Modeling Energy Futures Returns With Markov Regime Switching Model

Financial time series have a tendency of abruptly changing their behavior and maintain this behavior for several consecutive periods, and commodity futures returns are not an exception. This quality proposes that nonlinear models, as opposed to linear models, can more accurately describe returns and volatility. Markov regime switching models are able to match this behavior and have become a popular way to model financial time series. This study uses Markov regime switching model to describe the behavior of energy futures returns on a commodity level, because studies show that commodity futures are a heterogeneous asset class. The purpose of this thesis is twofold. First, determine how many regimes characterize individual energy commodities��� returns in different return frequencies. Second, study the characteristics of these regimes. We extent the previous studies on the subject in two ways: We allow for the possibility that the number of regimes may exceed two, as well as conduct the research on individual commodities rather than on commodity indices or subgroups of these indices. We use daily, weekly and monthly time series of Brent crude oil, WTI crude oil, natural gas, heating oil and gasoil futures returns over 1994���2014, where available, to carry out the study. We apply the likelihood ratio test to determine the sufficient number of regimes for each commodity and data frequency. Then the time series are modeled with Markov regime switching model to obtain the return distribution characteristics of each regime, as well as the transition probabilities of moving between regimes. The results for the number of regimes suggest that daily energy futures return series consist of three to six regimes, whereas weekly and monthly returns for all energy commodities display only two regimes. When the number of regimes exceeds two, there is a tendency for the time series of energy commodities to form groups of regimes. These groups are usually quite persistent as a whole because probability of a regime switch inside the group is high. However, individual regimes in these groups are not persistent and the process oscillates between these regimes frequently. Regimes that are not part of any group are generally persistent, but show low ergodic probability, i.e. rarely prevail in the market. This study also suggests that energy futures return series characterized with two regimes do not necessarily display persistent bull and bear regimes. In fact, for the majority of time series, bearish regime is considerably less persistent. Rahoituksen aikasarjoilla on taipumus arvaamattomasti muuttaa k��ytt��ytymist����n ja jatkaa t��t�� uutta k��ytt��ytymist�� useiden periodien ajan, eiv��tk�� hy��dykefutuurien tuotot tee t��h��n poikkeusta. T��m��n ominaisuuden johdosta lineaaristen mallien sijasta ep��lineaariset mallit pystyv��t tarkemmin kuvailemaan esimerkiksi tuottojen jakauman parametreja. Markov regiiminvaihtomallit pystyv��t vangitsemaan t��m��n ominaisuuden ja siksi niist�� on tullut suosittuja rahoituksen aikasarjojen mallintamisessa. T��m�� tutkimus k��ytt���� Markov regiiminvaihtomallia kuvaamaan yksitt��isten energiafutuurien tuottojen k��ytt��ytymist��, sill�� tutkimukset osoittavat hy��dykefutuurien olevan hyvin heterogeeninen omaisuusluokka. Tutkimuksen tarkoitus on selvitt����, kuinka monta regiimi�� tarvitaan kuvaamaan energiafutuurien tuottoja eri tuottofrekvensseill�� ja mitk�� ovat n��iden regiimien ominaisuudet. Aiempaa tutkimusta aiheesta laajennetaan m����ritt��m��ll�� regiimien lukum����r�� tilastotieteellisen testauksen menetelmin sek�� tutkimalla energiafutuureja yksitt��in; ei indeksi- tai alaindeksitasolla. Tutkimuksessa k��ytet����n p��iv��-, viikko- ja kuukausiaikasarjoja Brent-raaka��ljyn, WTI-raaka��ljyn, maakaasun, l��mmitys��ljyn ja poltto��ljyn tuotoista aikav��lilt�� 1994���2014, silt�� osin kuin aineistoa on saatavilla. Likelihood ratio -testin avulla estimoidaan kaikille aikasarjoille regiimien m����r��,jonka j��lkeen Markov regiiminvaihtomallia hy��dynt��en m����ritet����n yksitt��isten regiimientuottojakaumien ominaisuudet sek�� regiimien v��linen transitiomatriisi. Tulokset regiimien lukum����r��n osalta osoittavat, ett�� energiafutuurien p��iv��kohtaisten tuottojen aikasarjoissa regiimien lukum����r�� vaihtelee kolmen ja kuuden v��lill��. Viikko- ja kuukausituottojen kohdalla kaikkien energiafutuurien prosesseissa regiimien lukum����r�� on kaksi. Kun regiimej�� on enemm��n kuin kaksi, on prosessilla taipumus muodostaa regiimeist�� koostuvia ryhmi��. Prosessi pysyy ryhm��n sis��ll�� yleens�� pitk����n, koska todenn��k��isyys siirty�� ryhm����n kuuluvien regiimien v��lill�� on suuri. Yksitt��iset regiimit ryhm��n sis��ll�� eiv��t kuitenkaan ole kovin pysyvi��. N��in ollen prosessi vaihtelee ryhm��n sis��isten regiimien v��lill�� tiuhaan. Regiimit, jotka eiv��t kuulu ryhm����n, ovat yleens�� pysyvi��, mutta prosessi ajautuu niihin vain harvoin, sill�� todenn��k��isyys siirty�� muista regiimeist�� niihin on pieni. Tutkimuksen tulokset osoittavat my��s, ett�� prosesseissa, joita ohjaa kaksi regiimi��, n��m�� regiimit eiv��t v��ltt��m��tt�� ole pysyv��t bull- ja bear-markkinatilanteet. Tulokset osoittavat sen sijaan, ett�� bear-markkinatilanne on energiafutuureissa selv��sti v��hemm��n pysyv��.

