Modelling High Frequency Financial Count Data

This thesis comprises two papers concerning modelling of financial count data. The papers advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. Paper [1] advances the INMA model to model the number of transactions in stocks in intra-day data. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. There is evidence of asymmetric effects of news about prices on the number of transactions. Paper [2] introduces a bivariate integer-valued moving average model (BINMA) and applies the BINMA model to the number of stock transactions in intra-day data. The BINMA model allows for both positive and negative correlations between the count data series. The study shows that the correlation between series in the BINMA model is always smaller than 1 in an absolute sense. The conditional mean, variance and covariance are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series.Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance

Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks

The integer-valued moving average model is advanced to model the number of transactions in intra-day data of stocks. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study the least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. News about prices are found to exert a symmetric and positive effect on the number of transactions.Count data; Intra-day; High frequency; Time series; Estimation; Finance.

Extreme-Value Characteristics in Daily Time Series of Swedish Stock Returns

The paper studies Swedish stock series using extreme-value theoretical approaches. In a univariate setting support is found for the Fréchet family of distributions for minima and maxima. Pairs of return series are found to be asymptotically independent throughout. The results render support for joint modelling based on flexible moment specifications or, e.g., copulas.Value-at-Risk; minimum/maximum return; crossing

Do Regional Investment Grants Improve Firm Performance? Evidence from Sweden

The effect of Swedish regional investment grants during 1990-1999 on firm performance, in terms of returns on equity and number of employees, were studied using a propensity-score matching-method to control for sample selection. Firms that received grants did not perform better in terms of returns on equity when compared to matched firms in the control group. In most years, recipient firms also did not hire more employees. The results thus cast doubt on the use of regional investment grants as a general policy instrument to improve firm performance.Economic efficiency; propensity score matching; sample selection; logit regression; panel data

Do Regional Investment Grants Improve Firm Performance? Evidence from Sweden

The effect of Swedish regional investment grants during 1990-1999 on firm performance, in terms of returns on equity and number of employees, were studied using a propensity-score matching-method to control for sample selection. Firms that received grants did not perform better in terms of returns on equity when compared to matched firms in the control group. In most years, recipient firms also did not hire more employees. The results thus cast doubt on the use of regional investment grants as a general policy instrument to improve firm performance.Economic efficiency; propensity score matching; sample selection; logit regression; panel data

Equity market contagion during global financial and Eurozone crises: Evidence from a dynamic correlation analysis

The devastation resulting from the recent global financial and Eurozone crises is immense. Most researchers commonly believe that the global financial crisis originated in the United States, and spread immediately to global financial hubs where it eventually became the Eurozone crisis. Several studies have been conducted on financial market contagion during both global and Eurozone crises; however, the issue of whether equity market contagion spreads from the United States to the world equity markets during these crises has not been addressed yet. Through using US dollar-denominated MSCI daily indices from fifty-five equity markets for the period 2003–2013, we have found evidence of contagion in developed and emerging markets during the global and Eurozone crises. We show that contagion spread from the United States to the world markets during both crises. Our regression results identify that the bank risk transfer between the United States and other countries is the key transmission channel for cross-country correlations. This study has an important policy implication for portfolio diversification between the United States and other countries during these crises

TIME SERIES MODELLING OF HIGH FREQUENCY STOCK TRANSACTION DATA

This thesis comprises four papers concerning modelling of financial count data. Paper [1], [2] and [3] advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. Paper [4] focuses on modelling the long memory property of time series of count data and on applying the model in a financial setting. Paper [1] advances the INMA model to model the number of transactions in stocks in intraday data. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. There is evidence of asymmetric effects of news about prices on the number of transactions. Paper [2] introduces a bivariate integer-valued moving average (BINMA) model and applies the BINMA model to the number of stock transactions in intra-day data. The BINMA model allows for both positive and negative correlations between the count data series. The study shows that the correlation between series in the BINMA model is always smaller than one in an absolute sense. The conditional mean, variance and covariance are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series. Paper [3] introduces a vector integer-valued moving average (VINMA) model. The VINMA model allows for both positive and negative correlations between the counts. The conditional and unconditional first and second order moments are obtained. The CLS and FGLS estimators are discussed. The model is capable of capturing the covariance between and within intra-day time series of transaction frequency data due to macroeconomic news and news related to a specific stock. Empirically, it is found that the spillover effect from Ericsson B to AstraZeneca is larger than that from AstraZeneca to Ericsson B. Paper [4] develops models to account for the long memory property in a count data framework and applies the models to high frequency stock transactions data. The unconditional and conditional first and second order moments are given. The CLS and FGLS estimators are discussed. In its empirical application to two stock series for AstraZeneca and Ericsson B, we find that both series have a fractional integration property.Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance

