to the most profitable and productive uses; 3) Concepts embedded in mathematics and physics could be directly adapted to markets, in the form of various financial models used to evaluate credit risk; 4) Economic imbalances, such as large trade deficits and low savings rates indicative of over- consumption, were sustainable; and 5) Stronger regulation of the shadow banking system and derivatives markets was not needed.” – [http://en.wikipedia.org/wiki/Subprime_mortgage_crisis]http://en.wikipedia.org/wiki/Subprime_mortgage_crisis

10 10 FE & Crisis – ตัวย่อ / ศัพท์ต่าง ๆ ที่เกี่ยวข้อง  Collateralized Debt Obligation (CDO) – Equity/Junior/Mezzanine/Senior/Super Senior Tranches – CMO, CBO – CDO 2  ซาลาเปาใส้ซาลาเปา  Credit Default Swaps (CDS)  Special Investment Vehicle (SIV)  Asset Backed Commercial Paper (ABCP)

11 11 FE & Crisis – ‘CDO Losses’  Key Parameters – Default Rate (‘Lamba’)  or Probability of Default (PD)   Junior Tranche  – Default Correlation (‘Rho’)   Senior/Super Senior Tranche  – Tranche Width   Yield   Riskiness   Key Mechanisms – Senior/Super Senior Tranches used as asset to back ABCP – ABCP sold to ‘safe-money’ funds (provident, state payroll, co-op, etc.) – SIV doesn’t take deposits; it issues ABCP. – Big banks are SIV sponsors.

12 12 บทเรียนทั้ง ๘  1. Never again fall for a ‘new paradigm’  2. Whenever institutional investors became frustrated with low yields …  3. That ‘reputational’ risk underlines most, if not all, financial risks  4. That interplay between risk and correlation is far from understood  5. The network effect, the dynamics of chaos, the paradox of stability  6. Ignorance and greed are convertible commodities  7. The simpler the (inherrently complex) products, the more dangerous  8. ‘Lose Both Ways’ Paradox, Buyers & Sellers, Junior & Senior Tranches

13 13 1. Never again fall for a ‘new paradigm’  What’s the lesson?  New/unique?  – Actually this is the lesson from the ‘dot com’ productivity-changes-everything crisis.  Picked up?   – The public already appreciate the fact that securitization does not remove risk, merely relocates it …  Reveals gaps?  – … but how many even suspects, never mind understand, that securitization actually increases risk (of supervisory gap, and especially of making hitherto impossible credit leverage possible)?  Lasting lesson?  – Every generation thinks it’s better (smarter, more immune, etc.) than the last.  Reform relevant?  – This is the one area where regulatory-supervisory reform runs into a paradox: the more a bank regulator/supervisor tries to make itself relevant to the banking environment it oversees, the more susceptible it is to falling for the ‘new paradigm’ thinking that will surely return. Nonetheless, ‘never fall for that again’ is not very amenable to putting into regulatory-supervisory codes.

14 14 2. Whenever institutional investors became frustrated with low yields …  What’s the lesson?  New/unique?  – This is one of the oldest lessons … going back to the Asian Crisis of 1997 and the Latin American Debt Crisis in the 80’s …  Picked up?   – … that has not received due attention by policy reformers and general public alike.  Reveals gaps?  – As far as cross border issues are concerned, we already plenty research done on the effects of short-term capital flows, yet such knowledge tends to seem irrelevant and get ignored whenever capital rich countries begin to hunt for higher yields abroad (and recipient countries again feel capital hungry) is hard to deny.  Lasting lesson?  – After all, searching for higher ‘risk-adjusted’ yields is the primary mandate of all institutional investors.  Reform relevant?  – The upshot is that many central banks begin to see that rapid capital accumulation is no more inherently desirable than rapid growth in any one sector of the economy. What is needed is to ensure central bank’s ability and willingness to ‘take away the punch bowl’ does not wax and wane in cycles.

15 15 3. That ‘reputational’ risk underlines most, if not all, financial risks  What’s the lesson?  New/unique?  – !!!  Picked up?  – !!!  Reveals gaps?  – !!!  Lasting lesson?  – That is, once our financial risk classification scheme gets reshuffled.  Reform relevant?  – It’s about time the policy sector overhauls its understanding of risk, rather than let private technology drives it.

