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2016 | 17 | nr 2 | 241--260
Tytuł artykułu

Price Duration Versus Trading Volume in High-Frequency Data for Selected DAX Companies

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main goal of this paper is to gain insights into the dependence structure between the duration and trading volume of selected stocks listed on the Frankfurt Stock Exchange. We demonstrate the usefulness of the copula function to describe the dependence of specific unevenly spaced time series. The properties of the time series of price durations and trading volumes under study are in line with common observations from other empirical studies. We observe clustering, overdispersion, and diurnality. For most of the stocks, the seminal model (linear parametrization with exponential or Weibull distribution) can be replaced by a logarithmic specification with more-flexible conditional distributions. The price duration and trading volume associated with this duration exhibit dependence in the tails of distribution. We may conclude that high cumulative trading volumes are associated with long duration. However, changes of price over short times are related to low cumulative volume. (original abstract)
Słowa kluczowe
Rocznik
Tom
17
Numer
Strony
241--260
Opis fizyczny
Twórcy
  • AGH University of Science and Technology, Poland
autor
  • Jagiellonian University, Poland
  • Karl-Franzens-Universität Graz, Germany
Bibliografia
  • Admati, A.R. and Peiderer, P. (1988) 'A theory of intraday patterns: Volume and price variability', The Review of Financial Studies, vol. 1(1), pp. 3-40.
  • Alfonsi, A. and Schied, A. (2010) 'Optimal trade execution and absence of price manipulations in limit order book models', SIAM Journal on Financial Mathematics, vol. 1, pp. 490-522.
  • Almgren, R. (2003) 'Optimal execution with nonlinear impact functions and trading-enhanced risk', Applied Mathematical Finance, vol. 10(1), pp. 1-18.
  • Almgren, R. (2009) 'Optimal trading in a dynamic market', Working Paper, New York University.
  • Asmussen, S. (2003) 'Applied probability and queues', 2nd ed., Berlin: Springer.
  • Avellaneda, M. and Stoikov, S. (2008) 'High-frequency trading in a limit order book', Quantitative Finance, vol. 8, pp. 217-224.
  • Baum, L., Petrie, T., Soules, G. and Weiss, N. (1970) 'A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains', The Annals of Mathematical Statistics, vol. 41(1), pp. 164-171.
  • Bauwens, L. and Giot, P. (2000) 'The logarithmic ACD model: An application to the bid-ask quote process of three NYSE stocks', Annales D'economie Et De Statistique, vol. 60, pp. 117-149.
  • Bauwens, L. and Hautsch, N. (2009) 'Modelling financial high frequency data using point processes', in Mikosch, T., Kreiß, J.P., Davis R.A., and Andersen, T.G. (eds) Handbook of Financial Time Series, Berlin: Springer, pp. 953-979.
  • Bayraktar, E. and Ludkovski, M. (2011) 'Liquidation in limit order books with controlled intensity', Working Paper, University of Michigan and UCSB.
  • Biernacki, C., Celeux, G. and Govaert, G. (2001) 'Assessing a mixture model for clustering with the integrated completed likelihood', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22(7), pp. 719-725.
  • Bouchard, B., Dang, N.M. and Lehalle, C.A. (2011) 'Optimal control of trading algorithms: A general impulse control approach', SIAM Journal on Financial Mathematics, vol. 2, pp. 404-438.
  • Cappé, O., Moulines, E. and Rydén, T. (2005) Inference in hidden Markov models, Berlin: Springer.
  • Cartea, Á., Jaimungal. S. and Ricci, J. (2011) 'Buy low sell high: a high frequency trading perspective', SSRN eLibrary, [Online], Available: http://ssrn.com/abstract=1964781.
  • Cartea, Á. and Jaimungal, S. (2012) 'Risk metrics and fine tuning of high frequency trading strategies', Mathematical Finance, [Online], Available: http://dx.doi.org/10.1111/mafi.12023.
  • Cartea, Á. and Jaimungal, S. (2013) 'Modelling Asset Prices for Algorithmic and High-Frequency Trading', Applied Mathematical Finance, vol. 20 (6), pp. 512-547.
  • Cartea, Á. and Meyer-Brandis, T. (2010) 'How duration between trades of underlying securities affects option prices', Review of Finance, vol. 14(4), pp. 749-785.
  • Cartea, Á. and Penalva, J. (2012) 'Where is the value in high frequency trading?', Quarterly Journal of Finance, vol. 2(3), pp. 1-46.
  • Celeux, G. and Durand, J.B. (2008) 'Selecting hidden Markov model state number with cross-validated likelihood', Computational Statistics, vol. 23(4), pp. 541-564.
  • Cvitanic, J. and Kirilenko, A.A. (2010) 'High frequency traders and asset prices', SSRN eLibrary, [Online], Available: http://ssrn.com/abstract=1569067.
