In the Shadow of Freedom : Life on board the oil tanker

The aim of this study was to look at the freedom of ordinary people as they construct it. The scope, however, was limited to contemporary Finnish sailors and their freedom discourses. The study belongs to the field of the anthropology of religions, which is part of comparative religion. Worldview, which is one of the key concepts in comparative religion, provided the broader theoretical basis of the study. The data consisted of 92 interviews with Finnish professional seafarers conducted in 1996, 1999, 2000 and 2005, field journals that were written during two periods of fieldwork in 1996 and 1999-2000, and correspondence with some of the seafarers during 1999-2005. The analysis process incorporated new rhetoric and metaphor theory. The thesis is in three parts. The first part discusses the methodological challenges of this type of ethnography, the second an ethnography of modern Finnish shipworld focuses on work, organization, hierarchy and gender, and the third part discusses the freedom concepts of seafarers. It was found that seafarers use two kinds of freedom discourse. The first is in line with the stereotypical Jack Tar, a free-roving sailor who is not bound to land and its mundane routines, and the second views shipworld as freedom from freedom, meaning one is not responsible for one s own actions because one is not free to make a choice. It was also found that seafarers are well aware of the stereotypical images that are attached to their profession: they not only deny them, but also utilize, reflect on and construct them.

Bioavailability aspects of hydrophobic contaminant degradation in soils

This thesis concentrates on bioavailability of organic soil contaminants in the context of bioremediation of soil contaminated with volatile or non-volatile hydrophobic pollutants. Bioavailability and biodegradation was studied from four viewpoints: (i) Improvement of bioavailability and biodegradation of volatile hydrocarbons in contained bioremediation systems at laboratory - and pilot-scale. (ii) Improvement of bioavailability of non-volatile, hydrophobic compounds in such systems. (iii) Biodegradation of a non-volatile hydrophobic compound in soil organic matter in microcosms. (iiii) Bioavailability of nitrogen in an open, full-scale bioremediation system. It was demonstrated that volatility of organic compounds can be controlled by amending the soil with adsorbents. The sorbed hydrocarbons were shown to be available to soil microbiota. As the result, biodegradation of the volatile hydrocarbons was greatly favored at the expense of volatilization. PAH compounds were shown to be mobilized and their bioavailability improved by a hydrophobic, non-toxic additive, vegetable oil. Bioavailability of the PAHs was recorded as an increased toxicity of the soil. In spite of the increased bioavailability, biodegradation of the PAHs decreased. In microcosms simulating boreal forest organic surface soil, PAH-compound (pyrene) was shown to be removed from soil biologically. Therefore hydrophobicity of the substrate does not necessarily mean low availability and biodegradation in organic soil. Finally, in this thesis it was demonstrated that an unsuitable source of nitrogen or its overdose resulted in wasteful spending of this nutrient and even harmful effects on soil microbes. Such events may inhibit rather than promote the bioremediation process in soil.

