Applying machine learning in capital markets: Pricing, valuation adjustments, and market risk
This article was a collaborative effort by Juan Aristi Baquero, Akos Gyarmati, Marie-Paule Laurent, Pedro J Silva, and Torsten Wegner, representing views from McKinsey & Company’s Risk Practice.
When the COVID-19 outbreak became a global pandemic, financial-markets volatility hit its highest level in more than a decade, amid pervasive uncertainty over the long-term economic impact. Calm has returned to markets in recent months, but volatility continues to trend above its long-term average. Amid persistent uncertainty, financial institutions are seeking to develop more advanced quantitative capabilities to support faster and more accurate decision making.
This article, first published in McKinsey & Company, details why, by enhancing crisis-challenged financial models with machine-learning techniques such as neural networks, banks can emerge stronger from the present crisis.
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