The Information Content of the Spot VIX Term Structure

Considerable research has been devoted to the study of the VIX futures term structure, showing that the shape of the curve contains valuable information about the future direction of volatility. Trading strategies have been developed based on the informational content of the VIX futures curve. However, much less attention has …

How Effective Are LLM Trading Agents?

AI is evolving rapidly across many areas of life, and trading is no exception. Large Language Models (LLMs) have increasingly been used as tools to assist financial analysts with research, data synthesis, and decision-making. More recently, researchers and practitioners have moved beyond analyst assistance and begun exploring LLMs as autonomous …

Decomposing the Variance Risk Premium, Part 2

The volatility risk premium (VRP) is the difference between implied volatility and subsequently realized volatility, and is one of the most extensively studied phenomena in options markets. We previously discussed Reference , which decomposes the VRP into upside and downside components and studies their dynamics separately. Reference applies a …

Volatility Measures for Regime Classification

Regime detection and classification are important in portfolio management and asset allocation. One of the key inputs into regime detection models is volatility. Reference examines which volatility measure is most effective for regime classification. The authors study three volatility measures, Implied volatility (VIX/VIXBR), GARCH conditional volatility, Historical volatility. They …

Network Effects in Social Media Sentiment

Social media sentiment has become increasingly important in modern portfolio and risk management. Most studies on social media rely on aggregate sentiment measures, such as average bullishness scores or overall positive-versus-negative comment ratios. Reference introduces an innovative approach to analyzing social media sentiment by investigating network effects, specifically how …

VIX Forecasting Using Crypto Overnight Returns

Prediction is central in finance. A growing line of research uses cross-asset signals to forecast market movements. A recent example showed that Bitcoin can serve as a strong leading indicator in a machine learning-based trading system. Along similar lines, Reference examines whether cryptocurrency overnight returns, defined as price changes …

Regime-Aware Trading Strategies with Machine Learning

Regime detection is important in portfolio management and remains an active area of research, particularly in the age of machine learning and AI. Reference proposes a trading strategy based on machine learning, combined with regime detection using a Hidden Markov Model. Specifically, the machine learning technique used is LightGBM, …

Gamma Exposure and S&P500 Return Predictability

Options trading volume has been increasing rapidly, potentially altering market dynamics. Reference examines whether aggregate gamma exposure (GEX) in the S&P500 index options market contains predictive information about future equity returns and whether it can enhance short-term forecasting models. To do so, the authors construct an Autoregressive Distributed Lag …