Last Updated on 10 February, 2024 by Rejaul Karim
Venturing into the realm of quantitative strategies, Fernando B. Sabino da Silva, Flávio Ziegelman, and João Caldeira unveil a pioneering “Pairs Trading Strategy Based on Mixed Copulas” in their 29-page intellectual odyssey.
Crafted with precision and presented in November 2017, this strategy recalibrates the conventional pairs trading narrative. The authors ingeniously leverage mixed copulas, orchestrating an optimal blend that forms a mispricing index. Akin to maestros, they conduct a symphony of analysis on S&P 500 daily stock returns spanning from 1990 to 2015.
What distinguishes their approach is the dual lens of fixed time length and market state dependency, offering nuanced insights into dynamic changes and distinct strategies tailored to bull and bear market conditions. The results echo the strategy’s prowess, particularly shining in the intricate dance of bear market mean returns.
Abstract Of Paper
We propose an alternative pairs trading strategy based on computing a mispricing index in a novel way via a mixed copula model, or more specifically via an optimal linear combination of copulas. We evaluate the statistical and economic performances of our proposed approach by analyzing S&P 500 daily stock returns between 1990 and 2015. Empirical results are obtained not only from the full sample analysis but also from subperiods analyses. These subperiods are chosen in two different ways: i) fixed time length; and ii) bull/bear market dependent. The fixed time length analysis is able to capture possible dynamics changes over time whereas the bull/bear analysis makes it possible for one to have distinct pairs trading strategies depending on the market state. Our results suggest that two-component mixed copulas perform very well, especially during bear market times when it regards to mean returns.
Original paper – Download PDF
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Fernando B. Sabino da Silva
Federal University of Rio Grande do Sul (UFRGS) – Economics Department
Universidade Federal de Santa Catarina & CNPq
Concluding this study, the pairs trading strategy built on mixed copulas emerges as a novel and effective approach in navigating the intricate realm of financial markets. By leveraging optimal linear combinations of copulas, we introduce a distinctive mispricing index computation.
The empirical analysis, spanning S&P 500 daily stock returns from 1990 to 2015, showcases the strategy’s robustness, with a particular highlight on its performance during bear market conditions. This research presents a valuable addition to the quantitative strategist’s toolkit, offering adaptability and efficacy in pairs trading strategies across diverse market scenarios.
Q1: What makes the pairs trading strategy based on mixed copulas unique?
A1: The strategy stands out for leveraging a novel approach to pairs trading by using mixed copulas. The authors craft an optimal blend through an optimal linear combination of copulas, forming a distinctive mispricing index. This approach introduces a unique perspective to the conventional pairs trading narrative.
Q2: How does the study incorporate both fixed time length and market state dependency in its analysis?
A2: The study employs a dual lens approach by considering fixed time length and market state dependency. The fixed time length analysis captures potential dynamics changes over time, while the bull/bear market analysis enables the formulation of distinct pairs trading strategies tailored to different market states. This nuanced approach provides insights into dynamic changes and market-specific strategies.
Q3: What are the key findings of the empirical analysis on S&P 500 daily stock returns from 1990 to 2015?
A3: The empirical results suggest that the pairs trading strategy based on mixed copulas, particularly using two-component mixed copulas, performs very well. The strategy’s robustness is highlighted, especially during bear market times, showcasing its effectiveness in achieving mean returns. This research offers a valuable addition to the toolkit of quantitative strategists, emphasizing adaptability and efficacy across diverse market scenarios.