Gene flow from transgenic plant populations: models and applications for risk assessment

The future use of genetically modified (GM) plants in food, feed and biomass production requires a careful consideration of possible risks related to the unintended spread of trangenes into new habitats. This may occur via introgression of the transgene to conventional genotypes, due to cross-pollination, and via the invasion of GM plants to new habitats. Assessment of possible environmental impacts of GM plants requires estimation of the level of gene flow from a GM population. Furthermore, management measures for reducing gene flow from GM populations are needed in order to prevent possible unwanted effects of transgenes on ecosystems. This work develops modeling tools for estimating gene flow from GM plant populations in boreal environments and for investigating the mechanisms of the gene flow process. To describe spatial dimensions of the gene flow, dispersal models are developed for the local and regional scale spread of pollen grains and seeds, with special emphasis on wind dispersal. This study provides tools for describing cross-pollination between GM and conventional populations and for estimating the levels of transgenic contamination of the conventional crops. For perennial populations, a modeling framework describing the dynamics of plants and genotypes is developed, in order to estimate the gene flow process over a sequence of years. The dispersal of airborne pollen and seeds cannot be easily controlled, and small amounts of these particles are likely to disperse over long distances. Wind dispersal processes are highly stochastic due to variation in atmospheric conditions, so that there may be considerable variation between individual dispersal patterns. This, in turn, is reflected to the large amount of variation in annual levels of cross-pollination between GM and conventional populations. Even though land-use practices have effects on the average levels of cross-pollination between GM and conventional fields, the level of transgenic contamination of a conventional crop remains highly stochastic. The demographic effects of a transgene have impacts on the establishment of trangenic plants amongst conventional genotypes of the same species. If the transgene gives a plant a considerable fitness advantage in comparison to conventional genotypes, the spread of transgenes to conventional population can be strongly increased. In such cases, dominance of the transgene considerably increases gene flow from GM to conventional populations, due to the enhanced fitness of heterozygous hybrids. The fitness of GM plants in conventional populations can be reduced by linking the selectively favoured primary transgene to a disfavoured mitigation transgene. Recombination between these transgenes is a major risk related to this technique, especially because it tends to take place amongst the conventional genotypes and thus promotes the establishment of invasive transgenic plants in conventional populations.

Bioavailability and toxicity of chemicals in inland waters : the importance of prevailing water chemistry and implications for risk assessment

Physicochemical characterization of freshwater samples from Finland, Sweden, the Netherlands, and Spain revealed that water hardness and pH decreased and the quantity and quality of humic substances changed considerably in this geographical series from south to north. Since the ambient water chemistry may affect the availability of chemicals, the total aqueous concentration of a chemical may be insufficient to predict the bioconcentration, subsequent biological response, and thus risk. In addition, organisms could be affected directly by water quality characteristics. In this context the main objective of this thesis was to investigate the bioavailability of selected ecotoxicologically relevant chemicals (cadmium, benzo(a)pyrene, and pyrene) in various European surface waters and to show the importance of certain water chemistry characteristics in interpreting the bioavailability and toxicity results. The bioavailability of cadmium to Daphnia magna was examined in very soft humic lake water. Humic substances as natural ligands decreased the free and bioavailable proportion of cadmium in soft lake water. As a consequence the uptake rate and the acute toxicity decreased compared with the humic-free reference. When the hardness of humic lake water was artificially elevated, the acute toxicity of cadmium decreased, although the proportion of free cadmium increased. The decreased bioavailability of cadmium in hard water was a result of effective competition for uptake by the hardness cations, especially calcium ions. The protective role of humic substances and water hardness against cadmium toxicity was also observed in Lumbriculus variegatus, although D. magna was more sensitive to cadmium. The bioavailability of two polycyclic aromatic hydrocarbons (PAHs), pyrene and benzo(a)pyrene, was studied in European surface waters of varying water chemistry. Humic substances acted as complexing ligands with both PAHs, but the bioavailability of the more lipophilic benzo(a)pyrene to D. magna was affected more by humic substances than that of pyrene. In addition, not only the quantity of humic substances, but also their quality affected the bioavailability of benzo(a)pyrene. Nevertheless, the humic substances played a protective role in the photo-enhanced toxicity of pyrene under UV-B radiation. Water hardness had no effect on pyrene toxicity. Results indicate that the typical physicochemical characteristics of boreal freshwaters should be considered carefully in local and regional risk assessment of chemicals concerning the Fennoscandian region.

