SMLF24 — Stochastics and Machine Learning in Finance, Econometric Risk Modeling, and Other Sciences

Stochastic calculus finds so extensive applications and enables us to analyze various phenomena affected by random factors, such as asset price movements, option pricing, risk assessment, and geopolitical or climate risks. Moreover, stochastic calculus's effectiveness extends beyond finance, with its use in physics, biology, engineering, and economics, where it helps describe and analyze systems influenced by random events. Its ability to handle stochastic processes makes it an invaluable resource for comprehending and navigating uncertain environments and investigating the panel of risks that affect and impact financial markets or supply chains in commodity one. Another powerful tool for modeling risks is machine learning. Machine learning has emerged as a powerful tool for modeling complex (financial and or economic) systems and making data-driven decisions. By exploiting advanced algorithms, machine learning enables the analysis of vast datasets to identify patterns, forecast market trends, and manage risks. The capabilities of these models range from forecasting stock prices and optimizing investment portfolios to detecting fraudulent activities and assessing credit risk. Machine learning's ability to adapt to changing market conditions and handle non-linear relationships makes it indispensable in the ever-evolving world of finance but also all possible relationships and contagion on commodity markets, offering the potential to enhance decision-making, reduce errors, and unlock valuable insights from data.

Topics: Financial mathematics, stochastic models, pricing financial derivatives, commodity markets, energy markets, global energy markets, geopolitical risk, climate change, natural resources, energy transition, volatility modeling, high-frequency data analysis, machine learning prediction models, asymmetric structures, software development with applications, complex networks, big data analytics, nonlinear time series analysis, recurrent neural networks, Stochastic calculus and machine Learning with applications to other sciences.