Centralne banke potrebujejo bolj kompleksne modele

Aleš Praprotnik

Justin Lyon, ustanovitelj in direktor podjetja Simudyne, je napisal zanimiv članek, v katerem razglablja, zakaj ekonomska veda (kot tudi finančni ministri in regulatorji, ki jo uporabljajo) nujno potrebuje prenovo na ravni uporabnih modelov. Ugotavlja, da ti še vedno temeljijo na zastarelih idejah in konceptih, ki slonijo na okvirih učinkovitosti trgov, ekonomskega ravnovesja in analize izoliranih subjektov in ki se v uradnih institucijah le s težavo umikajo pristopom in orodjem, ki upoštevajo medsebojno povezanost, kompleksnost in nelinearnost finančnega in ekonomskega sistema. Poudarja, da bo ekonomska veda morala integrirati spoznanja iz področij biologije, biokemije, meteorologije in drugih znanosti, ki proučujejo prilagodljive nelinearne kompleksne sisteme in obenem opaža, da se hedge skladi vse bolj poslužujejo tovrstnih modelov, saj se zavedajo, da le-ti bolj realistično prikazujejo kompleksno stvarnost finančnega sistema, kar jim v konkurenčnem okolju, v katerem se nahajajo, lahko zelo koristi.

Hedge funds are filled with the really bright guys (‘Masters of the Universe’ – to quote Tom Wolfe – e.g., the twenty year top-performing ex-mathematician James Simon of Renaissance Capital). These bright guys use their superior modelling and risk management techniques in a highly leveraged way to make regular exceptional profits for their partners and themselves. Even so, only a few practitioners are believed to have begun to use models that take into account irrational and collective behaviour hence the frequent, recent, and regular whining about ‘once in a hundred year’ events and ‘25 sigma deviations’ (e.g. from a Goldman’s hedge fund), but see recent reports in Nature and New Scientist and work by the Econophysics community.

While many of these guys have a strong math or physics background, the rest of the market (and its regulators and risk managers) is filled with classical traders (typically chartists and people who sense what’s happening by talking to people and looking at patterns on screens) and people who have, since the eighties, been to business school where they were taught Standard Finance Theory (SFT) and its companion the Efficient Market Hypothesis. SFT comes from a world where computation is expensive and short cut assumptions (such as the use of only the first two moments of a distribution – mean and variance – which was also assumed to be Gaussian) justified the arguments in favour of the random walk approach to investing, the CAPM, use of alpha and beta, adoption of the brilliant Black-Scholes derivative pricing formula based on the stochastic calculus and so on. These arguments have been rattling on for years (and it’s the conventional SFT approach that seems to have been adopted by regulators and risk managers) although there is lots of evidence to suggest that chartists make their money from adopting the opposite viewpoint – which seemingly is related to the market impact of traders themselves. It seems reasonable to suggest that, in the future, leading hedge funds can gain additional benefit from a smarter approach to modelling that begins to incorporate more of the micro (types of trader/institutional, their states and probable behaviours under various market conditions and its evolution) as well as the existing macro level of modelling.

Following some spectacular disasters in the eighties (and complaints about Japanese banks getting unfair advantages by excessive balance sheet growth for the capital employed), regulators came up with Basel I (and then Basel II) which imposed controls on banks’ risk-adjusted capital ratios but without accompanying it with proper investigation of how they were implemented (hence the drive by most banks to use off-balance sheet vehicles and other tactics to get round the regulations and grow their earnings). Until recently nobody paid much attention to what happens at a system level when everyone is corralled into adopting the same mechanism – another unintended consequence of standardised regulation was seen in the raging arguments over the implementation of the ‘mark to market’ regime. One of the biggest issues in the 2008 crisis has been a largely non-systemic approach to modelling and measuring risk i.e., looking at an individual institution’s portfolio of instruments, their past correlations, risk parameters etc. as if it could, in a crisis, act in isolation when regulation has, of course, created systemic correlations which come into play as soon as systemic risk begins to arise. A smart hedge fund might have anticipated these probable behaviours and taken advantage of them at an early stage.

This lack of insight into complex system behaviour and its opportunities (and threats) may possibly be put down to the prevalence of a quantitative mind set derived from the relatively predictable world of mathematics and physics, which may be fine while the complex financial system is in one of its relatively stable states. When that is no longer the case (Black Swan events?), rather more powerful mental models coming from biology (e.g. evolutionary behaviour) and from non-linear complex adaptive systems (e.g., climate or enzyme kinetics in biochemistry) may be  needed. Indeed one biological concept – punctuated equilibrium – could fit the 2008 credit crunch very well; the post crunch world will be quite likely to have rather different dynamics.

Models built using insights from complex adaptive systems, that is, using agent-based modelling, system dynamics and so on, are transforming fields from biochemistry to epidemiology to ecology to weather forecasting, and whilst penetration into hedge funds is accelerating, uptake by policymakers remains slow. Advanced simulations can not only be used to make vast amounts of money for hedge funds; they must be used on a daily basis to design economic policies in the 21st century so we avert tomorrow’s crisis.

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