Externalities – and cheating – in the financial crisis
Much of recent innovation has led to products that make cheating the public easier.
Joseph Stiglitz, 2019.
How the UK system of market-based finance might behave under stress is evidently an important policy issue, as witnessed by a Bank of England working paper by Aikman et al. (2019). Key features discussed are creditor runs that can lead to fire sales, with prices falling enough to threaten insolvency.
In planning for the future, one can learn from the past – and the near collapse of the US shadow banking system is a case in point. Some of the US’ most prestigious banks seemed to have found a form of alchemy where mortgage lending across the country could be financed by raising funds at low rates on Wall Street. On the asset side, the secret was securitising subprime assets, on the liability side – tapping money markets for wholesale funding (Tooke 2018). This seems to contradict the Scottish proverb ‘Ye can ne make a Silk-Purse of a Sowe’s Luggs’,1 but the credit rating agencies did their best to help out!2
What of the Basel rules to limit risk taking? Alas, thereby hangs a fallacy of composition on the part of regulators – compounded by liquidity illusion on the part of shadow banks.
In a new paper (Miller and Zhang 2019), we use the model of Shin (2010: Chapter 3) to study the amplification effect that can operate despite value at risk (VaR) regulation. It’s rather like what happened on Wall Street in the 1920s, when rising share prices increased the value of collateral of those speculating on the stock market, so they could borrow more, so prices went higher, and so on … until the crash of 1929. We also look at the crash of 2008!
Amplification under the VaR rules is indicated in the figures that follow. Figure 1 illustrates the point that greater participation of highly-leveraged, risk-neutral players in the market will raise the price of risk assets, measured on the vertical axis – with q denoting the expected payoff and q-z the lowest payoff. On the horizontal axis is the equity of shadow banks, given a fixed supply of risk assets, normalised to unity.3 As risk-neutral shadow banks effectively ‘crowd out’ risk-averse lenders, the market price rises from L (where the price matches q-z) to H (where the price matches q). Even though shadow banks are risk- neutral, the price can lie below q as VaR regulation requires banks’ own equity to cover the downside risk – so low equity limits their demand. With initial equity of e0 for example, the asset price will be p0, as shown at point A.
Figure 1 Price of risk assets higher if shadow banks ‘crowd out’ risk-averse lenders
Charles Goodhart (2011) maintained that the VaR regime suffered from a ‘fallacy of composition’, since the regulators believed that ensuring each bank covered downside risk on its portfolio would ensure the safety of the system as a whole. Considerable amplification of news shocks will, indeed, occur if banks ‘mark their assets to market’, for the equity base of the leveraged sector becomes endogenous (pro-cyclical), as indicated by the line MM passing through point A in Figure 2.
‘Good news’ of a reduction in downside risk (from z to z), which raises asset prices from A to B without marking to market, will get amplified when equity is revised to incorporate capital gains. So, after a series of markings, equilibrium arrives at C. Shadow banks will doubtless be gratified with their capital gains and increased market share, and the VaR regulations have been satisfied – so why worry?
Figure 2 Asset prices and equity rise in response to ‘good news’ of lower downside risk
What if there is ‘bad news’? “The amplifying mechanism works exactly in reverse on the way down”, writes Shin (2010: 35). In fact, however, things can get much worse if the ‘bad news’ triggers creditor panic, and withdrawals lead to fire-sales that threaten insolvency. As Bernanke (2019) explains:
Panics emerge when bad news leads investors to believe that the ‘safe’ short-term assets they have been holding may not, in fact, be entirely safe. If the news is bad enough, investors will pull back from funding banks and other intermediaries, refusing to roll over their short-term funds as they mature. As intermediaries lose funding, they may be forced to sell existing loans and to stop making new ones.
In our work, where a bank run is added to Shin’s account of ‘bad news’, it leads to prompt systemic insolvency.4 In reality, of course, the US Treasury responded with capital injections (of over 2% of shadow bank assets) and the Fed provided liquidity support to those shadow banks not yet taken over,5 and purchased mortgage-backed assets under quantitative easing.
Without this, it seems clear that the shadow banks would have become insolvent, which is what Gertler and Kiyotaki (2015) concluded in one of a series of papers they have written “to develop a simple macroeconomic model of banking instability that features both financial accelerator effects and bank runs”. For them, the ‘bad news’ takes the form of a negative productivity shock of 5% on the payoff on bank assets, which triggers a fall of 15% in asset values if a bank run follows – enough to wipe out shadow bank equity. As the run is attributed to a ‘sunspot’, however, it seems that they are twice victims of bad luck – from a large unanticipated productivity shock plus from an unfortunate incidence of creditor panic.
