Inflation has come back with a bang following Covid-19, the war in Ukraine and associated spikes in prices, and previous loose monetary policies – even some green stimuli now – having all driven it up. A panel of banking and financial services practitioners at QuantMinds International 2023 in London discussed where inflation might go next and how best to hedge, model and approach this risk, while incorporating it into quant strategies.
Panellists from the buy- and sell-side, operating under the anonymised Chatham House rules, from Nordea, ANZ Global Markets, Crescendo Asset Management and Amundi were joined by a visiting lecturer from Queen Mary University in London, at the InterContinental O2 Hotel opposite the Canary Wharf financial centre in the UK capital, to debate inflationary pressures and responses.
“I don’t have a crystal ball,” was an opening line, but the panel promised to explore the trends, available hedging products, modelling implications and how best to approach and manage credit portfolio and inflationary risk moving forward nonetheless.
One panellist expected the “US to be a bit above” the generally agreed below 3% inflation figure polled from most economists. “Conversely, we expect US inflation to remain just above 3%.”
“You could argue we should have expected the recent steep rise we’ve seen globally,” continued another panellist, citing Covid-19 stimuli and the war in Ukraine. “But the forecasts from central banks haven’t been great either.”
Luckily there are number of hedging products, from traditional instruments like swaps and derivatives, “but many clients are demanding more innovative inflation protection products.” Some available products include:
- Inflation-linked bonds: In the US these can be linked to CPI, or the RPI metric in the UK, which admittedly are not as liquid as nominal bonds but do provide protection, at a premium
- Zero coupon inflation swaps (ZCIS): In dollars, euros, pounds or other currencies. These can be shorter-term, enabling more proactive hedging than monthly or longer-term options. “You can even make or see markets move with them,” commented one panellist
- Inflation options: Traditionally used for foreign exchange (FX) purposes but are very liquid and fast moving, so “be careful if you use them to manage inflation [and associated FX] risk.”
Perhaps the best way to deal with the inflation risk and volatility that is evident is via multiasset correlated proxy hedging models, “which are great for quants”. These can give liquidity when it is needed and cover inflation risks by compiling a list of overlapping impact factors, including things such as:
- Commodities pricing, as inflation tends to rise when oil prices do.
- Currencies, as inflation and interest rates deployed as a mechanism to slow it, directly feed into FX.
- Inflation indexes.
- And so on
“These days we’ve got a lot more data to help with modelling as well, so we aren’t just reliant on traditional metrics when building a model,” commented a panellist. He cited Broadway ticket sales, illustrating consumer sentiment and emissions data, illustrating trends in manufacturing output as just two examples of other useful modern data-led metrics. When allied to traditional metrics accuracy should go up.
Artificial intelligence (AI) can help mine these non-traditional data streams more effectively too, while also giving central banks another tool to improve their forecasting. “ChatGPT and generative AI might help with auto correlation modelling as well,” speculated one panellist.
Certainly, the new era of inflation evident since 2020, which is susceptible to supply chain shocks from geopolitical conflicts, represents a new regime to what preceded it the decade before. Inflation didn’t really exist from 2008 onwards as globalisation bit, but that was the exception that can no longer be relied on.
“This new regime means traditional risk frameworks struggle,” said a panellist, advocating for the use of new model-feeding metrics; increased use of AI; and new inflation products. “As computational capacity increases it can predict inflation on a more real-time basis in the future,” concluded a panellist. They were perhaps thinking of the impending power of quantum computing where the traditional binary 0s and 1s are replaced, and computation decisioning becomes exponentially faster. This increase in computing power could be very beneficial for funds that do inflation modelling and forecasting.
AI large language models (LLMs) and natural language processing (NLP) applications can also be very useful in getting value from text-based data in the future.
It seems even computing is suffering from ‘inflationary’ Moore’s law pressures at the moment. Unlike the general economy, however, this rise – in computing power – could be a positive.