Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-18T20:44:07.578Z Has data issue: false hasContentIssue false

SEQUENTIAL TESTING FOR THE STABILITY OF HIGH-FREQUENCY PORTFOLIO BETAS

Published online by Cambridge University Press:  28 November 2011

Abstract

Despite substantial criticism, variants of the capital asset pricing model (CAPM) remain to this day the primary statistical tools for portfolio managers to assess the performance of financial assets. In the CAPM, the risk of an asset is expressed through its correlation with the market, widely known as the beta. There is now a general consensus among economists that these portfolio betas are time-varying and that, consequently, any appropriate analysis has to take this variability into account. Recent advances in data acquisition and processing techniques have led to an increased research output concerning high-frequency models. Within this framework, we introduce here a modified functional CAPM and sequential monitoring procedures to test for the constancy of the portfolio betas. As our main results we derive the large-sample properties of these monitoring procedures. In a simulation study and an application to S&P 100 data we show that our method performs well in finite samples.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We thank Oliver Linton and three anonymous referees for their constructive comments. This research was partially supported by NSF grant DMS 0905400, grant GACR 201/09/J006, grant MSM 0021620839, Banque nationale de Belgique and Communauté française de Belgique—Actions de Recherche Concertées (2010–2015), DFG grant STE 306/22-1.

