In classic GARCH models for financial returns the innovations are usually assumed to be normally distributed. However, it is generally accepted that a non-normal innovation distribution is needed in order to account for the heavier tails often encountered in financial returns. Since the structure of the normal inverse Gaussian (NIG) distribution makes it an attractive alternative innovation distribution for this purpose, we extend the normal GARCH model by assuming that the innovations are NIG-distributed. We use the normal variance mixture interpretation of the NIG distribution to show that a NIG innovation may be interpreted as a normal innovation coupled with a multiplicative random impact factor adjustment of the ordinary GARCH volatility. We relate this new volatility estimate to realised volatility and suggest that the random impact factors are due to a news noise process influencing the underlying returns process. This GARCH model with NIG-distributed innovations leads to more accurate parameter estimates than the normal GARCH model. In order to obtain even more accurate parameter estimates, and since we expect an information gain if we use more data, we further extend the model to cater for high, low and close data, as well as full intraday data, instead of only daily returns. This is achieved by introducing the Brownian inverse Gaussian (BIG) process, which follows naturally from the unit inverse Gaussian distribution and standard Brownian motion. Fitting these models to empirical data, we find that the accuracy of the model fit increases as we move from the models assuming normally distributed innovations and allowing for only daily data to those assuming underlying BIG processes and allowing for full intraday data. However, we do encounter one problematic result, namely that there is empirical evidence of time dependence in the random impact factors. This means that the news noise processes, which we assumed to be independent over time, are indeed time dependent, as can actually be expected. In order to cater for this time dependence, we extend the model still further by allowing for autocorrelation in the random impact factors. The increased complexity that this extension introduces means that we can no longer rely on standard Maximum Likelihood methods, but have to turn to Simulated Maximum Likelihood methods, in conjunction with Efficient Importance Sampling and the Control Variate variance reduction technique, in order to obtain an approximation to the likelihood function and the parameter estimates. We find that this time dependent model assuming an underlying BIG process and catering for full intraday data fits generated data and empirical data very well, as long as enough intraday data is available. / Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2006.

Underpinning economic growth is the emergence of entrepreneurial ventures with the potential to grow that boost job creation and provide new sources of products for mature companies. The critical role associated with new firms, underscores the importance of understanding how entrepreneurship unfolds. Network-based research, while leading the way to rich empirical studies provides a limited understanding of how entrepreneurial networks are built and their impact on the emergence of a new venture. Employing a multiple case study design and a perspective based on organizational path building, three young technology ventures were investigated in terms of the formation of networks around five key entrepreneurial activities defined by entrepreneurs. Rich insight into new venture emergence is presented in terms of the reciprocal relationship between specific activities enacted by entrepreneurs and the networks that form to execute those activities revealing the path building mechanisms that evolve to drive network development. The findings of this research not only contribute to theories of new venture emergence, but also offer an interesting opportunity for future research into factors that may influence the outcome of entrepreneurial ventures and provide practical insight for organizations seeking to sustain or develop an entrepreneurial path.

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<p>The dissertation consists of four chapters that concern topics on copulas for high dimensions. Chapter 1 proposes a new general model for high dimension joint distributions of asset returns that utilizes high frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, which enables the use of high frequency data to accurately measure and forecast linear dependence, and the use of a new class of copulas designed to capture nonlinear dependence among the resulting linearly uncorrelated residuals. Estimation of the new class of copulas is conducted using a composite likelihood, making the model feasible even for hundreds of variables. A realistic simulation study verifies that multistage estimation with composite likelihood results in small loss in efficiency and large gain in computation speed. </p><p>Chapter 2, which is co-authored with Professor Andrew Patton, presents new models for the dependence structure, or copula, of economic variables based on a factor structure. The proposed models are particularly attractive for high dimensional applications, involving fifty or more variables. This class of models generally lacks a closed-form density, but analytical results for the implied tail dependence can be obtained using extreme value theory, and estimation via a simulation-based method using rank statistics is simple and fast. We study the finite-sample properties of the estimation method for applications involving up to 100 variables, and apply the model to daily returns on all 100 constituents of the S\&P 100 index. We find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. </p><p>Chapter 3, which is co-authored with Professor Andrew Patton, considers the estimation of the parameters of a copula via a simulated method of moments type approach. This approach is attractive when the likelihood of the copula model is not known in closed form, or when the researcher has a set of dependence measures or other functionals of the copula that are of particular interest. The proposed approach naturally also nests method of moments and generalized method of moments estimators. Drawing on results for simulation based estimation and on recent work in empirical copula process theory, we show the consistency and asymptotic normality of the proposed estimator, and obtain a simple test of over-identifying restrictions as a goodness-of-fit test. The results apply to both $iid$ and time series data. We analyze the finite-sample behavior of these estimators in an extensive simulation study.</p><p>Chapter 4, which is co-authored with Professor Andrew Patton, proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modelling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enable the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.</p> / Dissertation

