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Time-Varying Beta: Heterogeneous Autoregressive Beta Model

Time-Varying Beta: Heterogeneous Autoregressive Beta Model. Kunal Jain Spring 2010 Economics 201FS May 5, 2010. Background.

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Time-Varying Beta: Heterogeneous Autoregressive Beta Model

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  1. Time-Varying Beta:Heterogeneous Autoregressive Beta Model Kunal Jain Spring 2010 Economics 201FS May 5, 2010

  2. Background • Beta (β) is a commonly defined measure of market risk, which essentially measures the volatility of returns on assets or securities and co-movements of the market portfolio. • Demonstrated Through: Security Characteristic Line (SCL) • Markets are efficient (α = 0) • Effective Risk Free Rate = 0 • Ri,t = β*Rm,t + εi,t and • Capital Asset Pricing Model (CAPM)- conventionally estimate betas through monthly returns over a 5-year time horizon (Banz, 1981). • Static Beta unable to satisfactorily explain cross-section of average returns on stock. (Harvey (1989), Ferson and Harvey (1993), Jagannathan and Wang (1994)). • Recent literature suggests that a static-beta model may produce incoherent results.

  3. Data • SPY • January 2, 2001 – January 3, 2009 • KO, PEP, MSFT, JPM, BAC, JNJ, WMT, XOM • January 2, 2001 – January 3, 2009 • In Sample Time Interval • January 2, 2001 – January 2, 2006 • Out of Sample Time Interval (A) • January 3, 2006-January 2, 2008 • Out of Sample Time Interval (B) • January 3, 2006-January 2, 2009

  4. Optimal Sampling Frequency • Hansen and Lunde (2006) define the market microstructure noise: u(t) = p(t) – p*(t) • p(t) is the observable log price in the market at time t • p*(t) is the latent real log price at time t. • p(t + θ) – p(t) = [p*(t + θ) - p*(t)] + [u(t + θ) – u(t)] • θ is a real number increment • [p(t + θ) – p(t)]represents the change in price over a time interval • u(t) is i.i.d. and represents the microstructure noise applicable to the price change over the specified time interval. • Volatility Signature Plot [Andersen, Bollerslev, Diebold and Labys (2000)] • This graphical approach displays how average realized variance corresponds to sampling frequency.

  5. Volatility Signature Plots 10 minutes chosen as optimal sampling frequency for subsequent analysis.

  6. Motivation Given that beta was in fact constant over time, one would expect the standard deviation to be relatively zero omitting market microstructure noise.

  7. Motivation- Cont.

  8. AR(1)- F.O. Autocorrelations • β*(t)= c + θ β(t-1) + εt • εt is a “white noise” term, and represents a deviation from the fundamental beta β(t). • Positive First Order Autocorrelations

  9. HAR-Beta Model • Realized Beta = Realized betas are realizations of the underlying ratio between the integrated stock and market return covariance and the integrated market variance • Integrated Variance = • Integrated Covariance = • Underlying spot volatility is impossible to observe • Discrete measures of variation can be used to numerically approximate integrated variance and integrated covariance. • Realized Beta = • Cov(ri,t, rm,t) is the covariance of realized returns on an asset i and returns on the market on sampling interval t • Var(rm,t) is the variance of realized returns on asset i on the sampling interval t • Ntis the number of units into which the sampling interval is partitioned into.

  10. HAR-Beta Model • Rβt = • t represent time • n represents the number of units • Predict Daily Returns using: • where the dependent variables correspond to lagged daily (t =1), weekly (t = 5) and monthly (t = 22) regressors. Rβt+1,t = β0 + αD**Rβt-1,t + αW*Rβt-5,t + αM *Rβt-22,t + εt+1

  11. HAR-Beta Regression Coefficients The significance levels of the coefficients are denotes by the asterisk: * → p < 0.05, ** → p < 0.01

  12. Benchmark Comparison (1) • Constant Mean Return: • logarithmic returns at time t, R(t), are observed as a sum of all latent logarithmic returns leading up to time t-1, R(t - 1), divided by the number of observations n.

  13. Benchmark Comparison (2) • The usage of a beta computed from monthly returns over a 5-year time period has been noted by numerous studies including Banz (1981).

  14. Root Mean Squared Error (RMSE) • Mean square forecast error (RMSE) is defined as: • MSE = • n is the number of predictions contained • Ra,tis out of sample realized logarithmic return on Asset a at time t • Řa,t is the predicted return on Asset a at the corresponding time t. • RMSE = • The result is the root mean squared error (RMSE) which can be interpreted in annualized standard deviation units.

  15. Results- In Sample • January 2, 2001 – January 2, 2006 • Average SD of beta at 10 minute level is 0.3737 (statistically significant) • Positive Autocorrelations • Average First Order Autocorrelation is 0.3484 • Notable that MSFT and XOM display low F.O. autocorrelation (0.0866, 0.1521) • Less predictable • Beta Coefficients = sum equal to one – Persistency • Notable exception is MSFT, sum = 0.2387 • XOM sum = 0.7623 • Congruency

  16. Results – Out of Sample (A) All units are expressed in Annualized Standard Deviation Units.

  17. Results – Out of Sample (A) • January 3, 2006 – January 3, 2008 • Visual comparison between RMSE displays significant reductions • Average 21.94% reduction of HAR-Beta RMSE when compared to constant returns. • Average 6.62% reduction of HAR-Beta RMSE when compared to constant beta. • OLS, non-robust regression • WMT has a 35.56% increase in RMSE compared to constant returns • Surprising • SD of Beta (0.4783) – highest • F.O. Autocorrelation (0.5701) – highest • R2 (0.5391) – highest • Poon and Granger (2003) issues with sample outliers and volatility estimation. • MSFT and XOM less predictable? • Constant returns, low reduction 0.07% and 5.98% respectively • BUT constant beta, RMSE reduction 13.45% and 7.96% respectively • R2 values of WMT and XOM lowest, 0.0113 and 0.1106. • Salient point, given low predictability, the RMSE of the HAR-Beta was still reduced. • When MSFT and XOM constant beta compared to constant return, there is a significant increase in RMSE.

  18. Results- Out of Sample (B) • January 3, 2006 – January 2, 2009 All units are expressed in Annualized Standard Deviation Units.

  19. Results- Out of Sample (B) • RMSE reduced by average 19.67% when compared to constant beta • RMSE reduced by average of 39.28% when compared to constant return (including MSFT)

  20. Final Conclusions • Results in line with recent literature • Alternative to constant beta used in CAPM • Average SD over whole sample was .3663 when using optimal sampling frequency • Positive Autocorrelations – suggesting predictability of betas • HAR-Beta showed overall reduction of RMSE across equities in different industry sectors. • Weakness- OLS • Despite limitation, usage of logarithmic returns coupled with optimal sampling frequency provides relative sanity check • Importance of conventional change from the constant beta to a time-varying beta.

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