16 16 4. That interplay between risk and correlation is far from understood  What’s the lesson?  New/unique?  – Ever since ‘Black Monday’, people began to take ‘correlation measure’ less for granted.  Picked up?  – Most people don’t even realise that CDO trading is ‘rho’ trading.  Reveals gaps?  – Such issues were coming to the fore well before the crisis.  Lasting lesson?  – But only if the public finally becomes comfortable with copulas!  Reform relevant?  – Ironically, this is what I methinks should form the core of Basel 3!!

17 17 5. The network effect, the dynamics of chaos, the paradox of stability  What’s the lesson?  New/unique?  – were already well accustomed to the ‘cruder’ concept of crisis contagion.  Picked up?  – Thanks to many bouts of US congressional hearings, where “too systemic to fail” could be uttered w/o raising eyebrows.  Reveals gaps?  – Otherwise we wouldn’t be surprise that a So-Cal crisis could hit Iceland so hard!  Lasting lesson?  – One only has to note how ‘network effects’ have entered mainstream vocabulary.  Reform relevant?  – That’s what ‘Quant Teams’ at advanced central banks are working on.

18 18 The Awakenings – Network Effects Evident from Crisis “On 16 November 2002, the first official case of Severe Acute Respiratory Syndrome (SARS) was recorded in Guangdong Province, China. Panic ensued … On 15 September 2008, Lehman Brothers filed for Chapter 11 bankruptcy in a New York courtroom in the United States. Panic ensued … The flap of a butterfly’s wing in New York or Guangdong generates a hurricane for the world economy. The dynamics appear chaotic, mathematically and metaphorically … … Both events were manifestations of the behaviour under stress of a complex, adaptive network. Complex because these networks were a cat’s-cradle of interconnections, financial and non- financial. Adaptive because behaviour in these networks was driven by interactions between optimising, but confused, agents.” Haldane, Rudolf ( ), “Rethinking the Financial Network”, Speech by the Bank of England’s Executive Director of Financial Stability, delivered at the Financial Student Association in Amsterdam.

19 19 The Awakenings – Network Effects Evident from Crisis (cont.) “Connectivity... [1] knife-edge dynamics … lengthy period of seeming robustness … punctuated by an acute period of financial fragility … robust-yet-fragile property … [2] networks have been found to have a thin middle and long, fat tails … [3] key nodes can introduce short-cuts connecting otherwise detached local communities … small world property … the likelihood of local disturbances having global effects.” “Feedback … In epidemiology … behavioural responses typically take one of two forms … During this financial crisis … [1] The ‘hiding’ has taken the form of hoarding … liquidity. And the [2] ‘flight’ … from … toxic assets.” “Diversity … In explaining the collapse in fish and finance, lack of diversity seems to be a common denominator … [1] business strategies came to be replicated across the financial sector … [2] Management of the risks …amplified this homogeneity. Basel II provided a prescriptive rule- book... Ratings were hard-wired into regulation. Risk models … looked and acted alike …” Ibid.

20 20 Example ‘Classic’ Topologies

21 21 Example ‘Hybrid’ Topologies (cont.)

22 22 Case Studies: ‘Weighted Connectivity’ Networks

23 23 Why Network Model of Crisis Contagion?  Epidemically speaking: there’re just 3 types of diseases in the world! – Radiation sickness/disease/syndrome – A matter of topography, geology & meteorology Banking in a ‘failed state’  unstable regime, analytics irrelevant … – Water-/Air-/Vector-borne diseases – Degree of exposure vs. amount of immunity Banking in the pre-deregulation era  focus on capital adequacy, liquidity buffer … – Sexually transmitted diseases – Network of privately interacting agents Banking in a capitalist’s globalisation context  not all interventions equally network critical … Eames, K.T.D. & Keeling, M.J. (2002), “Modeling Dynamic and Network Heterogeneities in the Spread of Sexually Transmitted Diseases”, PNAS, vol. 99, no. 20, pp – Newman, M.E.J. (2002), “Spread of Epidemic Disease on Networks”, Physical Review E, vol. 66, no , pp

24 24 What exactly is a network model?  Network (Graph) = collection of Nodes (Vertices) with (inter)connecting Edges (Arcs) – Each of the n nodes  individual bank, financial sector, national economy … – Each of the (at most) n 2 edge  channel of propagation, risk transfer, reputational linkage … – An edge may be directed (w/ an arrowhead) or simply undirected. – An edge may be weighted (associated w/ number) or simply unweighted. Barabasi, Albert-Lazlo (2002), Linked: The New Science of Networks, [New York: Perseus].  (Square) Adjacency Matrix = to account for the n 2 edges in a network with n nodes – Symmetric  undirected graph  bank i and bank j are interconnected in some way – Asymmetric  directed graph  bank i impacts bank j but not necessarily vice versa – Entries = zero’s and one’s  unweighted graph  whether there is a dependency – Entries = non-negative reals  weighted graph  how strong is each dependency  Network Topology = ‘stylised’ structure/architecture of nodal interconnectivities – i.e. ring, mesh, star, cascade, hierarchical, fully/randomly connected … [http://en.wikipedia.org/wiki/Network_topology]http://en.wikipedia.org/wiki/Network_topology

