A particle filter approach to parameter estimation in stochastic volatility models with jumps for crude oil market

L'oggetto di questa tesi è l'analisi del mercato del greggio WTI, i cui prezzi futures sono quotati sul mercato di New York (NYMEX). Lo scopo della ricerca è duplice: innanzitutto, proporre un modello appropriato per la descrizione della dinamica del WTI; quindi, sviluppare un algoritmo adatto per ottenere risultati di inferenza statistica, sia sotto la misura storica che risk-neutral, per i modelli trattati.This dissertation deals with the inference in a crude oil market, the West Texas Intermediate (WTI) crude oil, whose futures are quoted on the New York Mercantile Exchange Market (NYMEX). The purpose of the research underlying this dissertation is twofold: first, we were looking for an appropriate model for the WTI crude oil market. Second, we tried to develop a new technique for parameter estimation suitable for the models considered, allowing to get inference results when the price dynamics is described both under the historical and the risk-neutral probability measures.DIPARTIMENTO DI MATEMATICA25BARUCCI, EMILIOLUCCHETTI, ROBERT

A particle filtering approach to oil futures price calibration and forecasting

In this paper we propose a model for oil price dynamics for which we provide an estimation method based on a recent technique named Particle Filtering. The model we are going to introduce extends a previous model proposed by Liu and Tang (2011), including a non constant volatility and jumps in the spot price dynamics. The estimation methodology we are going to adopt is similar to the Particle Markov Chain Monte Carlo (PMCMC) method proposed by Andrieu et al. (2010), and both spot and futures quotation data related to WTI (West Texas Intermediate) are analyzed in order to perform our inference procedure. The models considered allow to obtain explicit expressions for futures prices as functions of the model parameters and this in turn makes the calibration procedure fast and accurate at the same time. A comparison between the model considered and the model proposed by Liu and Tang is provided in terms of prices forecasting ability. The inference analysis shows that the introduction of both stochastic volatility and jumps improve significantly the ability of the model in capturing the oil price dynamic features

A New Meta-Heuristic Multi-Objective Approach For Optimal Dispatch of Dispersed and Renewable Generating Units in Power Distribution Systems

The application of stochastic methods in engineering research and optimization has been increasing over the past few decades. Ant Colony Optimization, in particular, has been attracting growing attention as a promising approach both in discrete and continuous domains. The present work proposes a multi-objective Ant Colony Optimization for continuous domains showing good convergence properties and uniform coverage of the non-dominated front. These properties have been proved both with mathematical test functions and with a complex real world problem. Besides the second part of the chapter presents the application of the new algorithm to the problem of optimal dispatch of dispersed power generation units in modern electrical distribution networks. The issue is intrinsically multi-objective and the objectives are calculated based on the solution of the power load flow problem. The performances of the algorithm have been compared to those of the Non-dominated Sorting Genetic Algorithm II on all applications. The chapter is organized as follows, in the introductory part, the relevance of multi-objective optimization problems to modern power distribution operation is outlined. Then the Non-dominated Sorting Genetic Algorithm II is described as well as the proposed Multi-objective Ant Colony Optimization algorithm in details. Both approaches are compared on a test suite of mathematical test functions. Finally, an interesting case study in the field of modern electrical distribution systems management is proposed.</jats:p

A new meta-heuristic multi-objective approach for optimal dispatch of dispersed and renewable generating units in power distribution systems

The application of stochastic methods in engineering research and optimization has been increasing over the past few decades. Ant Colony Optimization, in particular, has been attracting growing attention as a promising approach both in discrete and continuous domains. The present work proposes a multi-objective Ant Colony Optimization for continuous domains showing good convergence properties and uniform coverage of the non-dominated front. These properties have been proved both with mathematical test functions and with a complex real world problem. Besides the second part of the chapter presents the application of the new algorithm to the problem of optimal dispatch of dispersed power generation units in modern electrical distribution networks. The issue is intrinsically multi-objective and the objectives are calculated based on the solution of the power load flow problem. The performances of the algorithm have been compared to those of the Non-dominated Sorting Genetic Algorithm II on all applications. The chapter is organized as follows, in the introductory part, the relevance of multi-objective optimization problems to modern power distribution operation is outlined. Then the Non-dominated Sorting Genetic Algorithm II is described as well as the proposed Multi-objective Ant Colony Optimization algorithm in details. Both approaches are compared on a test suite of mathematical test functions. Finally, an interesting case study in the field of modern electrical distribution systems management is proposed

Metabolomics Suggests That Soil Inoculation with Arbuscular Mycorrhizal Fungi Decreased Free Amino Acid Content in Roots of Durum Wheat Grown under N-Limited, P-Rich Field Conditions.