Molecular Biomonitoring during Rhizoremediation of Oil-Contaminated Soil

Rhizoremediation is the use of microbial populations present in the rhizosphere of plants for environmental cleanup. The idea of this work was that bacteria living in the rhizosphere of a nitrogen-fixing leguminous plant, goat's rue (Galega orientalis), could take part in the degradation of harmful monoaromatic hydrocarbons, such as benzene, toluene and xylene (BTEX), from oil-contaminated soils. In addition to chemical (e.g. pollutant concentration) and physical (e.g. soil structure) information, the knowledge of biological aspects (e.g. bacteria and their catabolic genes) is essential when developing the rhizoremediation into controlled and effective bioremediation practice. Therefore, the need for reliable biomonitoring methods is obvious. The main aims of this thesis were to evaluate the symbiotic G. orientalis - Rhizobium galegae system for rhizoremediation of oil-contaminated soils, to develop molecular methods for biomonitoring, and to apply these methods for studying the microbiology of rhizoremediation. In vitro, Galega plants and rhizobia remained viable in m-toluate concentrations up to 3000 mg/l. Plant growth and nodulation were inhibited in 500 mg/l m-toluate, but were restored when plants were transferred to clean medium. In the greenhouse, Galega showed good growth, nodulation and nitrogen fixation, and developed a strong rhizosphere in soils contaminated with oil or spiked with 2000 mg/l m-toluate. The high aromatic tolerance of R. galegae and the viability of Galega plants in oil-polluted soils proved this legume system to be a promising method for the rhizoremediation of oil-contaminated soils. Molecular biomonitoring methods were designed and/or developed further for bacteria and their degradation genes. A combination of genomic fingerprinting ((GTG)5-PCR), taxonomic ribotyping of 16S rRNA genes and partial 16S rRNA gene sequencing were chosen for molecular grouping of culturable, heterogeneous rhizosphere bacteria. PCR primers specific for the xylE gene were designed for TOL plasmid detection. Amplified enzyme-coding DNA restriction analysis (AEDRA) with AluI was used to profile both TOL plasmids (xylE primers) and, in general, aromatics-degrading plasmids (C230 primers). The sensitivity of the direct monitoring of TOL plasmids in soil was enhanced by nested C23O-xylE-PCR. Rhizosphere bacteria were isolated from the greenhouse and field lysimeter experiments. High genetic diversity was observed among the 50 isolated, m-toluate tolerating rhizosphere bacteria in the form of five major lineages of the Bacteria domain. Gram-positive Rhodococcus, Bacillus and Arthrobacter and gram-negative Pseudomonas were the most abundant genera. The inoculum Pseudomonas putida PaW85/pWW0 was not found in the rhizosphere samples. Even if there were no ecological niches available for the bioaugmentation bacterium itself, its conjugative catabolic plasmid might have had some additional value for other bacterial species and thus, for rhizoremediation. Only 10 to 20% of the isolated, m-toluate tolerating bacterial strains were also able to degrade m-toluate. TOL plasmids were a major group of catabolic plasmids among these bacteria. The ability to degrade m-toluate by using enzymes encoded by a TOL plasmid was detected only in species of the genus Pseudomonas, and the best m-toluate degraders were these Pseudomonas species. Strain-specific differences in degradation abilities were found for P.oryzihabitans and P. migulae: some of these strains harbored a TOL plasmid - a new finding observed in this work, indicating putative horizontal plasmid transfer in the rhizosphere. One P. oryzihabitans strain harbored the pWW0 plasmid that had probably conjugated from the bioaugmentation Pseudomonas. Some P. migulae and P. oryzihabitans strains seemed to harbor both the pWW0- and the pDK1-type TOL plasmid. Alternatively, they might have harbored a TOL plasmid with both the pWW0- and the pDK1-type xylE gene. The breakdown of m-toluate by gram-negative bacteria was not restricted to the TOL pathway. Also some gram-positive Rhodococcus erythropolis and Arthrobacter aurescens strains were able to degrade m-toluate in the absence of a TOL plasmid. Three aspects of the rhizosphere effect of G. orientalis were manifested in oil-contaminated soil in the field: 1) G. orientalis and Pseudomonas bioaugmentation increased the amount of rhizosphere bacteria. G. orientalis especially together with Pseudomonas bioaugmentation increased the numbers of m-toluate utilizing and catechol positive bacteria indicating an increase in degradation potential. 2) Also the bacterial diversity, when measured as the amount of ribotypes, was increased in the Galega rhizosphere with or without Pseudomonas bioaugmentation. However, the diversity of m-toluate utilizing bacteria did not significantly increase. At the community level, by using the 16S rRNA gene PCR-DGGE method, the highest diversity of species was also observed in vegetated soils compared with non-vegetated soils. Diversified communities may best guarantee the overall success in rhizoremediation by offering various genetic machineries for catabolic processes. 3) At the end of the experiment, no TOL plasmid could be detected by direct DNA analysis in soil treated with both G. orientalis and Pseudomonas. The detection limit for TOL plasmids was encountered indicating decreased amount of degradation plasmids and thus, the success of rhizoremediation. The use of G. orientalis for rhizoremediation is unique. In this thesis new information was obtained about the rhizosphere effect of Galega orientalis in BTEX contaminated soils. The molecular biomonitoring methods can be applied for several purposes within environmental biotechnology, such as for evaluating the intrinsic biodegradation potential, monitoring the enhanced bioremediation, and estimating the success of bioremediation. Environmental protection by using nature's own resources and thus, acting according to the principle of sustainable development, would be both economically and environmentally beneficial for society. Keywords: molecular biomonitoring, genetic fingerprinting, soil bacteria, bacterial diversity, TOL plasmid, catabolic genes, horizontal gene transfer, rhizoremediation, rhizosphere effect, Galega orientalis, aerobic biodegradation, petroleum hydrocarbons, BTEX

