Statistical Arbitrage & Pairs Trading

Statistical arbitrage covers strategies that trade temporary mispricings between related instruments: classic pairs trading, cointegration-based baskets, and mean-reversion signals across equities, futures, and crypto. It is one of the few strategy families where academic papers regularly include real backtests, which makes the rigor score especially useful here.

When reading this literature, three questions separate implementable work from curve-fitting: Does the paper account for transaction costs and shorting constraints? Is the pair-selection rule defined before the backtest window? And does performance survive the post-2010 period, when the classic distance method famously decayed? Papers ranked highly below tend to answer at least two of these.

Explainers from the corpus: ARDL models: appropriate use vs p-hacking →

Weak error approximation for rough and Gaussian mean-reverting stochastic volatility models

For a class of stochastic models with Gaussian and rough mean-reverting volatility that embeds the genuine rough Stein-Stein model, we study the weak approximation rate when using a Euler type scheme with integrated kernels. Our first result is a weak convergence rate for the discretised rough Ornst

Order routing and market quality: Who benefits from internalisation?

We analyse two models of liquidity provision to determine the retail traders’ preference for marketable order routing. Order internalization is captured by a model of market makers competing for the retail order flow in a Bertrand fashion. On the other hand, the price-taking competitive liquidity pr

Generating Alpha: A Hybrid AI-Driven Trading System Integrating Technical Analysis, Machine Learning and Financial Sentiment for Regime-Adaptive Equity Strategies

The intricate behavior patterns of financial markets are influenced by fundamental, technical, and psychological factors. During times of high volatility and regime shifts causes many traditional strategies like trend-following or mean-reversion to fail. This paper proposes a hybrid AI-based trading

Small Volatility Approximation and Multi-Factor HJM Models

Here we demonstrate how we can use Small Volatility Approximation in calibration of Multi-Factor HJM model with deterministic correlations, factor volatilities and mean reversals. It is noticed that quality of this calibration is very good and it does not depend on number of factors.

Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting

In this paper, we explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure, using relatively unexplored functional and augmented data structures. While many conventional forecasting methods concentrate on the short-term dynamics of ti

Dynamic Factor Models with Forward-Looking Views

Prediction models calibrated using historical data may forecast poorly if the dynamics of the present and future differ from observations in the past. For this reason, predictions can be improved if information like forward looking views about the state of the system are used to refine the forecast.

Using Internal Bar Strength as a Key Indicator for Trading Country ETFs

This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea

Gatheral double stochastic volatility model with Skorokhod reflection

We investigate the Gatheral model of double mean-reverting stochastic volatility, in which the drift term itself follows a mean-reverting process, and the overall model exhibits mean-reverting behavior. We demonstrate that such processes can attain values arbitrarily close to zero and remain near ze

Symmetry classification and invariant solutions of the classical geometric mean reversion process

Based on the Lie symmetry method, we investigate a Feynman-Kac formula for the classical geometric mean reversion process, which effectively describing the dynamics of short-term interest rates. The Lie algebra of infinitesimal symmetries and the corresponding one-parameter symmetry groups of the eq

An optimization dichotomy for capital injections and absolutely continuous dividend strategies

We consider an optimal stochastic control problem in which a firm’s cash/surplus process is controlled by dividend payments and capital injections. Stockholders aim to maximize their dividend stream minus the cost of injecting capital, if needed. We consider absolutely continuous dividend policies s

A Comparison between Financial and Gambling Markets

Financial and gambling markets are ostensibly similar and hence strategies from one could potentially be applied to the other. Financial markets have been extensively studied, resulting in numerous theorems and models, while gambling markets have received comparatively less attention and remain rela

Consumption adjustment with mean-reverting risk premia: A singular stochastic control approach

We study a two-sided singular stochastic control problem in which changes in consumption carry utility costs and the risk premium follows the mean-reverting Kim-Omberg model. Optimal consumption is reflected between two moving barriers given by thresholds that depend on the current risk premium. The

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