Volatility Smile Dynamics in Scenario Analysis

The objective of this paper is to suggest a method that accounts for the impact of the volatility smile dynamics when performing scenario analysis for a portfolio consisting of vanilla options. As the volatility smile is documented to change at least with the level of implied at-the-money volatility, a suitable model is here included in the calculation process of the simulated market scenarios. By constructing simple portfolios of index options and comparing the ex ante risk exposure measured using different pricing methods to realized market values, ex post, the improvements of the incorporation of the model are monitored. The analyzed examples in the study generate results that statistically support that the most accurate scenarios are those calculated using the model accounting for the dynamics of the smile. Thus, we show that the differences emanating from the volatility smile are apparent and should be accounted for and that the methodology presented herein is one suitable alternative for doing so.

Pricing Index Options with Stochastic Volatility

The objective of this paper is to investigate the pricing accuracy under stochastic volatility where the volatility follows a square root process. The theoretical prices are compared with market price data (the German DAX index options market) by using two different techniques of parameter estimation, the method of moments and implicit estimation by inversion. Standard Black & Scholes pricing is used as a benchmark. The results indicate that the stochastic volatility model with parameters estimated by inversion using the available prices on the preceding day, is the most accurate pricing method of the three in this study and can be considered satisfactory. However, as the same model with parameters estimated using a rolling window (the method of moments) proved to be inferior to the benchmark, the importance of stable and correct estimation of the parameters is evident.

Expression of functional GABAc receptors in the brain

γ-aminobutyric acid (GABA) is the main inhibitory transmitter in the nervous system and acts via three distinct receptor classes: A, B, and C. GABAC receptors are ionotropic receptors comprising ρ subunits. In this work, we aimed to elucidate the expression of ρ subunits in the postnatal brain, the characteristics of ρ2 homo-oligomeric receptors, and the function of GABAC receptors in the hippocampus. In situ hybridization on rat brain slices showed ρ2 mRNA expression from the newborn in the superficial grey layer of the superior colliculus, from the first postnatal week in the hippocampal CA1 region and the pretectal nucleus of the optic tract, and in the adult dorsal lateral geniculate nucleus. Quantitative RT-PCR revealed expression of all three ρ subunits in the hippocampus and superior colliculus from the first postnatal day. In the hippocampus, ρ2 mRNA expression clearly dominated over ρ1 and ρ3. GABAC receptor protein expression was confirmed in the adult hippocampus, superior colliculus, and dorsal lateral geniculate nucleus by immunohistochemistry. From the selective distribution of ρ subunits, GABAC receptors may be hypothesized to be specifically involved in aspects of visual image motion processing in the rat brain. Although previous data had indicated a much higher expression level for ρ2 subunit transcripts than for ρ1 or ρ3 in the brain, previous work done on Xenopus oocytes had suggested that rat ρ2 subunits do not form functional homo-oligomeric GABAC receptors but need ρ1 or ρ3 subunits to form hetero-oligomers. Our results demonstrated, for the first time, that HEK 293 cells transfected with ρ2 cDNA displayed currents in whole-cell patch-clamp recordings. Homomeric rat ρ2 receptors had a decreased sensitivity to, but a high affinity for picrotoxin and a marked sensitivity to the GABAC receptor agonist CACA. Our results suggest that ρ2 subunits may contribute to brain function, also in areas not expressing other ρ subunits. Using extracellular electrophysiological recordings, we aimed to study the effects of the GABAC receptor agonists and antagonists on responses of the hippocampal neurons to electrical stimulation. Activation of GABAC receptors with CACA suppressed postsynaptic excitability and the GABAC receptor antagonist TPMPA inhibited the effects of CACA. Next, we aimed to display the activation of the GABAC receptors by synaptically released GABA using intracellular recordings. GABA-mediated long-lasting depolarizing responses evoked by high-frequency stimulation were prolonged by TPMPA. For weaker stimulation, the effect of TPMPA was enhanced after GABA uptake was inhibited. Our data demonstrate that GABAC receptors can be activated by endogenous synaptic transmitter release following strong stimulation or under conditions of reduced GABA uptake. The lack of GABAC receptor activation by less intensive stimulation under control conditions suggests that these receptors are extrasynaptic and activated via spillover of synaptically released GABA. Taken together with the restricted expression pattern of GABAC receptors in the brain and their distinctive pharmacological and biophysical properties, our findings supporting extrasynaptic localization of these receptors raise interesting possibilities for novel pharmacological therapies in the treatment of, for example, epilepsy and sleep disorders.

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.