Elements of Volatility at High Frequency

Volatility is central in options pricing and risk management. It reflects the uncertainty of investors and the inherent instability of the economy. Time series methods are among the most widely applied scientific methods to analyze and predict volatility. Very frequently sampled data contain much valuable information about the different elements of volatility and may ultimately reveal the reasons for time varying volatility. The use of such ultra-high-frequency data is common to all three essays of the dissertation. The dissertation belongs to the field of financial econometrics. The first essay uses wavelet methods to study the time-varying behavior of scaling laws and long-memory in the five-minute volatility series of Nokia on the Helsinki Stock Exchange around the burst of the IT-bubble. The essay is motivated by earlier findings which suggest that different scaling laws may apply to intraday time-scales and to larger time-scales, implying that the so-called annualized volatility depends on the data sampling frequency. The empirical results confirm the appearance of time varying long-memory and different scaling laws that, for a significant part, can be attributed to investor irrationality and to an intraday volatility periodicity called the New York effect. The findings have potentially important consequences for options pricing and risk management that commonly assume constant memory and scaling. The second essay investigates modelling the duration between trades in stock markets. Durations convoy information about investor intentions and provide an alternative view at volatility. Generalizations of standard autoregressive conditional duration (ACD) models are developed to meet needs observed in previous applications of the standard models. According to the empirical results based on data of actively traded stocks on the New York Stock Exchange and the Helsinki Stock Exchange the proposed generalization clearly outperforms the standard models and also performs well in comparison to another recently proposed alternative to the standard models. The distribution used to derive the generalization may also prove valuable in other areas of risk management. The third essay studies empirically the effect of decimalization on volatility and market microstructure noise. Decimalization refers to the change from fractional pricing to decimal pricing and it was carried out on the New York Stock Exchange in January, 2001. The methods used here are more accurate than in the earlier studies and put more weight on market microstructure. The main result is that decimalization decreased observed volatility by reducing noise variance especially for the highly active stocks. The results help risk management and market mechanism designing.

On measurements of formation, growth, hygroscopicity, and volatility of atmospheric ultrafine particles

Atmospheric aerosol particles affect the global climate as well as human health. In this thesis, formation of nanometer sized atmospheric aerosol particles and their subsequent growth was observed to occur all around the world. Typical formation rate of 3 nm particles at varied from 0.01 to 10 cm-3s-1. One order of magnitude higher formation rates were detected in urban environment. Highest formation rates up to 105 cm-3s-1 were detected in coastal areas and in industrial pollution plumes. Subsequent growth rates varied from 0.01 to 20 nm h-1. Smallest growth rates were observed in polar areas and the largest in the polluted urban environment. This was probably due to competition between growth by condensation and loss by coagulation. Observed growth rates were used in the calculation of a proxy condensable vapour concentration and its source rate in vastly different environments from pristine Antarctica to polluted India. Estimated concentrations varied only 2 orders of magnitude, but the source rates for the vapours varied up to 4 orders of magnitude. Highest source rates were in New Delhi and lowest were in the Antarctica. Indirect methods were applied to study the growth of freshly formed particles in the atmosphere. Also a newly developed Water Condensation Particle Counter, TSI 3785, was found to be a potential candidate to detect water solubility and thus indirectly composition of atmospheric ultra-fine particles. Based on indirect methods, the relative roles of sulphuric acid, non-volatile material and coagulation were investigated in rural Melpitz, Germany. Condensation of non-volatile material explained 20-40% and sulphuric acid the most of the remaining growth up to a point, when nucleation mode reached 10 to 20 nm in diameter. Coagulation contributed typically less than 5%. Furthermore, hygroscopicity measurements were applied to detect the contribution of water soluble and insoluble components in Athens. During more polluted days, the water soluble components contributed more to the growth. During less anthropogenic influence, non-soluble compounds explained a larger fraction of the growth. In addition, long range transport to a measurement station in Finland in a relatively polluted air mass was found to affect the hygroscopicity of the particles. This aging could have implications to cloud formation far away from the pollution sources.

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.

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.

Three Essays on the Volatility of Finnish Stock Returns (summary section only)

First, in Essay 1, we test whether it is possible to forecast Finnish Options Index return volatility by examining the out-of-sample predictive ability of several common volatility models with alternative well-known methods; and find additional evidence for the predictability of volatility and for the superiority of the more complicated models over the simpler ones. Secondly, in Essay 2, the aggregated volatility of stocks listed on the Helsinki Stock Exchange is decomposed into a market, industry-and firm-level component, and it is found that firm-level (i.e., idiosyncratic) volatility has increased in time, is more substantial than the two former, predicts GDP growth, moves countercyclically and as well as the other components is persistent. Thirdly, in Essay 3, we are among the first in the literature to seek for firm-specific determinants of idiosyncratic volatility in a multivariate setting, and find for the cross-section of stocks listed on the Helsinki Stock Exchange that industrial focus, trading volume, and block ownership, are positively associated with idiosyncratic volatility estimates––obtained from both the CAPM and the Fama and French three-factor model with local and international benchmark portfolios––whereas a negative relation holds between firm age as well as size and idiosyncratic volatility.