This note examines the behavior of a competitive firm that faces joint price and inflation risk. Given that the price risk is negatively correlated with the inflation risk in the sense of expectation dependence, the firm optimally opts for an over-hedge if the firm's coefficient of relative risk aversion is everywhere no greater than unity. Furthermore, banning the firm from forward trading may induce the firm to produce more or less, depending on whether the price risk premium is positive or negative, respectively. While the price risk premium is unambiguously negative in the absence of the inflation risk, it is not the case when the inflation risk prevails. In contrast to the conventional wisdom, forward hedging needs not always promote production should firms take in inflation seriously.

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This thesis describes the design, construction and application of a novel vacuum system for the preparation of thin films of organic charge-transfer compounds. The method of thermal evaporation was used for four materials: tetrathiafulvalene (TTF) and three of its derivatives, dimethyltetrathiafiilvalene (DiMe-TTF), trimethyltetrathiafiilvalene (TriMe-TTF) and bis(ethylenedithio)tetrathiafiilvalene (BEDT-TTF). The resulting thin layers were characterised using optical and electron microscopy, infrared/visible spectroscopy and dc conductivity measurements down to 77K.Thin films of tetrathiafulvalene, after doping with iodine, exhibited a maximum value of dc, in-plane room temperature conductivity σ of 8.0+2.4 S cm(^-1). Semiconducting behaviour was exhibited over the range 77-300 K with AE = 0.09+0.02 eV. The effect of the deposition rate on fihn morphology is reported. TTF iodide layers were also prepared by co- evaporating the two components. These films exhibited a maximum conductivity of 2.9+0.4 S cm(^-1) at room temperature. Again, semiconducting behaviour was noted over the range 77- 300 K with AE = 0.2+0.02 eV. A comparison of the optical, structural and electrical properties of the two types of films is made. DiMe-TTF and TriMe-TTF thin films were also successfully prepared. Doping with iodine resulted in in-plane, dc room temperature conductivities of 10(^-6) and 10(^-7) S cm(^-1), respectively. These values, together with data from optical spectroscopy, suggested that both salts were in the full charge-transfer state. (BEDT-TTF) iodide thin films were deposited by evaporating the organic compound and subsequent doping. Doped films possessed a dc, in-plane room temperature conductivity of 10(^-3) S cm(^-1).Annealing these layers at 60ºC resulted in an increase in conductivity with a final value of 1.6 S cm(^-1). Semiconducting behaviour over the range 77-300 K was exhibited by the annealed films (ΔE = 0.028 eV).Finally, thin film transistors, incorporating TTF and BEDT-TTF doped layers, were fabricated and their electrical characteristics measured.

This thesis presents an investigation into the factors which control the gain and amplification properties in conjugated materials. Conjugated polymers and perylene dyes are highly fluorescent, are easy to process into thin films, and exhibit strong amplification over a broad gain bandwidth making them ideal for use in lasers and amplifiers. The stimulated emission created when thin films of the red emitting polymer poly(2-methoxy-5-(2'-ethylhexyloxy)- p-phenylenvinylene) (MEH-PPV) were photoexcited with high energy laser pulses was investigated. This was characterised by a dramatic narrowing of the emission spectrum which has been assigned to amplified spontaneous emission (ASE). The emission was found to have a gaussian profile and the gain coefficient was found to be 4 cm-1.The temperature dependence of the absorption, photoluminescence and ASE of films of MEH-PPV was measured. The effect of film morphology on the photophysical properties was investigated by using films cast from two spinning solvents, chlorobenzene (CB) and tetrahydrofuran (THE). Film morphology was found to greatly affect the temperature dependence. A particularly important property is the spectral position of the ASE and the factors which affect it. By controlling the film thickness close to the cut-off thickness for waveguiding in the polymer film it was shown that the peak position of the ASE could be tuned by 31 nm. Modelling of the waveguide modes in the polymer films was used to explain this effect. The cut-off wavelength for each film was measured and good agreement with the theory was found. In order to investigate ways in which energy transfer could be used to control the emission, two perylene dyes were used as a donor-acceptor pair in a host matrix of poly methymethacralate (PMMA). The position of the ASE was found to depend on the acceptor concentration. Measurements of the photoluminescence quantum yield and time-resolved luminescence measurements showed that the energy transfer coefficient was 5x10(^11)mol(^-1)d(^3)

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