Strategisten reaalioptioiden mallintaminen Monte Carlo -simulointimenetelmällä, case Voest Alpine Stahl Ag

Tutkielman päätavoitteena oli selvittää, miten Monte Carlo –simulointi soveltuu strategisten reaalioptioiden arvonmääritykseen. Tutkielman teoriaosuudessa käytiin läpi reaalioptioteoriaa ja Monte Carlo –simulointimenetelmää toiminta-analyyttisella tutkimusotteella. Tuloksena todettiin, että simulointimenetelmää on reaalioptioiden yhteydessä yleensä käytetty, kun muu menetelmä ei ole ollut mahdollinen. Tutkielman pääpaino on tapaustutkimukseen pohjautuvassa empiriaosuudessa, jossa rakennettiin päätöksentekometodologista tutkimusotetta seuraten simulointimalli, jolla tutkittiin Voest Alpine Stahl Ag:n vaihtoehtoisten hinnoittelustrategioiden taloudellista vaikutusta. Mallin rakentaminen perustui yrityksen tilinpäätösaineistoon. Havaittiin, ettei yritys ole valitsemansa strategian vuoksi juurikaan menettänyt tuottoja, mutta toisaalta pelkkä tilinpäätösaineisto ei riitä kovin luotettavaan tarkasteluun. Vuosikertomusten antaman tiedon pohjalta analysoitiin lisäksi yrityksen toiminnassa havaittuja reaalioptioita. Monte Carlo –simulointimenetelmä sopii reaalioptioiden arvonmääritykseen, mutta kriittisiä tekijöitä ovat mallin rakentaminen ja lähtötietojen oikeellisuus. Numeerisen mallin rinnalla on siksi aiheellista suorittaa myös laadullista reaalioptioanalyysia.

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Monte Carlo -reaktorifysiikkalaskennan ja laskennallisen virtausmekaniikan kytkentä kuulakekoreaktorissa

Monte Carlo -reaktorifysiikkakoodit nykyisin käytettävissä olevilla laskentatehoilla tarjoavat mielenkiintoisen tavan reaktorifysiikan ongelmien ratkaisuun. Neljännen sukupolven ydinreaktoreissa käytettävät uudet rakenteet ja materiaalit ovat haasteellisia nykyisiin reaktoreihin suunnitelluille laskentaohjelmille. Tässä työssä Monte Carlo -reaktorifysiikkakoodi ja CFD-koodi yhdistetään kytkettyyn laskentaan kuulakekoreaktorissa, joka on yksi korkealämpötilareaktorityyppi. Työssä käytetty lähestymistapa on uutta maailmankin mittapuussa ajateltuna.

Monte Carlo Hypothesis Testing with The Sharpe Ratio

The purpose of this master thesis was to perform simulations that involve use of random number while testing hypotheses especially on two samples populations being compared weather by their means, variances or Sharpe ratios. Specifically, we simulated some well known distributions by Matlab and check out the accuracy of an hypothesis testing. Furthermore, we went deeper and check what could happen once the bootstrapping method as described by Effrons is applied on the simulated data. In addition to that, one well known RobustSharpe hypothesis testing stated in the paper of Ledoit and Wolf was applied to measure the statistical significance performance between two investment founds basing on testing weather there is a statistically significant difference between their Sharpe Ratios or not. We collected many literatures about our topic and perform by Matlab many simulated random numbers as possible to put out our purpose; As results we come out with a good understanding that testing are not always accurate; for instance while testing weather two normal distributed random vectors come from the same normal distribution. The Jacque-Berra test for normality showed that for the normal random vector r1 and r2, only 94,7% and 95,7% respectively are coming from normal distribution in contrast 5,3% and 4,3% failed to shown the truth already known; but when we introduce the bootstrapping methods by Effrons while estimating pvalues where the hypothesis decision is based, the accuracy of the test was 100% successful. From the above results the reports showed that bootstrapping methods while testing or estimating some statistics should always considered because at most cases the outcome are accurate and errors are minimized in the computation. Also the RobustSharpe test which is known to use one of the bootstrapping methods, studentised one, were applied first on different simulated data including distribution of many kind and different shape secondly, on real data, Hedge and Mutual funds. The test performed quite well to agree with the existence of statistical significance difference between their Sharpe ratios as described in the paper of Ledoit andWolf.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Hintariskienhallinta pitkäaikaisissa urakoissa