  • Diamond, D.W. and Verrechia, R.E. (1987) 'Constraints on short-selling and asset price adjustment to private information', Journal of Financial Economics, vol. 18, pp. 277-311.
  • Diebold, F.X., Gunther, T.A. and Tay, A.S. (1998) 'Evaluating density forecasts with applications to financial risk management', International Economic Review, vol. 39, pp. 863-883.
  • Dufour, A. and Engle, R.F. (2000) 'Time and the price impact of a trade', The Journal of Finance, vol. LV(6), pp. 2467-2498.
  • Easley, D. and O'Hara, M. (1992) 'Time and the process of security price adjustment', The Journal of Finance, vol. XLVII(2), pp. 577-605.
  • Engle, R.F. (2000) 'The econometrics of ultra-high-frequency data', Econometrica, vol. 68(1), pp. 1-22.
  • Engle, R.F. and Russell, J.R. (1998) 'Autoregressive conditional duration: A new model for irregularly spaced transaction data', Econometrica, vol. 66(5), pp. 1127-1162.
  • Fernandes, M. and Grammig, J. (2005) 'Nonparametric specification tests for conditional duration models', Journal of Econometrics, vol. 127(1), pp. 35-68.
  • Ghysels, E., Gourieroux C. and Jasiak, J. (2004), 'Stochastic Volatility Duration Models', Journal of Econometrics, vol. 119(2), pp. 413-433.
  • Gourieroux, C. and Jasiak, J., 2001, Financial Econometrics: Problems, Models and Methods, New Jersey: Princeton University Press.
  • Gramming, J. and Maurer, K.O. (2000) 'Non-Monotonic Hazard Functions and the Autoregressive Conditional Duration Model', The Econometrics Journal, vol. 3, pp. 16-38.
  • Gurgul, H. and Syrek, R. (2016) 'The logarithmic ACD model: The microstructure of the German and Polish stock markets', Managerial Economics, vol. 17(1), pp. 77-92.
  • Hujer, R., Vuletic, S. and Kokot, S. (2002) 'The Markov switching ACD model', SSRN eLibrary, [Online], Available: http://ssrn.com/abstract=332381.
  • Jaimungal, S. and Kinzebulatov, D. (2012) 'Optimal execution with a price limiter', SSRN eLibrary, [Online], Available: http://ssrn.com/abstract=2199889.
  • de Jong, F. and Rindi, B. (2009) 'The microstructure of financial markets', 1st ed., Cambridge: Cambridge University Press.
  • Kharroubi, I. and Pham, H. (2010) 'Optimal portfolio liquidation with execution cost and risk', SIAM Journal on Financial Mathematics, vol. 1, pp. 897-931.
  • Latza, T., Marsh, I. and Payne, R. (2012) 'Computer-based trading in the cross-section', Working Paper, Cass Business School.
  • Lorenz, J. and Almgren, R. (2011) 'Meanvariance optimal adaptive execution', Applied Mathematical Finance, vol. 18, pp. 395-422.
  • Lunde, A. (2000) 'A Generalized Gamma Autoregressive Conditional Duration Model', Discussion paper, Aarlborg University.
  • Maheu, J.M. and McCurdy, T.H. (2000) 'Volatility dynamics under durationdependent mixing', Journal of Empirical Finance, vol. 7(3-4), pp. 345-372.
  • Manganelli, S. (2005) 'Duration, volume and volatility impact of trades', Journal of Financial Markets, vol. 8(4), pp. 377-399.
  • Meitz, M. and Terasvirta, T. (2006) 'Evaluating models of autoregressive conditional duration', Journal of Business & Economic Statistics, vol. 24, pp. 104-124.
  • Mongillo, G. and Deneve, S. (2008) 'Online learning with hidden Markov models', Neural Computation, vol. 20(7), pp. 1706-1716.
  • Rémillard, B. (2010) 'Goodness-of-fit tests for copulas of multivariate time series', Working paper, HEC Montreal.
  • Renault, E., van der Heijden, T. and Werker, B.J.M. (2012) 'The dynamic mixed hitting-time model for multiple transaction prices and times', Working Paper, [Online] Available: http://dx.doi.org/10.2139/ssrn.2146220.
  • Patton, A. (2013) 'Copula Methods for Forecasting Multivariate Time Series, in Elliott, G. and Timmermann, A. (eds) Handbook of Economic Forecasting, vol. 2, London: Springer Verlag.
  • SEC (2010) 'Concept release on equity market structure', Concept Release No. 34-61358, File No. S7-02-10, SEC. 17 CFR PART 242.
  • Viterbi, A. (1967) 'Error bounds for convolutional codes and an asymptotically optimum decoding algorithm', IEEE Transactions on Information Theory, vol. 13(2), pp. 260-269.
  • Vuroenmaa, T.A. (2009) 'A q-Weibull Autoregressive Conditional Duration Model with an application to NYSE and HSE Data', [Online], Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1952550.
  • Zhang, M.Y., Russell, J.R. and Tsay, R.S. (2001) 'A nonlinear autoregressive conditional duration model with applications to financial transaction data', Journal of Econometrics, vol. 104(1), pp. 179-207.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171628534

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