Over the past 20 years, the share of outstanding corporate bonds rated BBB, the lowest investment-grade rating, has more than doubled. This has left a large volume of securities on the edge of a cliff, from which they could drop to a high-yield rating and become so-called ‘fallen angels’. Some investors may be forced to sell ‘fallen angels’, for example if their mandate prevents them from holding high-yield bonds. And this selling pressure could push bond prices down, beyond levels consistent with the downgrade news. In this post we explore the impact that sales of ‘fallen angels’ could have on market functioning, finding that they could test the liquidity of the sterling high-yield corporate bond market.
Francis Breedon, Louisa Chen, Angelo Ranaldo and Nicholas Vause
Most academic studies find that algorithmic trading improves the quality of financial markets in normal times by boosting market liquidity (so larger trades can be executed more quickly at lower cost) and enhancing price efficiency (so market prices better reflect all value-relevant information). But what about in times of market stress? In a recent paper looking at the removal of the Swiss franc cap, we find that algorithmic trading provided less liquidity than usual, at worse prices, and that its contribution to efficient pricing dropped to near zero. Market quality benefits from a diversity of participants pursuing different trading strategies, but it seems this was undermined in this episode by commonalities in the way algorithms responded.
Risky asset prices plummeted following the collapse of Lehman Brothers in 2008. Whilst driven partly by deteriorations in fundamental news, these falls were amplified by ‘flighty’ investors that sold at the first signs of trouble. Conventional wisdom dictates that life insurers, with their long-term investment horizons, are better placed than most to ‘lean against the wind’ by looking through short-term fluctuations in asset prices. They could thereby stabilise prices when others are selling. But the structure of regulations can greatly influence insurers’ investment incentives. Using our model of insurers’ asset allocations, we find that new ‘Solvency II’ regulations reduce UK life insurers’ willingness to act as the white knights of financial markets, particularly in the face of falling interest rates.
Collateral – that is, securities pledged to secure loans and other counterparty exposures – plays an important role in supporting the efficient functioning of the financial system. It supports a vast range of collateralised transactions, including repo and derivatives, which are important for both market liquidity and funding liquidity. But can collateral market dynamics play a role in exacerbating financial stability risks? In this post we explore two risks arising from the behaviour of market participants in stressed conditions:The first risk is that in response to market stress demand for collateral temporarily exceeds supply, until prices adjust. The second is that, during market stress, constraints on dealers’ balance sheets mean they have insufficient capacity to move collateral across the financial system.
Since QE began, banks have had a lot more liquidity to make payments. But some have argued (in a nutshell) that banks are reliant on this extra liquidity to make their CHAPS payments and it would be difficult to remove it from the system. Our analysis shows that banks don’t need a great deal of liquidity to make their payments simply because they recycle such a high proportion of them. In practical terms, banks do not rely on high reserves balances to make their CHAPS payments so unwinding QE shouldn’t have any impact on banks’ ability to do just that. We also briefly go over the potential reasons for this such as the CHAPS throughput rules, the Liquidity Savings Mechanism, and the tiered structure of CHAPS.
CHAPS banks have oodles of liquidity and are not afraid to use it, as quantitative easing has meant banks accumulated unprecedented quantities of reserves. And in this liquidity-abundant world, banks are less likely to be concerned with how well they use tools for liquidity saving in the Bank’s Real-Time Gross Settlement (RTGS) infrastructure. And besides, the timings of liquidity-hungry payments are stubborn. They can’t always be retimed to optimise liquidity usage, and this means that the potential for liquidity savings in RTGS from the Bank’s Liquidity Savings Mechanism (LSM) is limited.
It’s been a while now since high-frequency-trading (HFT) made its debut in the financial market landscape. Initially, little was known about it and regulators and market participants alike were naturally concerned about its potential impact on markets. Nevertheless, over the past few years we have learned quite a bit more about HFT. So what’s the deal with HFT? This short blog post briefly describes the evolution of HFT, summarizes the current understanding of the impact of HFT on market quality and highlights some aspects of HFT activity that are still contentious. Regardless, I believe, the inescapable conclusion that so far emerges is that HFT has mostly had a positive impact on market functioning.