This is a very different perspective from Akerlof and Shiller (2011: Chapter 2) who attribute insolvency to bad faith and not bad luck, alleging that banks and credit rating agencies had effectively colluded to mis-sell securitised assets way above their true value.6 In their terminology, the ‘good news’ outcome was a crash-prone ‘phishing equilibrium’, where those with better information secured profits by mis-selling to those with less. This is a serious allegation – yet, as far as we know, no legal action has been taken against them. Could this be because, in a raft of court cases, US shadow banks – and their European counterparts – have escaped criminal charges by confessing misbehaviour, promising to reform and paying large fines7 (with the credit rating agencies also making such deferred prosecution agreements regarding collusive behaviour)?8
Technically, the Shin model, with ‘fake news’ and bank runs9, and that of Gertler and Kiyotaki, with productivity shocks and sunspots, produce similar results, namely, losses by highly leveraged shadow banks leading to fire-sales and potential collapse. Ethically, however, they differ greatly. How to choose?
Perhaps one must go beyond simply comparing economic models by considering legal and political factors? Findings in US courts of law have already been mentioned – with banks and credit rating agencies subject to numerous and costly deferred prosecution agreements. No senior executives have been sent to prison in the US, nor in the UK, however.10 What of political repercussions?
For the US, Michael Lewis sees the election of Trump as president as “an unfortunate aftershock” of the financial blunders of the last decade. In his view:
The collapse of the US mortgage market and the subsequent bailout of the banks left Americans of varying political views feeling that the system was rigged. I think of this as echoing the 2008 financial crisis (Silverman 2016)
For the UK, the consequences may be even more dramatic. The geographical correlation between suffering post-crisis austerity and voting to leave the EU, leads Nicholas Crafts (2019) to argue that the ultimate costs of the financial crisis for the UK should include GDP losses on leaving. With the election of Boris Johnson as prime minister, leaving is much more likely – with some risk that exit without agreement may lead to the break-up of the UK.
Are these the results of ‘technology shocks and sunspots’, or signs that ‘something is rotten in the state of Denmark’?
Aikman D, P Chichkanov, G Douglas, Y Georgiev, J Howat and B King (2019), “System-wide stress simulation”, Bank of England staff working paper 809.
Akerlof, G A and R J Shiller (2015), Phishing for phools: The economics of manipulation and deception, Princeton University Press
Bernanke, B (2018), “Financial panic and credit disruptions in the 2007-2009 crisis”, Brookings blog, 13 September.
Crafts, N (2019), “The fall in UK potential output due to the financial crisis: A much bigger estimate”, CAGE working paper 399, University of Warwick.
Danielsson, J, P Embrechts, C Goodhart, F Muennich, O Keating, C Renault and H S Shin (2001), “An academic response to Basel II”, Financial Markets Group, LSE, special paper 130.
Goodhart, C (2011), The Basel committee on banking supervision: A history of the early years 1974-1997, Cambridge University Press
Gylfason, T (2019), “Ten years after: Iceland’s unfinished business”, in Aliber, R Z and G Zoega (eds), The 2008 Global Financial Crisis in retrospect, Chapter 16, p. 297-325, Palgrave Macmillan
Miller, M and L Zhang (2019), “Externalities and financial crisis: Enough to cause collapse?”, CEPR discussion paper 13484.
Miller, M, S Rastapana and L Zhang (2018), “The blind monks and the elephant: Contrasting narratives of financial crisis”, The Manchester School 86 (S1): 83-109.
Rakoff, J S (2014), “The Financial Crisis: Why have no high-level executives been prosecuted?”, The New York Review of Books, 9 January.
Shin, H S (2010), Risk and liquidity, Oxford University Press
Silverman, G (2016), “American psyche: Michael Lewis on the triumph of irrational thinking”, Financial Times, 9 December.
Tooze, A (2018), Crashed: How a decade of financial crisis changed the world, Allen Lane.
 ‘You can’t make a silk purse out of a sow’s ear’.
 According to Akerlof and Shiller (2015, p.34), “Moody’s gave 45,00 mortgage related securities a triple A rating (for the period 2000 to 2007) [compared] with only six US companies that were similarly rated AAA (in the later year of 2010)”.
 So when equity e matches z, all downside risk is covered by shadow banks’ own resources.
 While we provide some illustrative calculations and diagrams in Miller and Zhang (2019), a much more detailed numerical analysis is provided in Gertler and Kiyotaki (2015), discussed below.
 Tooze (2018, chapter 8) provides graphic detail on how the Fed acted as Global Lender of Last Resort.
 “The new financial system, so dependent on borrowing short-term, was on the verge of total collapse when it was discovered that much of its assets had been too highly rated, and were rotten”(Akerlof and Shiller 2011, p.36).
 As described by Rakoff (2014).
 Well before the crisis, in fact, a group of economists at LSE had written to the Basel Committee on Banking Standards warning that the credit rating agencies were not to be trusted, see Danielsson et al. (2001) and Shin (2010, p.171).
 As discussed in Miller et al. (2018), for example.
 Unlike the case of Iceland where they have, Gylfason (2019).
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