An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions

This paper uses the Value-at-Risk approach to define the risk in both long and short trading positions. The investigation is done on some major market indices(Japanese, UK, German and US). The performance of models that takes into account skewness and fat-tails are compared to symmetric models in relation to both the specific model for estimating the variance, and the distribution of the variance estimate used as input in the VaR estimation. The results indicate that more flexible models not necessarily perform better in predicting the VaR forecast; the reason for this is most probably the complexity of these models. A general result is that different methods for estimating the variance are needed for different confidence levels of the VaR, and for the different indices. Also, different models are to be used for the left respectively the right tail of the distribution.

Assessment of Risk for Type 1 Diabetes in Children of Affected Families and in the General Population : Role of Immunological and Metabolic Markers

Background and aims. Type 1 diabetes (T1D), an autoimmune disease in which the insulin producing beta cells are gradually destroyed, is preceded by a prodromal phase characterized by appearance of diabetes-associated autoantibodies in circulation. Both the timing of the appearance of autoantibodies and their quality have been used in the prediction of T1D among first-degree relatives of diabetic patients (FDRs). So far, no general strategies for identifying individuals at increased disease risk in the general population have been established, although the majority of new cases originate in this population. The current work aimed at assessing the predictive role of diabetes-associated immunologic and metabolic risk factors in the general population, and comparing these factors with data obtained from studies on FDRs. Subjects and methods. Study subjects in the current work were subcohorts of participants of the Childhood Diabetes in Finland Study (DiMe; n=755), the Cardiovascular Risk in Young Finns Study (LASERI; n=3475), and the Finnish Type 1 Diabetes Prediction and Prevention Study (DIPP) Study subjects (n=7410). These children were observed for signs of beta-cell autoimmunity and progression to T1D, and the results obtained were compared between the FDRs and the general population cohorts. --- Results and conclusions. By combining HLA and autoantibody screening, T1D risks similar to those reported for autoantibody-positive FDRs are observed in the pediatric general population. Progression rate to T1D is high in genetically susceptible children with persistent multipositivity. Measurement of IAA affinity failed in stratifying the risk assessment in young IAA-positive children with HLA-conferred disease susceptibility, among whom affinity of IAA did not increase during the prediabetic period. Young age at seroconversion, increased weight-for-height, decreased early insulin response, and increased IAA and IA-2A levels predict T1D in young children with genetic disease susceptibility and signs of advanced beta-cell autoimmunity. Since the incidence of T1D continues to increase, efforts aimed at preventing T1D are important, and reliable disease prediction is needed both for intervention trials and for effective and safe preventive therapies in the future. Our observations confirmed that combined HLA-based screening and regular autoantibody measurements reveal similar disease risks in pediatric general population as those seen in prediabetic FDRs, and that risk assessment can be stratified further by studying glucose metabolism of prediabetic subjects. As these screening efforts are feasible in practice, the knowledge now obtained can be exploited while designing intervention trials aimed at secondary prevention of T1D.

Modeling and Forecasting Implied Volatility: Implications for Trading, Pricing, and Risk Management