Threat modeling a factory environment using Microsoft Security Development Lifecycle methodology

The number of security violations is increasing and a security breach could have irreversible impacts to business. There are several ways to improve organization security, but some of them may be difficult to comprehend. This thesis demystifies threat modeling as part of secure system development. Threat modeling enables developers to reveal previously undetected security issues from computer systems. It offers a structured approach for organizations to find and address threats against vulnerabilities. When implemented correctly threat modeling will reduce the amount of defects and malicious attempts against the target environment. In this thesis Microsoft Security Development Lifecycle (SDL) is introduced as an effective methodology for reducing defects in the target system. SDL is traditionally meant to be used in software development, principles can be however partially adapted to IT-infrastructure development. Microsoft threat modeling methodology is an important part of SDL and it is utilized in this thesis to find threats from the Acme Corporation���s factory environment. Acme Corporation is used as a pseudonym for a company providing high-technology consumer electronics. Target for threat modeling is the IT-infrastructure of factory���s manufacturing execution system. Microsoft threat modeling methodology utilizes STRIDE ���mnemonic and data flow diagrams to find threats. Threat modeling in this thesis returned results that were important for the organization. Acme Corporation now has more comprehensive understanding concerning IT-infrastructure of the manufacturing execution system. On top of vulnerability related results threat modeling provided coherent views of the target system. Subject matter experts from different areas can now agree upon functions and dependencies of the target system. Threat modeling was recognized as a useful activity for improving security.

A software engineering approach to distributed systems for composite modeling

The goal of this thesis is to define and validate a software engineering approach for the development of a distributed system for the modeling of composite materials, based on the analysis of various existing software development methods. We reviewed the main features of: (1) software engineering methodologies; (2) distributed system characteristics and their effect on software development; (3) composite materials modeling activities and the requirements for the software development. Using the design science as a research methodology, the distributed system for creating models of composite materials is created and evaluated. Empirical experiments which we conducted showed good convergence of modeled and real processes. During the study, we paid attention to the matter of complexity and importance of distributed system and a deep understanding of modern software engineering methods and tools.

Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Business modeling in turbulent environments

This study presents an agile tool set for the business modeling in companies, entering the turbulent environment. The study���s aim is to explore business modeling techniques and their tooling by utilizing a case study of a Finnish media monitoring company, expanding to the Russian market. This work proposes a tailored ���two-approach��� of business modeling development that analyzes both the past and future conditions of two key factors of business modeling for companies ��� internal and external environments. The study explores a case company by investigating the benefits and disadvantages of firm���s present business modeling tools, developing a new tooling and applying it to the case company. Among primary data utilization, such as interviews with media monitoring industry analysts and representatives of the competing companies, and academic experts, study leans up on the comprehensive analysis of Russian media monitoring niche and its players. This study benefits the business modeling research area and combines traditional analysis tools, such as market, PESTLE and competitor analyses, in a systemic manner, with the business modeling techniques. This transformation proceeds through applying of the integrated scenario, heat map and critical design issues��� analyses in the societal, industrial and competitive context of turbulent environments. The practical outcome of this approach is the development of agile business modeling tool set, customizable by company���s requirements.

Transient and Pulsed Electron Paramagnetic Resonance Spectroscopy on Type I Photosynthetic Reaction Centers

Two time-resolved EPR techniques, have been used to study the light induced electron transfer(ET) in Type I photosynthetic reaction centers(RCs). First, pulsed EPR was used to compare PsaA-M688H and PsaB-M668H mutants of Chlamydomonas reinhardtii and Synechosystis sp. PCC 6803.The out-of-phase echo modulation curves combined with other EPR and optical data show that the effect of the mutations is species dependent. Second, transient and pulsed EPR data are presented which show that PsaA-A660N and PsaB-A640N mutations in C. reinhardtii alter the relative quantum yield of ET in the A- and B-branches of PS I. Third, transient EPR studies on RCs from Heliobacillus mobilis that have been exposed to oxygen show partial inhibition of ET. In the RCs in which ET still occurs, the ET kinetics and EPR spectra show evidence of oxidation of some but not all of the, BChl g and BChl g' to Chl a.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Modeling short-range ballistic missile defense and Israel's Iron Dome system

This paper develops a model of short-range ballistic missile defense and uses it to study the performance of Israel���s Iron Dome system. The deterministic base model allows for inaccurate missiles, unsuccessful interceptions, and civil defense. Model enhancements consider the trade-offs in attacking the interception system, the difficulties faced by militants in assembling large salvos, and the effects of imperfect missile classification by the defender. A stochastic model is also developed. Analysis shows that system performance can be highly sensitive to the missile salvo size, and that systems with higher interception rates are more ���fragile��� when overloaded. The model is calibrated using publically available data about Iron Dome���s use during Operation Pillar of Defense in November 2012. If the systems performed as claimed, they saved Israel an estimated 1778 casualties and $80 million in property damage, and thereby made preemptive strikes on Gaza about 8 times less valuable to Israel. Gaza militants could have inflicted far more damage by grouping their rockets into large salvos, but this may have been difficult given Israel���s suppression efforts. Counter-battery fire by the militants is unlikely to be worthwhile unless they can obtain much more accurate missiles.

An Eigenfunction Approach for Volatility Modeling.

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Affiliation: Institut de recherche en immunologie et en canc��rologie, Universit�� de Montr��al