Mathematical Aspects of Financial Markets with Frictions

Frictions are factors that hinder trading of securities in financial markets. Typical frictions include limited market depth, transaction costs, lack of infinite divisibility of securities, and taxes. Conventional models used in mathematical finance often gloss over these issues, which affect almost all financial markets, by arguing that the impact of frictions is negligible and, consequently, the frictionless models are valid approximations. This dissertation consists of three research papers, which are related to the study of the validity of such approximations in two distinct modeling problems. Models of price dynamics that are based on diffusion processes, i.e., continuous strong Markov processes, are widely used in the frictionless scenario. The first paper establishes that diffusion models can indeed be understood as approximations of price dynamics in markets with frictions. This is achieved by introducing an agent-based model of a financial market where finitely many agents trade a financial security, the price of which evolves according to price impacts generated by trades. It is shown that, if the number of agents is large, then under certain assumptions the price process of security, which is a pure-jump process, can be approximated by a one-dimensional diffusion process. In a slightly extended model, in which agents may exhibit herd behavior, the approximating diffusion model turns out to be a stochastic volatility model. Finally, it is shown that when agents' tendency to herd is strong, logarithmic returns in the approximating stochastic volatility model are heavy-tailed. The remaining papers are related to no-arbitrage criteria and superhedging in continuous-time option pricing models under small-transaction-cost asymptotics. Guasoni, Rásonyi, and Schachermayer have recently shown that, in such a setting, any financial security admits no arbitrage opportunities and there exist no feasible superhedging strategies for European call and put options written on it, as long as its price process is continuous and has the so-called conditional full support (CFS) property. Motivated by this result, CFS is established for certain stochastic integrals and a subclass of Brownian semistationary processes in the two papers. As a consequence, a wide range of possibly non-Markovian local and stochastic volatility models have the CFS property.

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.

Funktionaalinen Transitioteoria : Hallinto, Instituutiot ja Oikeusjärjestys Venäjällä

Functional transition theory: administration, legal order and institutions in Russia This dissertation examines some of the salient characteristics of Russia that are deemed to have impeded the growth of its economy and investments in particular. These characteristics are the volatility of the administrative and legal systems, corruption, and the perceived irrationality and difference in the operating environment in comparison with European conditions. The dissertation is one of the first studies on Russia that approaches the subject from the perspective of comprehensive social scientific theories. The study is based on the structural functionalistic theory, which is widely used in the social sciences. Adopting a sufficiently ambitious theoretical examination will provide a systematic and logical explanation of the characteristics of Russian institutions and ways of operations, such as corruption, that are commonly perceived as inexplicable. The approach adopted in the dissertation sheds light on the history of Russia's development and provides a comparative view of other societies in transition. Furthermore, it suggests recommendations as to how the structures of Russian society could be comprehensively strengthened.

Understanding the chemistry of atmospheric aerosol particle formation via observations of physical properties

It is widely accepted that the global climate is heating up due to human activities, such as burning of fossil fuels. Therefore we find ourselves forced to make decisions on what measures, if any, need to be taken to decrease our warming effect on the planet before any irrevocable damage occurs. Research is being conducted in a variety of fields to better understand all relevant processes governing Earth s climate, and to assess the relative roles of anthropogenic and biogenic emissions into the atmosphere. One of the least well quantified problems is the impact of small aerosol particles (both of anthropogenic and biogenic origin) on climate, through reflecting solar radiation and their ability to act as condensation nuclei for cloud droplets. In this thesis, the compounds driving the biogenic formation of new particles in the atmosphere have been examined through detailed measurements. As directly measuring the composition of these newly formed particles is extremely difficult, the approach was to indirectly study their different characteristics by measuring the hygroscopicity (water uptake) and volatility (evaporation) of particles between 10 and 50 nm. To study the first steps of the formation process in the sub-3 nm range, the nucleation of gaseous precursors to small clusters, the chemical composition of ambient naturally charged ions were measured. The ion measurements were performed with a newly developed mass spectrometer, which was first characterized in the laboratory before being deployed at a boreal forest measurement site. It was also successfully compared to similar, low-resolution instruments. The ambient measurements showed that sulfuric acid clusters dominate the negative ion spectrum during new particle formation events. Sulfuric acid/ammonia clusters were detected in ambient air for the first time in this work. Even though sulfuric acid is believed to be the most important gas phase precursor driving the initial cluster formation, measurements of the hygroscopicity and volatility of growing 10-50 nm particles in Hyytiälä showed an increasing role of organic vapors of a variety of oxidation levels. This work has provided additional insights into the compounds participating both in the initial formation and subsequent growth of atmospheric new aerosol particles. It will hopefully prove an important step in understanding atmospheric gas-to-particle conversion, which, by influencing cloud properties, can have important climate impacts. All available knowledge needs to be constantly updated, summarized, and brought to the attention of our decision-makers. Only by increasing our understanding of all the relevant processes can we build reliable models to predict the long-term effects of decisions made today.