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, …

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 …

Backtesting is an essential part of quantitative strategy development, and naturally, strategies are often selected based on strong backtest performance. However, an important question when evaluating backtested strategies is how much of the results reflects skill versus luck. Reference examines this issue by analyzing 1,726 commercially marketed strategies from …

A significant amount of research has been conducted on the volatility risk premium (VRP). Reference contributes to this literature by linking the VRP to the parameters of the Heston model. The Heston model is a widely used stochastic volatility model that captures time-varying volatility and mean-reverting dynamics. Unlike previous …

Gold and silver have been strong recently, with upward trends and increased volatility. Reference studies the price dynamics of leading precious metals, gold, silver, platinum, and palladium, over the period from December 9, 2019, to March 1, 2026, dividing the sample into pre- and post-COVID subperiods. The study employs …

There is a noteworthy line of research that decomposes asset or strategy returns into daytime and overnight components. This type of decomposition has been discussed previously in the context of the volatility risk premium. Reference follows a similar approach, examining SPY and nine sector ETFs over the period 1999 …

Regime classification is important in asset and risk management. Traditional approaches classify regimes based on direction, bullish or bearish, and volatility, high or low. Reference departs from this framework and instead classifies markets as mean-reverting or trending. Specifically, it uses return thresholds of 0.5%, 0.75%, and 1% to define …

The Black–Scholes–Merton model is a groundbreaking and foundational framework in option pricing; however, it has well-known limitations. Several extensions have been developed to address these issues, including stochastic volatility and Lévy process-based models, which are largely parametric. Reference proposes a semi-parametric approach to overcome these limitations. Specifically, the model …

Retail options trading is rising rapidly, driven by factors such as the growth of retail brokers, the popularity of social media, and more flexible working hours. Alongside this trend, there has been an increased interest in research on retail options trading behavior. We recently discussed retail options trading and its …

Machine learning (ML) is increasingly applied in finance. In trading in particular, researchers aim to determine whether ML can generate consistent alpha. Reference revisits this question. The authors utilize a long sample dataset from 1970 to 2024 and evaluate a wide range of ML models, including linear, nonlinear, and …