Pitkäaikaisten rakennusurakoiden tarjouslaskennassa on ennakoitava hintojen muutoksia useiden vuosien päähän, kun tarjoukset on tehtävä kiinteillä hinnoilla. Kustannusten ennakointi ja hintariskienhallinta on kriittinen tekijä rakennusalan yrityksen kilpailukyvylle. Tämän tutkielman tavoitteena on kehittää YIT Rakennus Oy:n Infrapalveluille toimintamalli ja työkalu, joiden avulla hintariskejä voidaan hallita tarjouslaskennassa sekä hankintatoimessa. Ratkaisuksi kehitettiin kustannusten ennakointi -malli, jossa panosryhmien hintojen kehitystä ennustetaan asiantuntijaryhmissä säännöllisesti. Kustannusten ennakointi -mallin käyttöönotto vaatii ennustettavien panosryhmien määrittelyä. Lisäksi on nimettävä asiantuntijaryhmä sekä valittava aikajänne, jolle ennuste tehdään. Ennusteisiin sisältyvä epävarmuus saadaan esiin Monte Carlo simulaatiolla, ja urakan hintariskiä voidaan siten arvioida todennäköisyysjakaumien ja herkkyysanalyysin avulla. Valmiita ennusteita hyödynnetään tarjouslaskennassa sekä hankintatoimessa taktiikoiden ja strategioiden valinnassa.

Hankintaprosessin suorituskyvyn arviointi ja kehittäminen

Tämän työn tarkoituksena oli tarkastella kohdeorganisaation hankintaprosessin suorituskykyä. Tutkimuksen päämääränä oli tuottaa yritykselle sellaista tietoa ja arviointikriteerejä, joiden avulla yritys voi kehittää valmiuksiaan oman suorituskyvyn tehokkaampaan arviointiin tulevaisuudessa. Tutkielma tehtiin Skanska Oy:n osto-osastolle Helsinkiin. Tutkimuksen kohteeksi valittiin kausisopimusten hankintaprosessi epäsuorissa hankinnoissa, kotimaisilla markkinoilla. Keskitetyn kausisopimusten hankintaprosessin tarkoituksena on tuottaa yritykselle kilpailukykyisiä sopimuksia sekä saavuttaa prosessin parempi hallinta ja läpinäkyvyys. Tietoa tutkimuksen kohteena olevasta prosessista kerättiin haastatteluilla ja keskustelutuokioilla sekä yrityksen dokumenteista. Aineiston keräämisen kautta pyrittiin saamaan syvempi kuva prosessin toiminnasta, sen ongelmakohdista sekä niiden syistä ja seurauksista. Toisen tarkastelunäkökulman prosessin arvioinnille tarjosi läpimenoajan mittaaminen. Saatua aineistoa luokiteltiin vika- ja vaikutusanalyysiin pohjautuvalla mallilla sekä Monte Carlo – simulaatiomenetelmään perustuvalla ohjelmalla. Työn tuloksena esitetään tutkimuksen kohteena olevalle prosessille sopivia kehitystoimenpiteitä sekä suositeltavia prosessin mittaamisalueita.

Hakkuutähteestä valmistetun pyrolyysiöljyn elinkaaren kasvihuonekaasupäästöt

Kasvihuonekaasupäästöjen vähentämiseksi ja uusiutuvan energian käytön lisäämiseksi EU:ssa on säädetty direktiivi uusiutuvan energian käytön edistämisestä(RES-direktiivi). Direktiivissä annetaan ohjeet biopolttoaineiden ja bionesteiden kasvihuonekaasuvaikutusten laskemiseen. Tämän työn tarkoituksena oli selvittää, täyttääkö hakkuutähteistä valmistettu pyrolyysiöljy RES-direktiivin asettamat määrälliset vaatimukset kasvihuonekaasujen päästövähennykselle silloin, kun pyrolyysiöljyllä korvataan raskasta polttoöljyä lämmöntuotannossa. Laskenta suorittiin direktiivin ohjeiden mukaan. Lisäksi työssä pohdittiin laskentamenetelmän soveltuvuutta pyrolyysiöljyn ilmastovaikutusten arviointiin yleisesti. Laskennassa huomioitiin raaka-aineen tuotannosta, jalostuksesta sekä kuljetuksesta ja jakelusta aiheutuvat kasvihuonekaasupäästöt. Päästöt laskettiin ensin oletusarvoilla, jonka jälkeen suoritettiin todennäköisyyspohjainen herkkyystarkastelu valituille parametreille. Herkkyystarkastelun tuloksista huomattiin, että päästövähennys riippuu pääasiassa kahdesta tekijästä: maaperän hiilitaseen muutoksesta aiheutuvista päästöistä ja pyrolyysiprosessin tarvitseman lämmön tuotantoon käytetyistä polttoaineista. Eniten tuloksiin vaikutti kuitenkin se, oletettiinko pyrolysaattori ja kattila tarkastelussa erillisiksi yksiköiksi (tapaus 1) vai kokonaisuudeksi (tapaus 2). Tulosten perusteella näyttää siltä, että pyrolyysiöljyn käyttö lämmöntuotannossa raskaan polttoöljyn sijasta johtaa päästövähennyksiin. Saatuja tuloksia ei kuitenkaan voida pitää ennusteina pyrolyysiöljyn todellisista ilmastovaikutuksista, koska elinkaariarviointiin liittyy monia epävarmuuksia. RES-direktiivin laskentaohjeet esimerkiksi järjestelmärajausten muodostamisesta ja päästöjen kohdentamisesta ovat epätarkat. Tästä syystä direktiiviä on mahdollista tulkita usealla eri tavalla, jolloin toisistaan poikkeavat tulokset voivat silti olla kaikki direktiivin mukaisesti laskettuja. Jotta liika tulkinnanvaraisuus ei aiheuttaisi ongelmia, olisi RES-direktiivin laskentaohjetta hyvä tarkentaa.

