Download
1 / 10
100 likes | 128 Views
This presentation delves into the application of a Pairs Trading strategy using the HAR-RV Model adaptation to calculate realized betas between competitive equities in the same sector. By leveraging daily, weekly, and monthly betas, the strategy aims to predict beta movements and capitalize on convergence trades. The analysis focuses on Coca Cola (KO) and Pepsi (PEP) over a specific time interval, revealing insights on expected relative returns based on conditional beta calculations. The study further investigates autocorrelations, hinting at profitable trading opportunities through mean differentials and autocorrelation patterns. Future research areas include exploring additional competitor pairs, varied time intervals, assessing strategy profitability, and delving into autocorrelations for all returns.
E N D
Presentation 4 Kunal Jain April 7, 2010 Economics 201FS
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
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
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?
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
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
Further Research • Significance Levels • More Competitor Pairs • Different Time Intervals • Autocorrelation for all returns • Calculate Strategy Returns and Profitability
