Conferences - Seminars
Machine Learning for Volatility Trading
By Artur SEPP (Julius Baer)
Many applications of quantitative trading and investing require the forecast of the future realized volatility as the fundamental input. While there are many models for volatility measurement and forecast, the key decision is how to select the best models with the highest predicative power for a given application. I apply the methods of supervised machine learning and learning to rank for the machine-based selection of volatility models. I demonstrate applications of this framework to volatility trading and risk management of option books.
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