Principal researcher

Miloslav Vošvrda

Members of the working group

Ladislav Krištoufek, Martin Šmíd, Lukáš Vácha, Miloslav Vošvrda, Evžen Kočenda, Krenar Avdulaj, Jiri Kukacka, Jan Vorisek, Jaroslava Maresova, Milan Hanousek, Tomáš Křehlík, Luboš Hanus, Jozef Baruník, Petr Marek, Pavel Irinkov, Ales Maršal, Frantisek Cech, Matej Nevrla

The research objectives

The increasing availability of high frequency data for a wide range of securities has brought researchers to a deeper understanding of the stock market’s behaviour in the past decade. Several key questions in financial econometrics literature have emerged. Among the most important ones are: the identification of the sources of jumps, the study of the noise impact on the volatility and covariance measures, understanding of the market makers’ behaviour or the study of the multivariate dynamics of the stock markets are avenues where researchers have not yet come to any satisfactory conclusion. In this part of the project, we will address these issues by developing the methodology allowing us to extend the current state of knowledge. The multidisciplinary dimension of the project is due to the fact that this specific part of financial research lies on the intersection of probability theory, Euler equations, stochastic processes and fundamental economic theory. Our research will focus on following fields.

Price jumps and co-jumps identification.

The key role price jumps play in financial engineering triggered interest in the financial econometrics literature. Yet, there is still no clear consensus on how to identify price jumps properly. One branch of the literature considers new information as a primary source of price jumps (see e.g. Lee and Mykland (2008) and Lahaye et al. (2010)). Joulin et al. (2008) and Bouchaud et al. (2004) conclude that price jumps are usually caused by a local lack of liquidity on the market. They also claim that news announcements have a negligible effect on the origin of price jumps. The behavioural finance literature provides other explanations for price jumps. Schiller (2005) claims that price jumps are caused by market participants who themselves create an environment that tends to cause extreme reactions, and thus, price jumps occur. Finally, price jumps can be viewed as a manifestation of Black Swans, as discussed by Taleb (2007), where the jumps are rather caused by complex systemic interactions that cannot be easily tracked down. The substance of this part of the project is to develop a workable strategy for the identification of the price jumps on financial markets. We assume a great degree of randomness in the price movements, and account for non-normal price jumps by employing the Euler scheme for price evolution with price jumps. We employ five different specifications of price jumps. These five specifications are combined with the four different groups of indicators. We will utilize the simulation technique to identify the most accurate price jump indicators. Important generalizations will also be the development of methodology for disentangling jumps from co-jumps in multivariate settings.

Study of multivariate dependence.

While the research of statistical inference for realized variance is active, its generalization to realized covariance only emerges in literature. Griffin and Oomen (2011), Aït-Sahalia et al. (2010) deal with a microstructure noise and non-synchronous trading and propose a consistent and efficient estimator of realized covariance, Zhang (2011) generalizes his Two Scale Realized Variance (TRSV) estimator to the Two Scale Realized Covariance (TSCV), Audrino and Corsi (2010) propose a forecasting model for realized correlations. This part of the project aims at developing a new class of realized multivariate volatility estimators in time-frequency domain. The main contribution will be to bring unbiased and consistent estimators of realized covariance in time-frequency space, allowing us to study the dependence at various investment horizons. Moreover, we aim to carry on the research of the non-Gaussianity and dependence in multivariate settings as well as long-range cross correlations.

Dynamic model of market makers.

Dynamic model of market makers. Market makers’ behaviour and price creation is an important topic, which needs to be addressed in the econometrics of high frequency data. This part of the project will aim at proposing a dynamic model of a market maker, maximizing his discounted utility from consumption by taking a limit of a discrete time problem similar to Madhavan and Schmidt (1993). The importance of this work is the shift from a discrete to a continuous time setting. Moreover, long-range dependence of the microstructure data will be studied in order to propose a new type of sentiment measures.

References

  • Aït-Sahalia, Y., Fan, J., and Xiu, D. (2010): High Frequency Covariance Estimates with Noisy and Asynchronous Financial Data, Journal of the American Statistical Association, 105, 1504–1517.
  • Audrino, F., and Corsi, F. (2010). Modeling tick-by-tick realized correlations, Computational Statistics and Data Analysis, 54, 2372–2382.
  • Bouchaud, J-P., Kockelkoren, J., and Potters, M. (2004). Random Walks, Liquidity Molasses and Critical Response in Financial Markets. Finance (CFM) Working Paper Archive 500063, Finance, Capital Fund Management.
  • Griffin, J. E., and Oomen, R. C. A. (2011). Covariance Measurement in the Presence of Non- synchronous Trading and Market Microstructure Noise. Journal of Econometrics 160 (1), 58–68.
  • Joulin, A., Lefevre, A., Grunberg, D., and Bouchaud, J.-P. (2008). Stock Price Jumps: News and Volume Play a Minor Role. Quantitative Finance Papers by arXiv.org, 0803.1769.
  • Lahaye, J., Laurent, S., and Neely, C. J. (2010): Jumps, Cojumps and Macro Announcements, Journal of Applied Econometrics, 25 (6), 893-921
  • Lee, S. S. and Mykland, P. A. (2008). Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics. The Review of Financial Studies 21 (6), 2535–2563.
  • Madhavan, A. and Schmidt, S. (1993): An Analysis of Changes in Specialist Inventories and Quotations, Journal of Finance, XLVIII (5), 1595–1628.
  • Taleb, N. (2007): The Black Swan: The Impact of the Highly Improbable, Random House, New York.
  • Shiller, R. J. (2005): Irrational Exuberance, Princeton University Press, Princeton.
  • Zhang, L. (2011): Estimating Covariation: Epps Effect, Microstructure Noise, Journal of Econometrics, 160 (1), 33–47.