Value of Information Logistics Integration in Digital Business Ecosystem

Digital business ecosystems (DBE) are becoming an increasingly popular concept for modelling and building distributed systems in heterogeneous, decentralized and open environments. Information- and communication technology (ICT) enabled business solutions have created an opportunity for automated business relations and transactions. The deployment of ICT in business-to-business (B2B) integration seeks to improve competitiveness by establishing real-time information and offering better information visibility to business ecosystem actors. The products, components and raw material flows in supply chains are traditionally studied in logistics research. In this study, we expand the research to cover the processes parallel to the service and information flows as information logistics integration. In this thesis, we show how better integration and automation of information flows enhance the speed of processes and, thus, provide cost savings and other benefits for organizations. Investments in DBE are intended to add value through business automation and are key decisions in building up information logistics integration. Business solutions that build on automation are important sources of value in networks that promote and support business relations and transactions. Value is created through improved productivity and effectiveness when new, more efficient collaboration methods are discovered and integrated into DBE. Organizations, business networks and collaborations, even with competitors, form DBE in which information logistics integration has a significant role as a value driver. However, traditional economic and computing theories do not focus on digital business ecosystems as a separate form of organization, and they do not provide conceptual frameworks that can be used to explore digital business ecosystems as value drivers—combined internal management and external coordination mechanisms for information logistics integration are not the current practice of a company’s strategic process. In this thesis, we have developed and tested a framework to explore the digital business ecosystems developed and a coordination model for digital business ecosystem integration; moreover, we have analysed the value of information logistics integration. The research is based on a case study and on mixed methods, in which we use the Delphi method and Internetbased tools for idea generation and development. We conducted many interviews with key experts, which we recoded, transcribed and coded to find success factors. Qualitative analyses were based on a Monte Carlo simulation, which sought cost savings, and Real Option Valuation, which sought an optimal investment program for the ecosystem level. This study provides valuable knowledge regarding information logistics integration by utilizing a suitable business process information model for collaboration. An information model is based on the business process scenarios and on detailed transactions for the mapping and automation of product, service and information flows. The research results illustrate the current cap of understanding information logistics integration in a digital business ecosystem. Based on success factors, we were able to illustrate how specific coordination mechanisms related to network management and orchestration could be designed. We also pointed out the potential of information logistics integration in value creation. With the help of global standardization experts, we utilized the design of the core information model for B2B integration. We built this quantitative analysis by using the Monte Carlo-based simulation model and the Real Option Value model. This research covers relevant new research disciplines, such as information logistics integration and digital business ecosystems, in which the current literature needs to be improved. This research was executed by high-level experts and managers responsible for global business network B2B integration. However, the research was dominated by one industry domain, and therefore a more comprehensive exploration should be undertaken to cover a larger population of business sectors. Based on this research, the new quantitative survey could provide new possibilities to examine information logistics integration in digital business ecosystems. The value activities indicate that further studies should continue, especially with regard to the collaboration issues on integration, focusing on a user-centric approach. We should better understand how real-time information supports customer value creation by imbedding the information into the lifetime value of products and services. The aim of this research was to build competitive advantage through B2B integration to support a real-time economy. For practitioners, this research created several tools and concepts to improve value activities, information logistics integration design and management and orchestration models. Based on the results, the companies were able to better understand the formulation of the digital business ecosystem and the importance of joint efforts in collaboration. However, the challenge of incorporating this new knowledge into strategic processes in a multi-stakeholder environment remains. This challenge has been noted, and new projects have been established in pursuit of a real-time economy.

