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Pairs Trading Strategy in Economics: HAR-RV Model Adaptation for Market Neutral Approach

Explore how the HAR-RV model adaptation can enhance pairs trading by predicting betas between competitor equities in the same sector. This market-neutral strategy looks at correlations within daily price movements of liquid equities to identify convergence trades. By calculating standard deviations and applying mean reversion principles, the strategy aims to capitalize on relative price movements. The HAR-RV model uses realized betas over different time intervals to build conditional betas for pairs trading, helping to mitigate drift and improve profitability. The study focuses on Coca Cola (KO) and Pepsi (PEP) from 4/9/1997 to 4/14/2000, implementing the model in MATLAB to calculate expected relative returns and assess strategy effectiveness. Further research includes exploring significance levels, additional competitor pairs, different time intervals, and autocorrelation analysis to optimize strategy returns and profitability.

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Pairs Trading Strategy in Economics: HAR-RV Model Adaptation for Market Neutral Approach

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  1. Presentation 4 Kunal Jain April 7, 2010 Economics 201FS

  2. Pairs Trading • Market Neutral Strategy looking at correlations within day-to-day price movements of certain equities • Competitors in same sector • Liquid Equities • Used to Hedge sector- and market-risk. • Finds some sort of index or relative mean • Calculate Standard Deviations • Mean Reversion • When correlation breaks, one equity trades up, while other trades down: • Sell outperforming stock • Buy underperforming stock • Convergence Trade

  3. HAR-RV Model Adaptation

  4. HAR-RV Model Adaptation

  5. HAR-RV Model Adaptation • Adapt HAR-RV Model to calculate Realized Betas between two competitor equities within same sector to predict Betas: • t=1 corresponds to daily Beta, t=5 corresponds to weekly Beta, t=22 corresponds to monthly Beta. • This model uses betas realized over a 1-day, 5-day, and 1-month time interval to build the conditional betas. Βt+1 = β0 + αDβt + αWβt-5,t + αMβt-22,t + εt+1

  6. HAR-RV Model Adaptation • Intuition: Test whether the HAR adaptation, using daily, weekly, monthly Betas, can be implemented specifically in terms of Pairs Trading to predict Beta and take advantage of strategy. • Negates Drift associated with Pairs Trading • Unless the relative prices return closer to their historical levels, the pair trade will not be profitable • Take advantage of high-frequency data • Potential better ways of calculating Beta?

  7. HAR-RV Model Adaptation • Chose liquid competitor equities and Time Interval • Coca Cola (KO): 4/9/1997-4/14/2000 • Pepsi (PEP): 4/9/1997-4/14/2000 • Calculate HAR-Beta coefficients (D,W,M) • Implemented in MatLAB • Find conditional Beta using observed data and Beta-coefficients from model • Found using observed Betas (Alphas) • Calculate expected relative return based on conditional Beta • Calculate/Compare actual return to estimate differential

  8. HAR-B Model • Model implemented in MatLAB • Calculate HAR-Beta coefficients (D,W,M) • Implemented in MatLAB • 5-minute sampling • Conditional Beta obtained • Utilized Conditional Beta to calculate expected relative return • Calculated Differential: Observed minus Expected • Mean differential: 1.9869e-004 • Calculated Autocorrelations for Equities: • Expect Negative autocorrelation between differential of equities • Expect approximately Zero autocorrelation with log-returns of equity with itself • Mean Autocorrelation: -0.1120 • Autocorrelation with self (Pepsi): -0.0873

  9. HAR-B Model

  10. Further Research • Significance Levels • More Competitor Pairs • Different Time Intervals • Autocorrelation for all returns • Calculate Strategy Returns and Profitability

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