Microstructure models offer an appropriate context for a comparison of dynamics of different financial instruments because they make a precise inference about a short term market sensitivity. Investors usually take actions based on the current and historical information and their own opinions but microstructure models can classify heterogeneous investors within three types, i.e. informed investors, noise investors and market-makers. The main feature that distinguishes these three groups is the access to information. Many theoretical models concerning an ideal market that reflects all possible information (Russel and Engle, 2010) have been constructed. Starting from the models by Bagehot (Bagehot, 1971), Garman (Garman, 1976), Grossman and Stiglitz (Grossman and Stiglitz, 1980), through more complicated models formulated by Kyle (Kyle, 1985) or Admati and Pfleiderer (Admati and Pfleiderer, 1988) up to the newest models by Hasbrouck (Hasbrouck, 2005) one can observe two things. First of all is the division of financial markets models into a price-driven market model and an order-driven market model, and, the second is evolutionary complication of the models. All the mentioned models and many others were discussed in details by Doman (Doman, 2011).

Another thing that is important when market microstructure is discussed is the quality of the market. The markets that are perceived as having a higher quality in comparison to the others can be counted as more popular and consequently more liquid markets (Russel and Engle, 2010). Therefore, before modeling, one should carefully read through the organization and the overall specificity of respondents stock markets. To check empirically the dynamics of the instruments quoted in the markets financial econometrics tools can be used (see for example Bień, 2006; Fernandes and Grammig, 2006; Russel and Engle, 2010; Doman, 2011).

The purpose of the paper consists in investigation of the relationships between volatility, duration, price and volume for selected joint stock companies quoted on the US and Russian financial markets (NYSE, NASDAQ and MOEX), besides, two Russian stocks quoted on the US market were also considered. As the data came from the first phase of the Ukrainian crisis (since 02/17/2014 to 04/04/2014) it became possible to show the impact on market microstructure elements caused by sanctions and disinformation campaign. For comparison, information from more neutral period of the same length (01/09/2013 – 10/17/2013) were also studied. The mentioned relationships were empirically tested using the following econometric tools related to high frequency data sets: ACD model, ACV model and one of the variants of the Manganelli model. The results of the estimation of ACD and ACV models showed a significant impact of new information on the expected duration of prices and expected volume for almost all Russian companies. Among US companies, this phenomenon practically did not exist.

One of the examples is AFLT company. It represents the largest airline in Russian Federation considering both: transport of passengers and transport of cargo. The empirical model that represents Manganelli’s approach is presented in the table 1.

Table 1. Estimates of the expected duration for price for AFLT company

It can be noticed that positive impact of real duration (p3) for expected duration is visible in both analyzed periods. In the first period the impact of volume (p2) was insignificant, while in the second period it was negative and significant. The impact of volatility (p4) in the model was statistically insignificant. It can be explained by domination of informed traders on the market. The rate of persistence coefficient (p1) was bigger in the first period in comparison to the second one.

Representatives of the NYSE and NASDAQ have higher rates of persistence than the companies from the MOEX, which confirms the hypothesis that this effect is stronger in more liquid markets. Study of the influence of the observed volume, duration and price volatility on the expected volume and the expected duration allowed for better description of the selected markets.

Based on the results it can be stated that on the MOEX a good channel for transmitting information from investors informed to the rest of the market does not exist. Individual investors disappear from the market during periods with higher risk, they cannot use the available information about volume and duration. The results obtained for the US companies are typical for companies listed on mature markets. The presence of many significant relationships allows to suspect that there are many quite large diversified groups of investors on the market. In the second analyzed period there was an increased uncertainty among investors, who were not eager to close their trades. Trades of the Russian companies’ stocks listed on the US stock market in the first analyzed period were similar to the rest of the companies from NASDAQ and the NYSE. After the Ukrainian crisis influence of the most of explanatory variables faded, which means disorder and distortion of the current process of information flow and the response of the investors. Trade shares of these companies has become more chaotic and unpredictable.

References:
Admati A. R., Pfleiderer P. (1988). A Theory of Intraday Patterns: Volume and Price. The Review of Financial Studies, 1, 3-40.
Bagehot W. (1971). The Only Game in Town. Financial Analysts Journal, Vol. 27, No. 2:12-14
Bień K. (2006). Zaawansowane specyfikacje modeli ACD - prezentacja oraz przykład zastosowania. Przegląd statystyczny 53, 91-108.
Doman M. (2011). Mikrostruktura giełd papierów wartościowych. Wydawnictwo UE, Poznań.
Fernandes M., Grammig J. (2006). A family of autoregressive conditional duration models. Journal of Econometrics 130, 1-23.
Garman M. (1976). Market Microstructure. Journal of Financial Economics, 3, 257-275.
Grossman S. J., Stiglitz J.E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 393-408.
Hasbrouck J. (2002). Stalking the “efficient price” in market microstructure specifications: an overview. Journal of Financial Markets, 5, 329–339.
Kyle A. S. (1985). Continuous auctions and insider trading. Econometrica, 53,1315-1335.
Russel J.R., Engle R. F. (2010). Analysis of High Frequency Data. In: Handbook of Financial Econometrics, (vol. 1), Y. Ait-Sahalia, L. P. Hansen (eds.). Elsevier.

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