Modeling and forecasting of implied volatility (IV) is important to both practitioners and academics, especially in trading, pricing, hedging, and risk management activities, all of which require an accurate volatility. However, it has become challenging since the 1987 stock market crash, as implied volatilities (IVs) recovered from stock index options present two patterns: volatility smirk(skew) and volatility term-structure, if the two are examined at the same time, presents a rich implied volatility surface (IVS). This implies that the assumptions behind the Black-Scholes (1973) model do not hold empirically, as asset prices are mostly influenced by many underlying risk factors. This thesis, consists of four essays, is modeling and forecasting implied volatility in the presence of options markets’ empirical regularities. The first essay is modeling the dynamics IVS, it extends the Dumas, Fleming and Whaley (DFW) (1998) framework; for instance, using moneyness in the implied forward price and OTM put-call options on the FTSE100 index, a nonlinear optimization is used to estimate different models and thereby produce rich, smooth IVSs. Here, the constant-volatility model fails to explain the variations in the rich IVS. Next, it is found that three factors can explain about 69-88% of the variance in the IVS. Of this, on average, 56% is explained by the level factor, 15% by the term-structure factor, and the additional 7% by the jump-fear factor. The second essay proposes a quantile regression model for modeling contemporaneous asymmetric return-volatility relationship, which is the generalization of Hibbert et al. (2008) model. The results show strong negative asymmetric return-volatility relationship at various quantiles of IV distributions, it is monotonically increasing when moving from the median quantile to the uppermost quantile (i.e., 95%); therefore, OLS underestimates this relationship at upper quantiles. Additionally, the asymmetric relationship is more pronounced with the smirk (skew) adjusted volatility index measure in comparison to the old volatility index measure. Nonetheless, the volatility indices are ranked in terms of asymmetric volatility as follows: VIX, VSTOXX, VDAX, and VXN. The third essay examines the information content of the new-VDAX volatility index to forecast daily Value-at-Risk (VaR) estimates and compares its VaR forecasts with the forecasts of the Filtered Historical Simulation and RiskMetrics. All daily VaR models are then backtested from 1992-2009 using unconditional, independence, conditional coverage, and quadratic-score tests. It is found that the VDAX subsumes almost all information required for the volatility of daily VaR forecasts for a portfolio of the DAX30 index; implied-VaR models outperform all other VaR models. The fourth essay models the risk factors driving the swaption IVs. It is found that three factors can explain 94-97% of the variation in each of the EUR, USD, and GBP swaption IVs. There are significant linkages across factors, and bi-directional causality is at work between the factors implied by EUR and USD swaption IVs. Furthermore, the factors implied by EUR and USD IVs respond to each others’ shocks; however, surprisingly, GBP does not affect them. Second, the string market model calibration results show it can efficiently reproduce (or forecast) the volatility surface for each of the swaptions markets.

Electrocardiographic parameters of ventricular repolarization : modifiers and the prognostic value

Electric activity of the heart consists of repeated cardiomyocyte depolarizations and repolarizations. Abnormalities in repolarization predispose to ventricular arrhythmias. In body surface electrocardiogram, ventricular repolarization generates the T wave. Several electrocardiographic measures have been developed both for clinical and research purposes to detect repolarization abnormalities. The study aim was to investigate modifiers of ventricular repolarization with the focus on the relationship of the left ventricular mass, antihypertensive drugs, and common gene variants, to electrocardiographic repolarization parameters. The prognostic value of repolarization parameters was also assessed. The study subjects originated from a population of more than 200 middle-aged hypertensive men attending the GENRES hypertension study, and from an epidemiological survey, the Health 2000 Study, including more than 6000 participants. Ventricular repolarization was analysed from digital standard 12-lead resting electrocardiograms with two QT-interval based repolarization parameters (QT interval, T-wave peak to T-wave end interval) and with a set of four T-wave morphology parameters. The results showed that in hypertensive men, a linear change in repolarization parameters is present even in the normal range of left ventricular mass, and that even mild left ventricular hypertrophy is associated with potentially adverse electrocardiographic repolarization changes. In addition, treatments with losartan, bisoprolol, amlodipine, and hydrochlorothiazide have divergent short-term effects on repolarization parameters in hypertensive men. Analyses of the general population sample showed that single nucleotide polymorphisms in KCNH2, KCNE1, and NOS1AP genes are associated with changes in QT-interval based repolarization parameters but not consistently with T-wave morphology parameters. T-wave morphology parameters, but not QT interval or T-wave peak to T-wave end interval, provided independent prognostic information on mortality. The prognostic value was specifically related to cardiovascular mortality. The results indicate that, in hypertension, altered ventricular repolarization is already present in mild left ventricular mass increase, and that commonly used antihypertensive drugs may relatively rapidly and treatment-specifically modify electrocardiographic repolarization parameters. Common variants in cardiac ion channel genes and NOS1AP gene may also modify repolarization-related arrhythmia vulnerability. In the general population, T-wave morphology parameters may be useful in the risk assessment of cardiovascular mortality.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Vascular dilatory function and cardiovascular risk factors in women with a history of pre-eclampsia

