This year marks 25 years since the failure of Barings Bank. On Sunday 26 February 1995, the 200-year old merchant bank blew up thanks to derivatives trading, which it believed was both risk-free and highly profitable. It was neither of these things. The firm’s star trader was illicitly pursuing a strategy akin to ‘picking up pennies in front of a steam-roller‘. The steamroller arrived in the form the Kobe earthquake. The star trader’s losses ballooned and he doubled up on his bets, unsuccessfully. Barings went bankrupt. The episode captured the public imagination, and helped lead to the creation of a new regulator in the UK.
Marco Bardoscia, Gerardo Ferrara and Nicholas Vause
Participants in derivative markets collect collateral from their counterparties to help secure claims against them should they default. This practice has become more widespread since the 2007-08 financial crisis, making derivative markets safer. However, it increases potential ‘margin calls’ for counterparties to top up their collateral. If future calls exceed available liquid assets, counterparties would have to borrow. Could money markets meet this extra demand? In a recent paper, we simulate stress-scenario margin calls for many of the largest derivative-market participants and see if they could meet them – including because of payments from upstream counterparties – without borrowing. We compare the sum of any shortfalls with daily cash borrowing in international money markets.
The financial crisis exposed banks’ vulnerability to a type of risk associated with derivatives: credit valuation adjustment (CVA) risk. Despite being a major driver of losses – around $43 billion across 10 banks according to one estimate – there had been no capital requirement to cushion banks against these losses. New rules in 2014 changed this.
Certain policy actions require a high level of precision to be successful. In a recent paper, we find that using margins on derivative trades as a macroprudential tool would require such precision. Such a policy could force derivative users to hold more liquid assets. This would help them to meet larger margin calls and avoid fire-selling their derivatives, which could affect other market participants by moving prices. We find that perfect calibration of such a policy would completely eliminate this fire-sale externality and achieve the best possible outcome, while simple rules are almost as effective. However, calibration errors in any rule could amplify fire-sales and leave the financial system worse off than if there had been no policy at all.
In 1995, Fischer Black, an economist whose ground-breaking work in financial theory helped revolutionise options trading, confidently stated that “the nominal short rate cannot be negative.” Twenty years later this assumption looks questionable: one quarter of world GDP now comes from countries with negative central bank policy rates. Practitioners have been forced to update their models accordingly, in many cases introducing greater complexity. But this shift is not just academic. Models allowing for a wider distribution of future rates require market participants to hedge against greater uncertainty. We argue that this hedging contributed to the volatility in global rates in early 2015, but that derivatives can also play an important role in facilitating monetary policy transmission at negative rates.
Financial market prices provide information about market participants’ Bank Rate expectations. But central expectations can be measured in different ways. Mean expectations, derived from forward interest rates, represent the average of the range of possible outcomes, weighted by their perceived probabilities. On the other hand, modal expectations, which can be estimated from interest rate options, represent the perceived single most likely outcome. Currently, these market-implied mean and modal expectations for the path of Bank Rate over the coming few years differ starkly, with the mode lying well below the mean. In this post we argue that this divergence primarily reflects the proximity of the effective lower bound to nominal interest rates.