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.
Volatility returned to markets in early February, sparked by strong US wage growth data. After months of calm, the S&P 500 equity index fell by 4% on 5 February and the VIX – a measure of US equity volatility that is sometimes referred to as Wall Street’s “fear gauge” – experienced its largest one-day move in its 28-year history. Interestingly, measures of volatility in other markets, including interest rates and currencies, moved by much less. So what caused the outsized spike in the VIX? Some of the rise was linked to rebalancing flows associated with VIX exchange-traded products (ETPs), which can amplify moves in the volatility market. The events have also led to some questions whether developments in VIX ETPs can also affect the S&P 500 itself –whether the ‘tail’ can wag the ‘dog’.
James Purchase and Nick Constantine.
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.