Arbuscular mycorrhizal fungi (AMF) have a major impact on plant nutrition, defence against pathogens, a plant's reaction to stressful environments, soil fertility, and a plant's relationship with other microorganisms. Such effects imply a broad reprogramming of the plant's metabolic activity. However, little information is available regarding the role of AMF and their relation to other soil plant growth-promoting microorganisms in the plant metabolome, especially under realistic field conditions. In the present experiment, we evaluated the effects of inoculation with AMF, either alone or in combination with plant growth-promoting rhizobacteria (PGPR), on the metabolome and changes in metabolic pathways in the roots of durum wheat (Triticum durum Desf.) grown under N-limited agronomic conditions in a P-rich environment. These two treatments were compared to infection by the natural AMF population (NAT). Soil inoculation with AMF almost doubled wheat root colonization by AMF and decreased the root concentrations of most compounds in all metabolic pathways, especially amino acids (AA) and saturated fatty acids, whereas inoculation with AMF+PGPR increased the concentrations of such compounds compared to inoculation with AMF alone. Enrichment metabolomics analyses showed that AA metabolic pathways were mostly changed by the treatments, with reduced amination activity in roots most likely due to a shift from the biosynthesis of common AA to γ-amino butyric acid. The root metabolome differed between AMF and NAT but not AMF+PGPR and AMF or NAT. Because the PGPR used were potent mineralisers, and AMF can retain most nitrogen (N) taken as organic compounds for their own growth, it is likely that this result was due to an increased concentration of mineral N in soil inoculated with AMF+PGPR compared to AMF alone

Metabolomic Analysis Of Durum Wheat Roots In Response to Arbuscular Mycorrhizal Fungi Inoculation in Field Conditions

Arbuscular mycorrhizal (AM) fungi are commonly occurring fungi that live in an obligate symbiotic status with the majority of land plants. The objective of the present work was to gain insight into the root metabolism changes (primary and secondary metabolism) of durum wheat in response to solely AM fungi inoculation or to combination of AM fungi with plant growth promoting rhizobacteria (PGPR) (Bacillus spp.). Field trial was performed in 2010–2011 in a typical semi-arid Mediterranean area (inner land of Sicily) in absence of fertilization. The untarget metabolomics analysis using the Agilent GC–quadrupole MS identified metabolites playing a key role in symbiosis as well as in root physiology during plant-microbe interactions in field conditions. Aminoacids was the category of compounds most affected by microrganism inoculation. Six of the 20 identified aminoacids significantly differed among the three treatments (natural, AM fungi, AM fungi + PGPR inoculation). They were tyrosine, tryptophan, threonine, ornithine, lysine, glutamine. In the AM fungi + PGPR treatment only tyrosine and lysine were lower than natural conditions. Oxoproline and glutamine were higher in AM fungi + PGPR treatment in comparison to solely AM fungi treatment. Six out of the 48 analysed carbohydrates (including pentitol, fructose, and cellobiose) significantly varied among the treatments as well as 7 out of the 22 analysed organic acids. Four among them were highly significantly altered: malic acid, isocitric acid, gluconic acid, arachidic acid. The treatment AM fungi + PGPR significantly downregulated only the aconitic acid. Inoculation with AM fungi as well as AM fungi + PGPR treatment severely modified the content of some key fatty acids and diterpene alcohols such as palmitoleic acid, isoheptadecanoic acid, ergosterol and phytol. Also myristic acid and monopalmitin significantly varied among treatment even though at a lower extent. Fifty one compounds belonging to the secondary metabolism were analysed: 41 were lipids and 11 were classified as other metabolites. Overall both AM fungi and AM fungi + PGPR treatments did not drastically alter lipid pathways since only four metabolites significantly varied. A principal component analysis highlighted which metabolites mostly contributed to the variability among the three treatments. The physiological consequences of metabolic changes induced by mycorrhyzal in both presence/absence of plant growth promoting bacteria were discussed

CDA run using biological group means of standardised data from identified GC peaks as vectors.

<p>The percentage of the total variance explained by each canonical axis is shown in parentheses. NAT, blue circles; AMF, red triangles; AMF+PGPR, green squares. Please note that CDA vectors do not represent perpendicular directions through the space of the original variables. Fatty acids vectors include both fatty acids and their esters.</p