References

REFERENCES

Amsler, C.E. & Schmidt, P. (1985) A Monte Carlo investigation of the accuracy of multivariate CAPM tests. Journal of Financial Economics 14, 393–375.CrossRefGoogle Scholar
Andersen, T.G., Bollerslev, T., Diebold, F.X., & Wu, J. (2006) Realized beta: Persistence and predictability. In Fomby, T. & Terrell, D. (eds.), Advances in Econometrics: Econometric Analysis of Economic and Financial Time Series in Honor of R.F. Engle and C.W.J. Granger, vol. B, pp. 140. Elsevier.Google Scholar
Andrews, D.W.K. (1991) Heteroskedasticity and autocorrelation-consistent covariance matrix estimation. Econometrica 59, 817858.CrossRefGoogle Scholar
Andrews, D.W.K. & Monahan, J.C. (1992) An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica 60, 953966.10.2307/2951574CrossRefGoogle Scholar
Aue, A, Hörmann, S., Horváth, L., & Reimherr, M. (2009) Break detection in the covariance structure of multivariate time series models. Annals of Statistics 37, 40464087.CrossRefGoogle Scholar
Aue, A. & Horváth, L. (2004) Delay time in sequential detection of change. Statistics and Probability Letters 67, 221231.10.1016/j.spl.2004.01.002CrossRefGoogle Scholar
Aue, A., Horváth, L., Hušková, M., & Kokoszka, P. (2006a) Change-point monitoring in linear models. Econometrics Journal 9, 373403.10.1111/j.1368-423X.2006.00190.xCrossRefGoogle Scholar
Aue, A., Horváth, L., Kokoszka, P., & Steinebach, J. (2006b) Detecting changes in mean: Asymptotic normality of stopping times. Test 17, 515530.10.1007/s11749-006-0041-7CrossRefGoogle Scholar
Aue, A., Horváth, L., & Reimherr, M.L. (2009) Delay time of sequential procedures for multiple time series regression models. Journal of Econometrics 149, 174190.10.1016/j.jeconom.2008.12.018CrossRefGoogle Scholar
Balduzzi, P. & Robotti, C. (2008) Mimicking portfolios, economic risk premia and tests of multi-beta models. Journal of Business & Economic Statistics 26, 354368.CrossRefGoogle Scholar
Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., & Shephard, N. (2008) Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica 76, 14811536.Google Scholar
Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., & Shephard, N. (2011) Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Journal of Econometrics 162, 149169.10.1016/j.jeconom.2010.07.009CrossRefGoogle Scholar
Barndorff-Nielsen, O.E. & Shephard, N. (2004) Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics. Econometrica 72, 885925.10.1111/j.1468-0262.2004.00515.xCrossRefGoogle Scholar
Bauwens, L., Laurent, S., & Rombouts, J.V.K. (2006) Multivariate GARCH models: A survey. Journal of Applied Econometrics 21, 79109.10.1002/jae.842CrossRefGoogle Scholar
Berkes, I., Gombay, E., Horváth, L., & Kokoszka, P. (2004) Sequential change-point detection in GARCH(p, q) models. Econometric Theory 20, 11401167.CrossRefGoogle Scholar
Billingsley, P. (1968) Convergence of Probability Measures. Wiley.Google Scholar
Bollerslev, T. (1990) Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics 74, 498505.CrossRefGoogle Scholar
Bosq, D. (2000) Linear Processes in Function Spaces: Theory and Applications. Springer-Verlag.10.1007/978-1-4612-1154-9CrossRefGoogle Scholar
Černiková, A., Hušková, M., Prásková, Z., & Steinebach, J. (2011) Delay time in monitoring jump changes in linear models. Statistics, 25 p.; available online (doi 10.1080/02331888.2011.577895).Google Scholar
Chu, C.-S.J., Stinchcombe, M., & White, H. (1996) Monitoring structural change. Econometrica 64, 10451065.10.2307/2171955CrossRefGoogle Scholar
Engle, R.F. (2000) The econometrics of ultra-high-frequency data. Econometrica 68, 122.CrossRefGoogle Scholar
Engle, R.F. (2002) Dynamic conditional correlation—A simple class of multivariate GARCH models. Journal of Business & Economic Statistics 20, 339350.CrossRefGoogle Scholar
Engle, R.F. & Kroner, K.F. (1995) Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122150.10.1017/S0266466600009063CrossRefGoogle Scholar
Engle, R.F., Ng, V., & Rothschild, M. (1990) Asset pricing with a factor-ARCH structure: Empirical estimates for Treasury bills. Journal of Econometrics 45, 213237.CrossRefGoogle Scholar
Garcia, R. & Ghysels, E. (1998) Structural change and asset pricing in emerging markets. Journal of International Money and Finance 17, 455473.10.1016/S0261-5606(98)00010-2CrossRefGoogle Scholar
Ghysels, E. (1998) On stable factor structures in the pricing of risk: Do time-varying betas help or hurt? Journal of Finance 53, 549573.CrossRefGoogle Scholar
Goodhart, C.A.E. & O’Hara, M. (1997) High frequency data in financial markets: Issues and applications. Journal of Empirical Finance 4, 73114.10.1016/S0927-5398(97)00003-0CrossRefGoogle Scholar
Hafner, C.M. & Preminger, A. (2009a) Asymptotic theory for a factor GARCH model. Econometric Theory 25, 336363.10.1017/S0266466608090117CrossRefGoogle Scholar
Hafner, C.M. & Preminger, A. (2009b) On asymptotic theory for multivariate GARCH models. Journal of Multivariate Analysis 100, 20442054.10.1016/j.jmva.2009.03.011CrossRefGoogle Scholar
Harrison, J.M., Pitbladdo, R., & Schaefer, S.M. (1984) Continuous prices in frictionless markets have infinite variation. Journal of Business 57, 353365.10.1086/296268CrossRefGoogle Scholar
Harvey, C.R. (1991) The world price of covariance risk. Journal of Finance 46, 111157.10.1111/j.1540-6261.1991.tb03747.xCrossRefGoogle Scholar
Hörmann, S., Horváth, L., & Reeder, R. (2010) Functional Volatility Sequences. Preprint, University of Utah.Google Scholar
Huang, H.C. & Cheng, W.H. (2005) Tests of the CAPM under structural changes. International Economic Journal 19, 523541.10.1080/10168730500381990CrossRefGoogle Scholar
Hušková, M., Prašková, Z., & Steinebach, J. (2007) On the detection of changes in autoregressive time series, I. Asymptotics. Journal of Statistical Planning and Inference 137, 12431259.10.1016/j.jspi.2006.02.010CrossRefGoogle Scholar
Ibragimov, I.A. (1962) Some limit theorems for stationary processes. Theory of Probability and Its Applications 7, 349382.CrossRefGoogle Scholar
Jacod, J., Li, Y., Mykland, P.A., Podolskij, M., & Vetter, M. (2009) Microstructure noise in the continuous case: The pre-averaging approach. Stochastic Processes and Their Applications 119, 22492276.CrossRefGoogle Scholar
Jansson, M. (2002) Consistent covariance matrix estimation for linear processes. Econometric Theory 18, 14491459.CrossRefGoogle Scholar
Jeantheau, T. (1998) Strong consistency of estimators for multivariate ARCH models. Econometric Theory 14, 7086.10.1017/S0266466698141038CrossRefGoogle Scholar
Lintner, J. (1965) The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics 47, 1337.CrossRefGoogle Scholar
Liu, W. & Wu, W.B. (2010) Asymptotics of spectral density estimates. Econometric Theory 26, 12181245.10.1017/S026646660999051XCrossRefGoogle Scholar
MacKinley, A.C. (1987) On multivariate tests of the CAPM. Journal of Financial Economics 18, 341371.CrossRefGoogle Scholar
Markowitz, H.M. (1999) The early history of portfolio theory: 1600–1960. Financial Analysts Journal 55, 516.CrossRefGoogle Scholar
Merton, R. (1973) An intertemporal asset pricing model. Econometrica 41, 867880.CrossRefGoogle Scholar
Móricz, F. (1977) Moment inequalities for the maximum of partial sums of random fields. Acta Scientiarum Mathematicarum (Szeged) 39, 353366.Google Scholar
Newey, W.K. & West, K.D. (1987) A simple, positive-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703708.CrossRefGoogle Scholar
Phillips, P.C.B. (2005) HAC estimation by automated regression. Econometric Theory 21, 116142.CrossRefGoogle Scholar
Santos, A.O. (1998) Capital Asset Pricing Model and Changes in Volatility. Research paper 4, International Center for Financial Asset Management and Engineering.Google Scholar
Shao, X. & Wu, W.B. (2004) Limit theorems for iterated random functions. Journal of Applied Probability 41, 425436.Google Scholar
Sharpe, W.F. (1964) Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19, 424442.Google Scholar
Smith, R.J. (2005) Automatic positive semidefinite HAC covariance matrix and GMM estimation. Econometric Theory 21, 158170.CrossRefGoogle Scholar
Tsay, R.S. (2002) Analysis of Financial Time Series. Wiley.CrossRefGoogle Scholar
Wang, J., Meric, G., Liu, Z., & Meric, I. (2009) Stock market crashes, firm characteristics, and stock returns. Journal of Banking & Finance 33, 15631574.CrossRefGoogle Scholar
Wu, W.B. (2005) Nonlinear systems theory: Another look at dependence. Proceedings of the National Academy of Sciences U.S.A. 102, 1415014154.10.1073/pnas.0506715102CrossRefGoogle Scholar
Wu, W.B. (2007) Strong invariance principles for dependent random variables. Annals of Probability 35, 22942320.CrossRefGoogle Scholar
Zeileis, A. (2004) Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software 11, 117.10.18637/jss.v011.i10CrossRefGoogle Scholar
Zhang, L., Mykland, P.A., & Aït-Sahalia, Y. (2005) A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association 100, 13941411.10.1198/016214505000000169CrossRefGoogle Scholar