|
on Market Microstructure |
| By: | Marwan Izzeldin |
| Abstract: | Trading volume and the number of trades are both used as proxies for market activity, with disagreement as to which is the better proxy for market activity. This paper investigates this issue using high frequency data for Cisco and Intel in 1997. A number of econometric methods are used, including GARCH augmented with lagged trading volume and number of trades, tests based on moment restrictions, regression analysis of volatility on volume and trades, normality of returns when standardized by volume and number of trades, and Correlation analysis using volatility generated from GARCH and realized volatility. Our results show that the number of trades is the better proxy for market activity. |
| Keywords: | Trading volume; number of trades; realized volatility, GARCH volatility, Mixture of distribution hypothesis |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:004798&r=mst |
| By: | Ingmar Nolte (University of Konstanz); Sandra Lechner (University of Konstanz) |
| Abstract: | This paper analyzes the relationship between currency price changes and their expectations. Currency price change expectations are derived with the help of different order flow measures, from the trading behavior of investors on OANDA FXTrade, which is an internet trading platform in the foreign exchange market. We investigate whether forecasts of intra-day price changes on different sampling frequencies can be improved with the information contained in the flow of our investors’ orders. Moreover, we verify several hypotheses on the trading behavior and the preference structure of our investors by investigating how past price changes affect future order flow. |
| Keywords: | Customer Dataset, Order Flow, Price Changes, Foreign Exchange Market |
| JEL: | G10 F31 C32 |
| Date: | 2007–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0703&r=mst |
| By: | Katarzyna Bien (University of Konstanz); Ingmar Nolte (University of Konstanz); Winfried Pohlmeier (University of Konstanz) |
| Abstract: | In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain Zn. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain, (ii) the tendency to cluster at certain outcome values and (iii) contemporaneous dependence. These kind of properties can be found for high or ultra-high frequent data describing the trading process on financial markets. We present a straightforward method of sampling from such an inflated multivariate density through the application of an Independence Metropolis-Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivari- ate density of bid and ask quote changes in a high frequency setup. We show how to derive the implied conditional discrete density of the bid-ask spread, taking quote clusterings (at multiples of 5 ticks) into account. |
| Keywords: | Multivariate Discrete Distributions, Conditional Inflation, Copula Functions, Truncations, Metropolized-Independence Sampler |
| JEL: | G10 F30 C30 |
| Date: | 2007–03–28 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0704&r=mst |
| By: | Taro Kanatani; Roberto Reno' |
| Abstract: | We study covariance estimation when compelled to use evenly spaced data which have already been manipulated by previous-tick interpolation. We propose an un- biased covariance estimator, which is designed to correct for the two biases arising because of the interpolation: non-synchronous trading and zero-return bias. We show how these sources make usual realized covariance estimators biased, and that the traditional lead-lag modification does not correct these biases completely. The proposed estimator is also proved to be consistent with the Hayashi and Yoshida (2005)’s unbiased estimator under extremely high frequency situation. We illustrate the potential advantages of the method with both simulated and actual data |
| Keywords: | Realized covariance; Previous tick interpolation; Epps effect; Nonsynchronous trading; Bias-correction |
| JEL: | C14 C32 C63 |
| Date: | 2007–04 |
| URL: | http://d.repec.org/n?u=RePEc:usi:wpaper:502&r=mst |
| By: | Ingmar Nolte (University of Konstanz); Valeri Voev (University of Konstanz) |
| Abstract: | We develop a panel intensity model, with a time varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. The model is designed to analyze individual trading behavior on the basis of trading activity datasets, which are characterized by four dimensions: an irregularly-spaced time scale, trading activity types, trading instruments and investors. Our approach extends the stochastic conditional intensity model of Bauwens & Hautsch (2006) to panel duration data. We show how to estimate the model parameters by a simulated maximum likelihood technique adopting the efficient importance sampling approach of Richard & Zhang (2005). We provide an application to a trading activity dataset from an internet trading platform in the foreign exchange market and we find support for the presence of behavioral biases and discuss implications for portfolio theory. |
| Keywords: | Trading Activity Datasets, Panel Intensity Models, Latent Factors, Efficient Importance Sampling, Behavioral Finance |
| JEL: | G10 F31 C32 |
| Date: | 2007–02–28 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0702&r=mst |