Mycorrhizas and biomass crops: opportunities for future sustainable development

Central to soil health and plant productivity in natural ecosystems are in situ soil microbial communities, of which mycorrhizal fungi are an integral component, regulating nutrient transfer between plants and the surrounding soil via extensive mycelial networks. Such networks are supported by plant-derived carbon and are likely to be enhanced under coppiced biomass plantations, a forestry practice that has been highlighted recently as a viable means of providing an alternative source of energy to fossil fuels, with potentially favourable consequences for carbon mitigation. Here, we explore ways in which biomass forestry, in conjunction with mycorrhizal fungi, can offer a more holistic approach to addressing several topical environmental issues, including ‘carbon-neutral’ energy, ecologically sustainable land management and CO2 sequestration

Assessment of the Diversity of Fungal Community Composition Associated With Vachellia pachyceras and Its Rhizosphere Soil From Kuwait Desert

This research examined the general soil fungi and AM fungal communities associated with a Lonely Tree species (Vachellia pachyceras) existing in the Sabah Al-Ahmad Natural Reserve located at the Kuwait desert. The goals of the study were to describe the general fungal and AM fungal communities present in the rhizospheric, non-rhizospheric soils and roots of V. pachyceras, respectively, as well as local and non-local V. pachyceras seedlings when grown under standard nursery growing environments. Soil and root samples were analyzed for an array of characteristics including soil physicochemical composition, and culture-independent method termed PCR-cloning, intermediate variable region of rDNA, the large subunit (LSU) and internal transcribed spacer (ITS) region sequence identifications. The results reveal that the fungal phylotypes were classified in four major fungal phyla namely Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota. The largest assemblage of fungal analyses showed communities dominated by members of the phylum Ascomycota. The assays also revealed a wealth of incertae sedis fungi, mostly affiliated to uncultured fungi from diverse environmental conditions. Striking difference between rhizosphere and bulk soils communities, with more fungal diversities and Operational Taxonomic Units (OTUs) richness associated with both the field and nursery rhizosphere soils. In contrast, a less diverse fungal community was found in the bulk soil samples. The characterization of AM fungi from the root system demonstrated that the most abundant and diversified group belongs to the family Glomeraceae, with the common genus Rhizophagus (5 phylotypes) and another unclassified taxonomic group (5 phylotypes). Despite the harsh climate that prevails in the Kuwait desert, studied roots displayed the existence of considerable number of AM fungal biota. The present work thus provides a baseline of the fungal and mycorrhizal community associated with rhizosphere and non-rhizosphere soils and roots of only surviving V. pachyceras tree from the Kuwaiti desert and seedlings under nursery growing environments

Modelling overdispersion with integer-valued moving average processes

A new first-order integer-valued moving average, INMA(1), model based on the negative binomial thinning operation defined by Risti´c et al. [21] is proposed and characterized. It is shown that this model has negative binomial (NB) marginal distribution when the innovations follow a NB distribution and therefore it can be used in situations where the data present overdispersion. Additionally, this model is extended to the bivariate context. The Generalized Method of Moments (GMM) is used to estimate the unknown parameters of the proposed models and the results of a simulation study that intends to investigate the performance of the method show that, in general, the estimates are consistent and symmetric. Finally, the proposed model is fitted to a real dataset and the quality of the adjustment is evaluated.publishe