Stochastic volatility models with possible extremal clustering

In this paper we consider a heavy-tailed stochastic volatility model, $X_t=\sigma_tZ_t$, $t\in\mathbb{Z}$, where the volatility sequence $(\sigma_t)$ and the i.i.d. noise sequence $(Z_t)$ are assumed independent, $(\sigma_t)$ is regularly varying with index $\alpha>0$, and the $Z_t$'s have moments of order larger than $\alpha$. In the literature (see Ann. Appl. Probab. 8 (1998) 664-675, J. Appl. Probab. 38A (2001) 93-104, In Handbook of Financial Time Series (2009) 355-364 Springer), it is typically assumed that $(\log\sigma_t)$ is a Gaussian stationary sequence and the $Z_t$'s are regularly varying with some index $\alpha$ (i.e., $(\sigma_t)$ has lighter tails than the $Z_t$'s), or that $(Z_t)$ is i.i.d. centered Gaussian. In these cases, we see that the sequence $(X_t)$ does not exhibit extremal clustering. In contrast to this situation, under the conditions of this paper, both situations are possible; $(X_t)$ may or may not have extremal clustering, depending on the clustering behavior of the $\sigma$-sequence.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ426 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Kliimamuutuste mõju leht- ja okaspuude peenjuurte omadustele

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKliima, sademete, õhuniiskuse ja lämmastiku muutus mõjutab metsaökosüsteeme, eriti probleemsed on metsad turbamullal. Puude peenjuured jagatakse imi- ja transpordijuurteks, nende kõrge eripind ja -pikkus aitavad puul paremini ressursse omandada. Töö eesmärk oli analüüsida leht- ja okaspuude peenjuure kohanemist kliima- ja mullaoludega. Selleks hinnati hübriidhaava, hõbekase ja männi juurte süsinikuvooge, juurte ehituste muutlikkust mulla niiskuse ja lämmastiku suurenedes. Kuivendatud metsades uuriti imijuurte ehituse muutumist sõltuvalt kaugusega kraavist, et teha kindlaks kase ja kuuse peenjuurte plastilisemad tunnused. Avaldatud uuringute põhjal analüüsiti ka puude juurte reageerimist mulla soojenemisele. Uuringus ilmnesid mulla süsinikutasakaalu mõjutavad muutused. Kliimakambris vähenes kõrgenenud õhuniiskuse juures transpiratsioon. Männi fotosüntees kiirenes kõrgema õhuniiskuse juures. Maa-all vähendas kõrgem õhuniiskus oluliselt süsiniku vooge, suurenes juure eripind kasel, vähenes männil. Kuivendatud turbametsades kohanesid imijuured mulla seisundiga kasel ja kuusel sarnaselt, kaugusega kraavist suurenes keskmine juure eripind, koe tihedus vähenes. Mulla soojenemine suurendas peenjuurte produktsiooni liigiti erinevalt, vähenes soojendav mõju massile. Mullatemperatuuri tõus suurendab juurte kasvukiirust. Soojemates oludes kasvatavad puud rohkem peenjuurte massi, morfoloogilist plastilisust mõjutatakse vähem. Otstarbekas oleks analüüsida peenjuurte tunnuseid koos mulla mikrobioomidega, eriti juurtega seotud ektomükoriisa seentega ja multifaktoriaalsetes keskkonnatingimustes.  Changes in global temperature, precipitation, air humidity, and N deposition pose challenges to forest ecosystems. Of particular concern are peatland forests. Fine roots (<2 mm in diameter) are divided into absorptive and transport roots. High specific root area and length mean higher resource acquisition. The thesis aimed to analyze fine root acclimation of deciduous and coniferous trees to varying climate and soil. We evaluated fine root C fluxes and morphological variation of hybrid aspen, silver birch, and Scots pine in response to elevated humidity and soil inorganic N; the role of fine root functional groups in carbon exudation; and morphological variation of absorptive roots across the distance from the ditch in drained peatland forests dominated by birch and spruce to identify plasticity in fine root acclimation. Response of fine root biomass and morphology to soil warming at global scale was assessed in meta-analysis. The study showed species-specific responses affecting soil carbon balance. In climate chambers, enhanced humidity reduced transpiration. Enhanced humidity caused growth in pine photosynthesis, decrease in belowground carbon fluxes. Specific root area increased in birch but decreased in pine. The absorptive root morphological responses were uniform in birch and spruce. With increased distance from the ditch, specific root area increased, tissue density decreased. Soil warming increased fine root biomass differently for deciduous and coniferous species. The warming effects on fine root biomass decreased with greater warming magnitude. Rise in soil temperature stimulates root growth. Trees allocate more biomass to fine roots under warmer conditions, morphological plasticity is influenced less. More comprehensive analyses of fine root traits along with root-associated ectomycorrhizal fungi under multifactorial environmental conditions are needed.https://www.ester.ee/record=b555518

Evaluation of bred fish and seawater fish in terms of nutritional value, and heavy metals

In many parts of the world consumption of fish and seafood comprises a key proportion of man's diet and health. Despite of having many benefits, eating fish can be dangerous for instance the existence of nonorganic material, especially heavy metals, in some fish is dangerous. There are numerous fish breeding pools across the Lorestan province of Iran and the majority of the people living in these areas consume these kinds of fish, so, we were impelled to carry out a study to compare the nutrients and also heavy metals existent in freshwater fish and seawater fish available to the public across Khorramabad city of Iran. In this cross-sectional study, 9 samples of each five species of freshwater and sea water fish were purchased and their total protein, fat, omega 3, 6, and 9 fatty acids and also their heavy metals content including mercury, lead and cadmium of them were measured. There were no significant differences between mean protein content of the two types of fish. The amount of total fat and omega 3, 6 and 9 fatty acids of freshwater fish was higher than of seawater fish (P>0.001). The levels of cadmium in seawater fish was significantly higher than freshwater fish (P>0.001), and as for the level of mercury and lead, no significant difference was observed between the two types of freshwater fish and seawater fish. According to the results, we recommend that people can secure a part of their protein and unsaturated fatty acids need by consuming freshwater fish

Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis