A Hidden Markov Model for Statistical Arbitrage in Crude Oil Futures Markets

Hi! Here's Iván with this week's exciting newsletter, brimming with insights and discoveries on building robust investment strategies and risk models using Machine Learning.

In this edition, I am presenting the following sections:

• 🕹️ AI-Finance Insights: I summarize two must-read academic papers that mix cutting-edge ML/DL with Asset Pricing & Quant Finance:

  • A Hidden Markov Model for Statistical Arbitrage in Crude Oil Futures Markets

  • Squeezing Financial Noise: A Novel Approach to Covariance Matrix Estimation

• 💊 AI Essentials: The section on top AI & Quant Finance learning resources: Today, I’m sharing an insightful video titled "An Interview with James Simons: The Mathematician Who Revolutionized Quant Finance." In this interview, Simons reflects on how he leveraged advanced mathematics and computing to build his fortune and shape modern quantitative trading. If you're interested in the intersection of math, finance, and groundbreaking innovations, this is a must-watch!

• 🥐 Asset Pricing Insights: In this edition, I introduce a paper that presents a cutting-edge, model-free statistical arbitrage framework powered by reinforcement learning (RL). Unlike traditional strategies based on preset mean-reversion models, this RL-driven approach dynamically adapts trading decisions in real time, optimizing profit in complex market conditions.

“A Hidden Markov Model for Statistical Arbitrage in Crude Oil Futures Markets”

👉 This paper presents an innovative approach to statistical arbitrage in international crude oil futures markets, leveraging a Hidden Markov Model (HMM) to enhance the classic pairs trading strategy. The authors introduce a regime-switching model for the cointegration spread between Brent, WTI, and Shanghai crude oil futures, offering new profit opportunities in this volatile market.

👉 By using online filtering techniques, the paper demonstrates how dynamically estimated parameters improve the strategy’s responsiveness to changing market conditions. Testing across real data, including the recently introduced Shanghai crude oil futures, shows that strategies involving all three contracts outperform traditional two-futures approaches.

👉 Performance figures: The HMM-based strategy delivered an annualized return of up to 72.77% and an annualized Sharpe ratio of 1.01. In comparison, the probability interval strategy returned 56.37% with a Sharpe ratio of 1.08, significantly outperforming simpler strategies like plain vanilla pairs trading. Even with high transaction costs, the HMM-based strategies outperformed standard benchmarks.

👉 The strategy yields significant profitability, especially when incorporating Shanghai futures, which exhibits a faster mean reversion, making it easier to exploit temporary mispricings. Even after accounting for transaction costs, the HMM-based strategies deliver superior performance compared to standard statistical arbitrage methods

“Squeezing Financial Noise: A Novel Approach to Covariance Matrix Estimation”

👉 This paper introduces a cutting-edge technique for managing noise in financial data, termed "squeezing," aimed at improving covariance matrix estimation. This approach leverages statistical alignment to better account for noise and applies it to enhance portfolio optimization strategies. The method is model-free, making it adaptable to various portfolio sizes and configurations.

👉 The authors propose a novel strategy to estimate covariance matrices that differ from traditional approaches like linear and non-linear shrinkage. By exploring the eigenspace of the covariance matrix, the squeezing technique optimizes both eigenvalues and eigenvectors to reduce noise and enhance portfolio performance.

👉 The method outperforms existing covariance estimation techniques, offering a flexible and objective-driven optimization approach. The squeezing method effectively filters out noise at the source rather than treating it after the fact, allowing for more accurate predictions and better-conditioned matrices.

👉 The squeezing approach shows particular strength in higher-dimensional portfolios, where traditional covariance estimation techniques struggle. The authors highlight the potential for this method to be applied in various financial modeling processes, providing superior performance even in noisy market environments.

AI-Essentials

In this edition, we're featuring an exclusive interview with James Harris Simons, often described as "the world's smartest billionaire." Simons amassed his fortune through the brilliant use of mathematics and computers in the financial markets, revolutionizing quantitative trading. Now a renowned philanthropist, Simons shares insights into his journey, the role of mathematics in his success, and his vision for the future.

Asset Pricing Insights

“Advanced Statistical Arbitrage with Reinforcement Learning”

👉 This paper introduces a cutting-edge, model-free statistical arbitrage framework powered by reinforcement learning (RL). Unlike traditional strategies relying on mean-reversion models, this RL-based approach dynamically adapts trading decisions, optimizing profit in real time.

👉 The authors developed a novel empirical mean reversion time metric to construct optimal portfolios. By minimizing this reversion time, they enhance the strategy's ability to identify profitable trades in mean-reverting securities without relying on preset parameters.

👉 Tested on simulated and real market data (S&P 500), the RL model outperformed classical methods such as the distance method and Ornstein-Uhlenbeck model, showcasing significant gains with minimal risk.

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