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on Market Microstructure |
| By: | Taras Bodnar; Nikolaus Hautsch; ; |
| Abstract: | We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows disentangling (multivariate) dynamics in higher order moments. To capture the latter, we propose a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks. |
| Keywords: | multiplicative error model, trading processes, copula, DCC-GARCH, liquidity risk |
| JEL: | C32 C58 C46 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-044&r=mst |
| By: | Bartosz Gębka (Newcastle University Business School); Dobromił Serwa (National Bank of Poland, Financial System Department; Warsaw School of Economics, Institute of Econometrics) |
| Abstract: | We analyse investors‟ motives for trading on international stock markets and investigate whether evidence for these motives is robust when time-varying market volatility, changes between calm and turbulent periods, and existence of international financial spillovers are controlled for. Applying the Markov-switching GARCH specification of the standard model commonly used in the literature, we find that trades conducted due to liquidity needs or driven by private information cannot be identified unequivocally in any market, and positive feedback trading becomes predominant when return spillovers from the US market are taken into account. |
| Keywords: | Informed trading, liquidity trading, feedback trading, return autocorrelation, trading volume, financial spillovers, contagion. |
| JEL: | C32 G12 G15 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:nbp:nbpmis:119&r=mst |
| By: | Thilo A. Schmitt; Rudi Sch\"afer; Michael C. M\"unnix; Thomas Guhr |
| Abstract: | The distribution of returns in financial time series exhibits heavy tails. In empirical studies, it has been found that gaps between the orders in the order book lead to large price shifts and thereby to these heavy tails. We set up an agent based model to study this issue and, in particular, how the gaps in the order book emerge. The trading mechanism in our model is based on a double-auction order book, which is used on nearly all stock exchanges. In situations where the order book is densely occupied with limit orders we do not observe fat-tailed distributions. As soon as less liquidity is available, a gap structure forms which leads to return distributions with heavy tails. We show that return distributions with heavy tails are an order-book effect if the available liquidity is constrained. This is largely independent of the specific trading strategies. |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1207.2946&r=mst |