Financial econometrics is the science of building financial models. These models are developed for optimal trading strategies, risk management, portfolio optimization, market making, and asset pricing, among other tasks. Several decades of research from disciplines such as statistics, economics, operations research, and game theory have been applied to get to where we are today. However, today’s models make many assumptions to allow appropriate mathematics to be applied to derive realistic models.
Recent developments in AI have a huge potential to transform financial model building by automatically creating the models from data. Ambika Sukla covers the challenges in financial data and demonstrates how to apply deep generative models, reinforcement learning, and gradient descent optimization on problems such as time series analysis, volatility models, and high-frequency limit order book analysis.
Ambika Sukla heads Morgan Stanley’s AI and ML Center of Excellence, where he works on applying machine learning techniques to algorithmic trading, risk management, operations and compliance, and wealth and investment management and helps set the firm’s AI strategy. He has extensive experience in machine learning, including recommendation systems, classification and regression, clustering, anomaly detection, and optimal control. Ambika is a big proponent of unsupervised and semisupervised learning methods. His background is in signal processing and information theory. He holds a master’s degree in telecommunication engineering from NJIT.
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