Interesting Research Papers
A selection of papers that I found interesting and relevant to my research.
A Neural Network Approach to Understanding Implied Volatility Movements
This research explores how deep learning can be used to understand and predict implied volatility surface dynamics, combining traditional financial theory with modern machine learning approaches.
TradingAgents: Multi-Agent LLM Financial Trading Framework
An innovative framework that leverages Large Language Models in a multi-agent setting for financial trading, exploring the intersection of AI and market microstructure.
Volatility Smile Analysis Through the Heston Model
A comprehensive analysis of the Heston stochastic volatility model and its ability to capture the volatility smile phenomenon in options markets. The paper includes detailed mathematical derivations and empirical tests.
Machine Learning for Volatility Trading
An exploration of how machine learning techniques can be applied to volatility trading strategies, with a focus on predictive modeling and risk management.
A Rigorous Exploration of the BSM
A deep dive into the Black-Scholes-Merton model, examining its mathematical foundations, assumptions, and practical implications in modern markets. Includes detailed proofs and numerical examples.