Analyzing the Effects of the New Renewable Energy Policy in Russia on Investments into Wind, Solar and Small Hydro Power

This thesis presents an analysis of recently enacted Russian renewable energy policy based on capacity mechanism. Considering its novelty and poor coverage by academic literature, the aim of the thesis is to analyze capacity mechanism influence on investors’ decision-making process. The current research introduces a number of approaches to investment analysis. Firstly, classical financial model was built with Microsoft Excel® and crisp efficiency indicators such as net present value were determined. Secondly, sensitivity analysis was performed to understand different factors influence on project profitability. Thirdly, Datar-Mathews method was applied that by means of Monte Carlo simulation realized with Matlab Simulink®, disclosed all possible outcomes of investment project and enabled real option thinking. Fourthly, previous analysis was duplicated by fuzzy pay-off method with Microsoft Excel®. Finally, decision-making process under capacity mechanism was illustrated with decision tree. Capacity remuneration paid within 15 years is calculated individually for each RE project as variable annuity that guarantees a particular return on investment adjusted on changes in national interest rates. Analysis results indicate that capacity mechanism creates a real option to invest in renewable energy project by ensuring project profitability regardless of market conditions if project-internal factors are managed properly. The latter includes keeping capital expenditures within set limits, production performance higher than 75% of target indicators, and fulfilling localization requirement, implying producing equipment and services within the country. Occurrence of real option shapes decision-making process in the following way. Initially, investor should define appropriate location for a planned power plant where high production performance can be achieved, and lock in this location in case of competition. After, investor should wait until capital cost limit and localization requirement can be met, after that decision to invest can be made without any risk to project profitability. With respect to technology kind, investment into solar PV power plant is more attractive than into wind or small hydro power, since it has higher weighted net present value and lower standard deviation. However, it does not change decision-making strategy that remains the same for each technology type. Fuzzy pay-method proved its ability to disclose the same patterns of information as Monte Carlo simulation. Being effective in investment analysis under uncertainty and easy in use, it can be recommended as sufficient analytical tool to investors and researchers. Apart from described results, this thesis contributes to the academic literature by detailed description of capacity price calculation for renewable energy that was not available in English before. With respect to methodology novelty, such advanced approaches as Datar-Mathews method and fuzzy pay-off method are applied on the top of investment profitability model that incorporates capacity remuneration calculation as well. Comparison of effects of two different RE supporting schemes, namely Russian capacity mechanism and feed-in premium, contributes to policy comparative studies and exhibits useful inferences for researchers and policymakers. Limitations of this research are simplification of assumptions to country-average level that restricts our ability to analyze renewable energy investment region wise and existing limitation of the studying policy to the wholesale power market that leaves retail markets and remote areas without our attention, taking away medium and small investment into renewable energy from the research focus. Elimination of these limitations would allow creating the full picture of Russian renewable energy investment profile.

Value at Risk in Foreign Exchange Risk Management

This thesis examines the suitability of VaR in foreign exchange rate risk management from the perspective of a European investor. The suitability of four different VaR models is evaluated in respect to have insight if VaR is a valuable tool in managing foreign exchange rate risk. The models evaluated are historical method, historical bootstrap method, variance-covariance method and Monte Carlo simulation. The data evaluated are divided into emerging and developed market currencies to have more intriguing analysis. The foreign exchange rate data in this thesis is from 31st January 2000 to 30th April 2014. The results show that the previously mentioned VaR models performance in foreign exchange risk management is not to be considered as a single tool in foreign exchange rate risk management. The variance-covariance method and Monte Carlo simulation performs poorest in both currency portfolios. Both historical methods performed better but should also be considered as an additional tool along with other more sophisticated analysis tools. A comparative study of VaR estimates and forward prices is also included in the thesis. The study reveals that regardless of the expensive hedging cost of emerging market currencies the risk captured by VaR is more expensive and thus FX forward hedging is recommended

Activity-based life cycle costing model for cost estimation and monitoring – Case: manned and autonomous dry bulk carrier

Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.

Mechanical design and optics simulation of the illuminated Volvo Iron mark

Utilization of light and illumination systems in automotive industry for different purposes has been increased significantly in recent years. Volvo as one of the leading companies in manufacturing of luxury cars has found the great capacity in this area. The performance of such an illumination systems is one of the challenges that engineers in this industry are facing with. In this study an effort has been made to design a system to make the iron mark of Volvo being illuminated and the system is being evaluated by optics simulation in software using Ray optics method. At the end, results are assessed and some optimizations are carried out. Different kind of light guides, front side of the iron mark and some possible arrangement for LED also evaluated and different materials tested. The best combination from uniformity, color and amount of luminance aspect selected as a possible solution for this special project which can be used as a base for further studies in Volvo.