Women with a history of pre-eclampsia have an increased risk of cardiovascular disease in later life. The mechanisms which mediate this heightened risk are poorly understood; it was long believed that pre-eclampsia was a separate disease without any connection to other pathologies. The present study was undertaken to investigate the cardiovascular risk milieu, vascular dilatory function and cardiovascular risk factors, in women with pre-eclampsia, 5 6 years after index pregnancy. The aim was to understand better the cardiovascular risks associated with pre-eclampsia and add tools to the evaluation of cardiovascular risk in women. --- The study involved 30 women with previous severe pre-eclampsia and 21 controls. The 2-day study protocol included venous occlusion plethysmography and pulse wave analysis for assessment of vascular dilatory function and central pulse wave reflection, respectively, office and ambulatory blood pressure measurements, assessment of insulin sensitivity, using a minimal model technique, and tests regarding renal function, lipid metabolism, sympathetic activity and inflammation. Vasodilatory function was impaired in women with a history of pre-eclampsia; this was seen in both endothelium-dependent and endothelium-independent vasodilatation. Proteinuria during pre-eclampsia did not predict changes in vasodilatation, and renal function was similar in the two groups. Insulin sensitivity was related to vasodilatation and features of metabolic syndrome, but only in the patient group, despite similar insulin sensitivity in the control group. Arterial pressure was higher in the patient group than in the controls and correlated with endothelin-1 levels in the patient group, whilst the overall difference between the groups was diminished in 24 hour arterial pressure measurements. Additionally, women with previous pre-eclampsia were characterized by increased sympathetic activity. Impaired vasodilatory function at the vascular smooth muscle level seems to characterize clinically healthy women with a history of pre-eclampsia. These vascular changes and the features of metabolic syndrome may be related to the increased risk of cardiovascular disease. Furthermore, increased blood pressure in combination with enhanced sympathetic activity may be additive as regards this risk. These women should be informed about their potential cardiovascular risk profile and the possibilities to minimize it via their own actions. Medical cardiovascular risk assessment in women should include obstetric history.

Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.

Essays on Risk Modeling. Applications to Portfolio and Risk Management (summary section only)

In this thesis we deal with the concept of risk. The objective is to bring together and conclude on some normative information regarding quantitative portfolio management and risk assessment. The first essay concentrates on return dependency. We propose an algorithm for classifying markets into rising and falling. Given the algorithm, we derive a statistic: the Trend Switch Probability, for detection of long-term return dependency in the first moment. The empirical results suggest that the Trend Switch Probability is robust over various volatility specifications. The serial dependency in bear and bull markets behaves however differently. It is strongly positive in rising market whereas in bear markets it is closer to a random walk. Realized volatility, a technique for estimating volatility from high frequency data, is investigated in essays two and three. In the second essay we find, when measuring realized variance on a set of German stocks, that the second moment dependency structure is highly unstable and changes randomly. Results also suggest that volatility is non-stationary from time to time. In the third essay we examine the impact from market microstructure on the error between estimated realized volatility and the volatility of the underlying process. With simulation-based techniques we show that autocorrelation in returns leads to biased variance estimates and that lower sampling frequency and non-constant volatility increases the error variation between the estimated variance and the variance of the underlying process. From these essays we can conclude that volatility is not easily estimated, even from high frequency data. It is neither very well behaved in terms of stability nor dependency over time. Based on these observations, we would recommend the use of simple, transparent methods that are likely to be more robust over differing volatility regimes than models with a complex parameter universe. In analyzing long-term return dependency in the first moment we find that the Trend Switch Probability is a robust estimator. This is an interesting area for further research, with important implications for active asset allocation.