Dagmar Linnertová

The Poisson Regression Analysis for Occurrence of Floods

Currently, flood risk can be considered as the most serious threat, mainly in areas and countries where hardly any other natural risks occur. In order to analyze this kind of natural disaster, it is necessary to examine the long-term development of its occurrence around the world as well as its financial and other consequences. The main objective of the research was to apply Poisson regression on flood occurrence as the dependent variable. The set of explanatory variables under consideration was tested and subsequently the final model was determined. Poisson regression model, which is a generalized linear model, was chosen as a computing model. Using it guarantees consistent results when working with variables with non-normal data distribution (skewed and discrete). Thus OLS estimator cannot work and is replaced by MLE estimator. Consequently, confidence intervals of estimated parameters and all model results can be received. The research resulted in selecting the Poisson regression model with an estimated and significant parameter. Moreover, sample sets of selected countries were compared and evaluated in terms of overall intensity of flood occurrence.

Liquidity of the European Stock Markets Under the Influence of HFT

Algorithmic trading and especially high frequency trading is the concern of the current research studies as well as legislative authorities. It is also the subject of criticism mostly from mostly low frequency traders and long-term institutional investors. This is mostly due to several cases of market manipulation and flash crashes in the previous years. Advocates of this trading mechanism claim that it has large positive influence on the market, such as liquidity growth by lowering spreads and others. This paper is focused on testing the relationship between market liquidity of futures traded on EUREX Exchange and HFT activity on European derivative markets. Econometrical methods for time series analysis are used to determine these relations. Results of this paper will reveal the relevance of the HFT traders main argument about creating liquidity and hence reducing of all the market risks related with high spreads and low number of limit orders.

Network Structures of the US Market with ETFs

Abstract The paper examines dependability and connectedness of US ETFs market. The ETFs market represents one of the most dynamic developing markets with new financial instruments. Since 1993, when the first ETF called Spider was introduced in dual listing in US and Canada, this market has grown to more than 3.200 ETF products at the end of 2013. About one third of these ETFs are traded in the United States in markets of NYSE that is dominating, Nasdaq or BATS. In the US market there are traded ETFs in the asset value more than 1.150 billion. Despite its fast growth and significant size, there is very little research on the structure of the ETFs market. The usage of network structure helps to fully identify relation between particular ETFs. In the analysis the most important ETFs (measured with their asset value) will be analysed on the individual and the provider basis. The provider basis is chosen because the US ETFs market is concentrated and three main providers (iShares, State Street Global Advisory and Vanguard) represent more than 80 percentage of the ETFs market. The application of the graph theory contributes to particularized understanding relations between these ETFs. Network modelling of ETFs market offers a powerful tool, because it provides much view on dependences of ETFss behaviour.

Cross-sectional Analysis of Short Sale of NYSE's Blue Chips☆

Abstract The aim of the paper is to analyse the determinants of short sale activity of blue chips on NYSE in the period 2000 – 2014. In the paper the fixed effect panel regression model is applied. The determinants of short selling activity are chosen according to previous literature review. The investigate period was divided into three separate sub periods that correspond with different economics conditions in the period 2000 – 2014. Analysed variables represent company fundamentals and market specifics characteristics. The impact of analysed variables on short sale level is long-term stable and constants.

Cross Sectional Analysis of Short Sale Determinants on U.S. Blue Chips

Short sale is a market practice that allows participle in overpricing markets. The fundamental goal of short sale is to sell borrowed securities, repurchase them back after their prices decrease and then return them to a lender. The aim of this paper is to investigate determinants of the short sale (measured by short sale ratio or SIR) activity. Based on the previous studies the short sale determinants are represented by market specific variables and fundamental-to-price ratios and correspond with hypotheses that explain investor motivations of going short. A panel regression with fixed effect is applied to determine these variables. The trend of short sale is analyzed by splitting the full sample period in three sub periods. There are identified factors such as abnormal rate of return, volume of trade, volatility, market capitalization and beta coefficient that are stable long term and influence the level of short sale. The results of fundamentals-to price ratio is not unambiguous and these variables do not considerably influence the level of short sale.