25 25 What can we do with it?  (Level 1) Static Network Analysis – derived from mathematical/statatistical properties – Network Characterisation e.g. Hierarchical? Sparse? Symmetry? Can all nodes be reached? – Path (‘Criticality’) Analysis e.g. What is the shortest path from i to j ? Is node i a bottleneck? – (Node) Centrality Measure e.g. Which node is the most central? In what sense is it central? Bech, Chapman & Garratt (2008), "Which Bank Is the “Central” Bank? An Application of Markov Theory to the Canadian Large Value Transfer System", FRBNY Staff Report, no Brin, S. & Page, L. (1998), “The Anatomy of a Large–Scale Hypertextual Web Search Engine”, Computer Networks and ISDN Systems, vol. 30, pp. 107–117. Markose et al. (2010), “Too Interconnected To Fail: Financial Contagion and Systemic Risk in Network Model of CDS and Other Credit Enhancement Obligations of US Banks”, University of Essex Discussion Paper, no. 683.

26 26 What can we do with it? (cont.)  (Level 2) Dynamic Network Analysis – simulation based on assumed ‘update rules’ – Robustness Analysis, Scenario-based/Parametric Stress Testing … e.g. failure of some bank(s) chosen at random, doubling the strengths of association … Canedo, J.M.D. & Jaramillo, S.M. (2009), “A Network Model of Systemic Risk: Stress Testing the Banking System”, Intelligent Systems in Accounting, Finance and Management, no.16, pp. 87–110. Gai, P. & Kapadia, S. (2010), “Contagion in Financial Networks”, Bank of England Working Paper, no  (Level 3) Agent-Driven Network Analysis – nodes endowed w/ decision making – Each node = economic agent (optimising, game-playing, copycatting …) e.g. opt to honour some obligations before others, to access discount window, to default … Fioretti, Guido (2004), “Financial Fragility in a Basic Agent–Based Model”, Unpublished Manuscript. Terano, et al. (2007), “The Socio-Network Model with an Agent-Based Approach”, in Springer Series on Agent Based Social Systems, vol. 3 Agent-Based Approaches in Economic and Social Complex Systems IV, pp , [Springer: Japan].

27 27 6. Ignorance and greed are convertible commodities  What’s the lesson?  New/unique?  – Talk about oldest trick in the book …  Picked up?  – … that unfortunatly will almost always work, again and again, with ‘base’ humanity!  Reveals gaps?  – This is no technological or knowledge problem, after all.  Lasting lesson?  – See above comments!  Reform relevant?  – While ignorance and greed cannot be regulated away, conscientious, methodical, and accountable processes of making financial decisions certainly can.

28 28 7. The simpler the (inherrently complex) products, the more dangerous  What’s the lesson?  New/unique?  – Especially with the public so (wrongly) obsessed with ‘toxic assets’ like CDS!  Picked up?  – Lest we forget … it’s always the butler …  Reveals gaps?  – But in the end it’s the innocuous gaps that really matter most.  Lasting lesson?  – Regulators should take this up …  Reform relevant?  – … as this means they can do so much (financial stability enhancement) with so little (advanced quantitative finance).

29 29 8. ‘Lose Both Ways’ Paradox, Buyers & Sellers, Junior & Senior Tranches  What’s the lesson?  New/unique?  – Even war has profiteers, but this crisis only losers from all directions (of financial exposure).  Picked up?  – One needs a certain amount of technical knowledge to appreciate.  Reveals gaps?  – There’s a vast difference between those ‘in the know’ and those who are ‘not’.  Lasting lesson?  – Once acquired, this is not a particularly difficult concept to hold on to.  Reform relevant?  – Regulators are too often (at least accused of) being able to focus on one crisis propagation path at a time.

30 ขอบพระคุณครับผม Q&A ดร. พูมใจ นาคสกุล ทีมแบบจำลองเชิงปริมาณและวิศวกรรมการเงิน (Quantitative Models & Financial Engineering) ๒๙ กรกาคม ๒๕๕๓


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