Maximum Loss Calculation using Scenario Analysis, Heavy Tails and Implied Volatility Patterns

The objective of this paper is to improve option risk monitoring by examining the information content of implied volatility and by introducing the calculation of a single-sum expected risk exposure similar to the Value-at-Risk. The figure is calculated in two steps. First, there is a need to estimate the value of a portfolio of options for a number of different market scenarios, while the second step is to summarize the information content of the estimated scenarios into a single-sum risk measure. This involves the use of probability theory and return distributions, which confronts the user with the problems of non-normality in the return distribution of the underlying asset. Here the hyperbolic distribution is used to describe one alternative for dealing with heavy tails. Results indicate that the information content of implied volatility is useful when predicting future large returns in the underlying asset. Further, the hyperbolic distribution provides a good fit to historical returns enabling a more accurate definition of statistical intervals and extreme events.

An Empirical Comparison of Linear and Nonlinear Volatility Models for Nordic Stock Returns

This paper examines how volatility in financial markets can preferable be modeled. The examination investigates how good the models for the volatility, both linear and nonlinear, are in absorbing skewness and kurtosis. The examination is done on the Nordic stock markets, including Finland, Sweden, Norway and Denmark. Different linear and nonlinear models are applied, and the results indicates that a linear model can almost always be used for modeling the series under investigation, even though nonlinear models performs slightly better in some cases. These results indicate that the markets under study are exposed to asymmetric patterns only to a certain degree. Negative shocks generally have a more prominent effect on the markets, but these effects are not really strong. However, in terms of absorbing skewness and kurtosis, nonlinear models outperform linear ones.

Evaluating the Predictability of the Volatility Smile Using Return Distributions

In this paper, we examine the predictability of observed volatility smiles in three major European index options markets, utilising the historical return distributions of the respective underlying assets. The analysis involves an application of the Black (1976) pricing model adjusted in accordance with the Jarrow-Rudd methodology as proposed in 1982. Thereby we adjust the expected future returns for the third and fourth central moments as these represent deviations from normality in the distributions of observed returns. Thus, they are considered one possible explanation to the existence of the smile. The obtained results indicate that the inclusion of the higher moments in the pricing model to some extent reduces the volatility smile, compared with the unadjusted Black-76 model. However, as the smile is partly a function of supply, demand, and liquidity, and as such intricate to model, this modification does not appear sufficient to fully capture the characteristics of the smile.

Unrealized expectations of jumps in volatility: An explanation to the low and time-varying predictive power of implied volatility

The low predictive power of implied volatility in forecasting the subsequently realized volatility is a well-documented empirical puzzle. As suggested by e.g. Feinstein (1989), Jackwerth and Rubinstein (1996), and Bates (1997), we test whether unrealized expectations of jumps in volatility could explain this phenomenon. Our findings show that expectations of infrequently occurring jumps in volatility are indeed priced in implied volatility. This has two important consequences. First, implied volatility is actually expected to exceed realized volatility over long periods of time only to be greatly less than realized volatility during infrequently occurring periods of very high volatility. Second, the slope coefficient in the classic forecasting regression of realized volatility on implied volatility is very sensitive to the discrepancy between ex ante expected and ex post realized jump frequencies. If the in-sample frequency of positive volatility jumps is lower than ex ante assessed by the market, the classic regression test tends to reject the hypothesis of informational efficiency even if markets are informationally effective.

The Influence of World Factors on Finnish Stock Market Volatility

This paper investigates to what extent the volatility of Finnish stock portfolios is transmitted through the "world volatility". We operationalize the volatility processes of Finnish leverage, industry, and size portfolio returns by asymmetric GARCH specifications according to Glosten et al. (1993). We use daily return data for January, 2, 1987 to December 30, 1998. We find that the world shock significantly enters the domestic models, and that the impact has increased over time. This applies also for the variance ratios, and the correlations to the world. The larger the firm, the larger is the world impact. The conditional variance is higher during recessions. The asymmetry parameter is surprisingly non-significant, and the leverage hypothesis cannot be verified. The return generating process of the domestic portfolio returns does usually not include the world information set, thus indicating that the returns are generated by a segmented conditional asset pricing model.