Unconventional Insights From Inside a Large Investment Bank
- Can economic models predict the next big crash? Not quite yet. Forecasting crises with algorithms might be a long way off.
- Mika Kastenholz, former Global Head of Structured Macro Trading at Credit Suisse, learned about risk firsthand when he first began trading during the Global Financial Crisis.
- Learn about why Mika’s “not a fan” of naive sizing, why there’s reason to believe liquidity compression has bottomed out and what surprises him about 2023 so far.
Some of us are nostalgic about our formative years. Others can’t help but cringe.
Mika, who until recently served as Managing Director and Global Head of Structured Macro at Credit Suisse in Hong Kong, doesn’t have a background in finance or economics. He earned a Ph.D. in computational biophysics from the Institute of Physical Chemistry at ETH [Swiss Federal Institute of Technology Zurich].
A good friend who worked at a hedge fund recommended that Mika consider interviewing with a few banks and insurance companies before committing to a career in academia. He joined Credit Suisse in ’04 or ’05, initially in equity derivatives structuring and equity exotic structuring. By 2007, he pivoted to trading at Credit Suisse’s London office.
“This was at the onset of the 2008 great financial crisis,” he says. “It was clearly a formative year … because we were sometimes aghast to experience [that] kind of volatility.”
Experiencing things like gap risk and dried-up liquidity firsthand, instead of just reading about it, “clearly determined how I looked at risk going forward,” he adds. Later on, he wrote the book on his particular discipline (“Trading Derivatives: The Theoretical Minimum: Trading Vanilla, Exotic and Corporate Derivatives.”)
My co-host Hari Krishnan welcomed Mika to the latest episode of our Global Macro series on Top Traders Unplugged to talk about his unconventional career in finance, his takes on risk and market cycles and other insights on global, cross-asset-class macro trading.
Econophysics and the ‘holy grail’
You can’t always predict the trajectory of economic cycles by just looking at things like price, volume data and implied volatility. Even with a robust quantitative background, Mika was missing some key context until he made that realization.
In his case, the missing piece was “an understanding of how economic cycles work, which until then, I’d never studied,” he notes. “That was my way into thinking more in terms of global macro and how the interactions between, let’s say, central banks and the underlying economy plays out.”
Mika did a short stint as a research assistant with Didier Sornette, ETH’s Chair of Entrepreneurial Risks, whose work focuses on log periodic power-law models that aim to predict market crashes.
“I worked a bit with Didier on the model, because at the time, he wanted to have a bit of input from somebody who came from a more trading background … and it’s certainly a very interesting model. Sometimes it works and a lot of times, it doesn’t really work. But … Didier is obviously a brilliant mind on his own,” says Mika.
“Crisis prediction is almost the holy grail,” Hari observes. “If you could do that, then you could run masses of risk most of the time and then wait for the LPPL signal to flash and then you’d be golden. But I guess it’s a very, very hard problem to solve. And probably, given your experience in 2008, you’re well aware of the risks that can emerge even from a low volatility backdrop. I know 2020 was like that too.”
“If you think about it, crisis (or crash) prediction is, in a way, a subset of market timing. And market timing is inherently difficult.”
While quant-based crash forecasting is elusive, “it’s much easier to get your position sizing to a level of risk you’re comfortable with,” says Mika. “So instead of pinpointing the correct moment to enter and exit, try to develop something that sizes your positions according to your perception of risk and your risk capacity. I think that’s a much better way for most people to approach the problem than trying to find a particular holy-grail formula that’s going to give you the answer.”
Given that take on predictive technology, Hari wonders about Mika’s opinion on naive sizing — “when you scale positions according to one, over the historical volatility or some expectation of vol (volatility)?”
“Honestly, I’m not a big fan,” Mika replies. “Because it can lead to issues [in which] your position size might still be very large because vol is very low. We are right now in an interesting period when if you compare fixed income markets to equity markets, there’s a clear disparity between … fixed income market prices [and] volatility … if you look at simple metrics such as the MOVE, and then in the equity space if you look at the VIX.”
Ratio and risk
Mika is not suggesting that fixed-income market prices and volatility should trade at a certain ratio to each other.
“They’re quite differently defined, and they look at different things in the options space — in terms of implied volatility,” he says. “But there’s clearly a disconnect as we speak. Either the fixed-income market is completely paranoid or the equity market is too complacent about potential growth or slowdown in the economy.”
Hari asks: “If I saw something like that where the MOVE was high and the VIX (to use a simple proxy) was low, is your thinking — putting your macro hat on — should [I] be a seller of rates vol and a buyer of equity vol? Or is that far too dangerous given the spread risk?”
Unfortunately, Mika’s answer is ambiguous, even if we appreciate the honesty.
“It depends on the positions you’re running first,” he says. “If you start with an empty book, nobody’s going to criticize you for taking [on] that type of spread risk.”
Compression and resilience
“A lot of us have been quite surprised by how 2023 has played out so far,” says Mika.
He argues that, for example, “even if we understand that in the equity space, a lot of key performance is in the mega-cap space and semiconductor space. It’s still quite astonishing how resilient or range-bound the market has been between, let’s say, 3,800 or 4,200 in the S&P space.”
In that case, the first approach might be a “traditional or neoclassical view of how market cycles play out,” he explains. “That is to say, OK, first the central banks tighten. Then maybe between six and 12 months, leading indicators peak. Earnings decline or compress, margins compress, employment declines, and then suddenly we hit a recession. Inflation subsides and then we can actually cut rates again.”
Perhaps, if we’re currently in this particular classical cycle, we’re now “somewhere in between earnings compressing — [but] maybe not as much as people have feared,” Mika adds. “And it just doesn’t yet trickle down to the employment numbers as we would expect. The way to square that circle is to basically say, Well, there are just delays this time — possibly because we’ve put so much money into the system; the savings are still carrying the economy through.”
Liquidity and flow
Wherever we are in the economic cycle, things have changed quite a lot since 2008.
Howell’s broad liquidity metrics allow us to understand “how collateral is used to fund and roll the debt cycle in our global economy,” Mika explains.
“If you do that and construct indicators in [with a] business mindset, you can make an argument that maybe we’ve already bottomed [out] in terms of the liquidity compression we’ve had over the last six to 12 months,” he adds. “Which means we might be in a better place in terms of equity returns going forward.”
Was that a bit of optimism? We love to hear it.
This is based on an episode of Top Traders Unplugged, a bi-weekly podcast with the most interesting and experienced investors, economists, traders and thought leaders in the world. Sign up to our Newsletter or Subscribe on your preferred podcast platform so that you don’t miss out on future episodes.
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