151
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Flexible weighted dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution

, &
Pages 989-1014 | Received 26 Mar 2020, Accepted 05 Oct 2020, Published online: 23 Oct 2020

References

  • Anderson, R.M., Eom, K.S., Hahn, S.B., and Park, J.H. (2012), ‘Sources of Stock Return Autocorrelation’. Working Paper.
  • Ardia, D., and Hoogerheide, L.F. (2010), ‘Bayesian Estimation of the GARCH(1, 1) Model with Student-t Innovations’, The R Journal, 2, 41–47. doi: 10.32614/RJ-2010-014
  • Blackwell, D., and MacQueen, J.B. (1973), ‘Ferguson Distributions Via Pólya Urn Schemes’, The Annals of Statistics, 1, 353–355. doi: 10.1214/aos/1176342372
  • Bollerslev, T. (1986), ‘Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 31, 307–327. doi: 10.1016/0304-4076(86)90063-1
  • Chib, S., and Greenberg, E. (2010), ‘Additive Cubic Spline Regression with Dirichlet Process Mixture Errors’, Journal of Econometrics, 156, 322–336. doi: 10.1016/j.jeconom.2009.11.002
  • Chib, S., and Jeliazkov, I. (2001), ‘Marginal Likelihood From the Metropolis–Hastings Output’, Journal of the American Statistical Association, 96, 270–281. doi: 10.1198/016214501750332848
  • Cowles, A., and Jones, H.E. (1937), ‘Some a Posteriori Probabilities in Stock Market Action’, Econometrica, 5, 280–294. doi: 10.2307/1905515
  • Dunson, D.B., Pillai, N., and Park, J. (2007), ‘Bayesian Density Regression’, Journal of Royal Statistical Society Series B, 69, 163–183. doi: 10.1111/j.1467-9868.2007.00582.x
  • Fama, E.F. (1970), ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, The Journal of Finance, 25, 383–417. doi: 10.2307/2325486
  • Franses, P.H., and van Dijk, D. (2000), Non-Linear Time Series Models in Empirical Finance, New York: Cambridge University Press, pp. 30–31.
  • Geweke, J.F. (1993), ‘Bayesian Treatment of the Independent Student-t Linear Model’, Journal of Applied Econometrics, 8, 19–40. doi: 10.1002/jae.3950080504
  • Griffin, J.E., and Steel, M.F.J. (2006), ‘Order-based Dependent Dirichlet Processes’, Journal of the American Statistical Association, 101, 179–194. doi: 10.1198/016214505000000727
  • Hossain, A., Zaman, F., and Islam, M. (2009), ‘ Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction’, International Conference on Pattern Recognition and Machine Intelligence, pp. 597–602.
  • Jacquier, E., Polson, N.G., and Rossi, P.E. (2004), ‘Bayesian Analysis of Stochastic Volatility Models with Fat-tails and Correlated Errors’, Journal of Econometrics, 122, 185–212. doi: 10.1016/j.jeconom.2003.09.001
  • Jegadeesh, N., and Titman, S. (1995), ‘Short-Horizon Return Reversals and the Bid-Ask Spread’, Journal of Financial Intermediation, 4, 116–132. doi: 10.1006/jfin.1995.1006
  • Jensen, M.J., and Maheu, J.M. (2013), ‘Bayesian Semiparametric Multivariate GARCH Modeling’, Journal of Econometrics, 176, 3–17. doi: 10.1016/j.jeconom.2013.03.009
  • Kalli, M., Walker, S., and Damien, P. (2013), ‘Modeling The Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model’, Journal of Business & Economic Statistics, 31, 371–383. doi: 10.1080/07350015.2013.794142
  • Korsgaard, I., Madsenm, P., and Jensen, J. (1998), ‘Bayesian Inference in the Semiparametric Log Normal Frailty Model Using Gibbs Sampling’, Genetics Selection Evolution, 30, 241–256. doi: 10.1186/1297-9686-30-3-241
  • Kim, J., Shephard, N., and Chib, S. (1998), ‘Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models’, The Review of Economic Studies, 5, 361–393. doi: 10.1111/1467-937X.00050
  • Llorente, G., Michaely, R., Saar, G., and Wang, J. (2002), ‘Dynamic Volume-Return Relation of Individual Stocks’, Review of Financial Studies, 15, 1005–1047. doi: 10.1093/rfs/15.4.1005
  • MacEachern, S.N. (2000), ‘Dependent Dirichlet Processes’, Technical Report, Ohio State University, Department of Statistics.
  • MacEachern, S.N., and Müller, P. (1998), ‘Estimating Mixtures of Dirichlet Process Models’, Journal of Computational & Graphical Statistics, 7, 223–238.
  • Modarres, R., and Ouarda, T.B.M.J. (2013), ‘Modeling the Relationship Between Climate Oscillations and Drought by a Multivariate GARCH Mode’, Water Resources Research, 50, 601–618. doi: 10.1002/2013WR013810
  • Platanios, E.A., and Chatzis, S.P. (2014), ‘Gaussian Process-Mixture Conditional Heteroscedasticity’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 888–900. doi: 10.1109/TPAMI.2013.183
  • Sethuraman, J. (1994), ‘A Constructive Definition of Dirichlet Priors’, Statistica Sinica, 4, 639–650.
  • Sun, P., Kim, I., and Lee, K. (2018), ‘Dual-Semiparametric Regression Using Weighted Dirichlet Process Mixture’, Computational Statistics and Data Analysis, 117, 162–181. doi: 10.1016/j.csda.2017.08.005
  • Tol, R. (1996), ‘Autoregressive Conditional Heteroscedasticity in Daily Temperature Measurements’, Environmetrics, 7, 67–75. doi: 10.1002/(SICI)1099-095X(199601)7:1<67::AID-ENV164>3.0.CO;2-D
  • Virbickaite, A., Auśin, C., and Galeano, P. (2016), ‘A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model with Application to Portfolio Selection’, Computational Statistics and Data Analysis, 100, 814–829. doi: 10.1016/j.csda.2014.12.005

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.