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Many business functions use automatically generated scenarios for pricing, hedging, and meeting regulatory and accounting requirements. These scenarios are generated with certain probabilities: real, risk-neutral, and so-called market-consistent ones. Some of these probabilities are appropriate for only one type of application, others for different ones. So is it possible to consistently combine them in automated…

Quite a bit, as it happens. Traditional Asset & Liability Management (ALM) assumes deterministic levels of assets and liabilities and focuses on interest rate, currency, and liquidity risks. Ideally, it should apply to the dynamic behavior of the entire balance sheet of an organization. To learn more, check out Alla Gil’s latest Global Association of Risk Professionals (GARP) column.

Like reality TV or Marmite, surprises aren’t to everyone’s taste. Sometimes they’re nasty (like the pandemic and the lockdowns it caused). Sometimes they’re nice (like the rapid market recovery prompted by unprecedented government stimulus). By definition, surprises are unexpected. No single market shock is a carbon copy of a previous one. And although we stress…

Like animal-friendly faux fur coats, synthetic data is manufactured – artificially created rather than resulting from actual events. It’s having a moment. Thanks to the failings of historical data during the pandemic, synthetic data approaches have been sprouting like mushrooms after the rain. In the financial services industry they’re being used for risk management and…

How can you use machine learning for Strategic Decision Making in finance? This was the subject of Straterix CEO Alla Gil’s case study lecture at the 3rd Annual Machine Learning in Quantitative Finance conference, a virtual event which took place on June 3 & 4. In case you missed it, you can download the full presentation here.

Pre-pandemic, life was pretty simple: work was from an office, pajamas were for bedtime, and revenue projections of financial institutions and corporations were fairly predictable. But now history has changed, how can modelers accurately project performance, while factoring in baseline and stressed scenarios? For the answer, check out Alla Gil’s latest column in GARP (SPOILER ALERT: it…

“There are known knowns…[and] known unknowns. But there are also unknown unknowns.” Donald Rumsfeld was referring to Iraq and WMDS, but the last part of that quote, about the things we don’t know we don’t know, could just as easily apply to risk management today. Uncertainty abounds. To deal with it, agility, and resilience, will…

The Terminator movie franchise left us with many memorable quotes, but the one from the unborn John Conner, relaed by the hero, Kyle Reese, in the 1984 original, is perhaps the most poignant. “The future is not set,” he says. Believe it or not, that may also turn out to be the most valuable lesson COVID-19 has…

On the surface, there’s a singular purpose behind the introduction of stress testing: ensure that the next unprecedented crisis doesn’t cause banks to blow up like they did in 2008/9. Of course, as with so many things in life, the law of unintended consequences means stress tests have morphed from being a single tool with…

The COVID-19 crisis found most financial models wanting. In response, modelers panicked: what models, and how many of them, were now needed to cope with the “new normal”? Adding more regression models with different drivers won’t help capture tail risk in advance. Applying models calibrated to historical data to stress scenarios is simply wrong. In my latest…

The coronavirus pandemic continues to rage globally. Vaccination campaigns have injected some much needed optimism, but they’ve also prompted thoughts to turn to what happens next. This is especially so when it comes to the world of finance and concerns about post COVID-19 risks.  To that end, our CEO, Alla Gil, recently gave a lecture…

Like the Titanic being unsinkable, banks’ models were meant to be unbreakable – able to withstand any scenario. They’d been perfectly validated, yet in the face of COVID-19, they failed. So how should we fix them? Instead of trying to fit square-pegged data into round-holed models, banks need to modify the models themselves, combining the…