<p>Abstract</p><p>Business, or business decisions always involves an endangerment from the company’s aspect, but when it comes to business with foreign countries the risk profile looks quite different from the profile the company has when its doing business inside the country. Therefore, it is extremely important for an export company or import company to carefully consider the risks that are enclosed with the deal, before he makes the decision to enter a deal. One of the most important risks a company have to considerate when doing business with foreign countries is the currency risk. A company is exposed to currency risk when either long-term or short-term changes in the exchange rate may affect the result of the company, competitive position and growth. Those companies that are exposed to currency risks should take such action that will minimise these risks and thus avoid negative effects on the company’s result and growth. In practise, this is filled with both complexity and difficulties. For example, there are both internal and external restrictions that limits the company’s possibilities to effectively manage currency risks.</p><p>The Swedish export has the past years had a significant increase. This increase also means that the Swedish companies have to, even more, consider their chose of currency risk management method, because of the more exposure to currency risks. The purpose of this essay is to describe which currency risk management methods and derivative securities large Swedish export companies use in practise when doing business with foreign countries, and also to examine the reason to the companies choice of derivative security.</p><p>To answer the purpose with the essay there has been done a qualitative survey by personal interviews with respondents from six of Sweden’s largest export companies, considering the company’s turnover. The theories that have been used consider currency risks and currency risk management, and also the aspects that can affect a company’s choice of derivative security.</p><p>The conclusions that have been drawn after analysing the empirical material of the survey, are that all companies principally manage their currency risk exposure with currency forwards. The majority also uses currency swaps to a grate extent, while currency options are used within limits. The reason that the companies mostly use forwards is mainly that they bring a much smaller expense to the company, comparing to for example currency options.</p>

Abstract Business, or business decisions always involves an endangerment from the company’s aspect, but when it comes to business with foreign countries the risk profile looks quite different from the profile the company has when its doing business inside the country. Therefore, it is extremely important for an export company or import company to carefully consider the risks that are enclosed with the deal, before he makes the decision to enter a deal. One of the most important risks a company have to considerate when doing business with foreign countries is the currency risk. A company is exposed to currency risk when either long-term or short-term changes in the exchange rate may affect the result of the company, competitive position and growth. Those companies that are exposed to currency risks should take such action that will minimise these risks and thus avoid negative effects on the company’s result and growth. In practise, this is filled with both complexity and difficulties. For example, there are both internal and external restrictions that limits the company’s possibilities to effectively manage currency risks. The Swedish export has the past years had a significant increase. This increase also means that the Swedish companies have to, even more, consider their chose of currency risk management method, because of the more exposure to currency risks. The purpose of this essay is to describe which currency risk management methods and derivative securities large Swedish export companies use in practise when doing business with foreign countries, and also to examine the reason to the companies choice of derivative security. To answer the purpose with the essay there has been done a qualitative survey by personal interviews with respondents from six of Sweden’s largest export companies, considering the company’s turnover. The theories that have been used consider currency risks and currency risk management, and also the aspects that can affect a company’s choice of derivative security. The conclusions that have been drawn after analysing the empirical material of the survey, are that all companies principally manage their currency risk exposure with currency forwards. The majority also uses currency swaps to a grate extent, while currency options are used within limits. The reason that the companies mostly use forwards is mainly that they bring a much smaller expense to the company, comparing to for example currency options.

Machine learning models has gained traction as an effective tool for short-term electricity price forecasting, namely day ahead and hourly price forecasting. Efficient and accurate forecasting is crucial for demand and capacity planning to ensure stability and optimal use of resources. This project applies two proven machine learning models, LSTM and TCN, to electricity futures contracts in the Swedish pricing areas SE1 and SE3. Future contracts are used to secure the price of electricity in the future. A multivariate time series of fundamental data that correlates with electricity prices is used as input for the forecasting. Fur- thermore, a portfolio approach for hedging is evaluated based on the predictive performance of the models. The forecasting accuracy of the multivariate TCN model outperform the LSTM model. The optimal hedging strategy based on the TCN model indicated potential cost savings of 1.43% compared to a benchmark method. / Maskininlärnings modeller har vunnit mark som effektiva verktyg för att prognosticera kortsiktiga elpriser, för dagen före och timpriser. Effektiv och korrekt prognosticering är viktigt för att skatta behovs- och kapacitetsplanering för optimal resursanvändning. Det här projektet applicerar två välbeprövade modeller, LSTM och TCN, för att prognosticera terminskontrakt i de två svenska pris- områdena SE1 och SE3. Terminskontrakt används för att säkra elpriser i framtiden. En tidsserie, med flera variabler av fundamental data som korrelerar med elpriser, används för att prognosticera elpriser. Vidare utvärderas en portfölj approach för prissäkring baserat på prognoserna från modellerna. TCN modellen gav högre noggrannhet än LSTM modellen. Optimal prissäkringsstrategi baserad på TCN modeller resulterade i 1.43% lägre elpriser jämfört med bench- marks.

Neural networks are powerful tools for modelling complex non-linear mappings, but they often suffer from overfitting and provide no measures of uncertainty in their predictions. Bayesian techniques are proposed as a remedy to these problems, as these both regularize and provide an inherent measure of uncertainty from their posterior predictive distributions. By quantifying predictive uncertainty, we attempt to improve a systematic trading strategy by scaling positions with uncertainty. Exact Bayesian inference is often impossible, and approximate techniques must be used. For this task, this thesis compares dropout, variational inference and Markov chain Monte Carlo. We find that dropout and variational inference provide powerful regularization techniques, but their predictive uncertainties cannot improve a systematic trading strategy. Markov chain Monte Carlo provides powerful regularization as well as promising estimates of predictive uncertainty that are able to improve a systematic trading strategy. However, Markov chain Monte Carlo suffers from an extreme computational cost in the high-dimensional setting of neural networks. / Neurala nätverk är kraftfulla verktyg för att modellera komplexa icke-linjära avbildningar, men de lider ofta av överanpassning och tillhandahåller inga mått på osäkerhet i deras prediktioner. Bayesianska tekniker har föreslagits för att råda bot på dessa problem, eftersom att de både har en regulariserande effekt, samt har ett inneboende mått på osäkerhet genom den prediktiva posteriora fördelningen. Genom att kvantifiera prediktiv osäkerhet försöker vi förbättra en systematisk tradingstrategi genom att skala modellens positioner med den skattade osäkerheten. Exakt Bayesiansk inferens är oftast omöjligt, och approximativa metoder måste användas. För detta ändamål jämför detta examensarbete dropout, variational inference och Markov chain Monte Carlo. Resultaten indikerar att både dropout och variational inference är kraftfulla regulariseringstekniker, men att deras prediktiva osäkerheter inte kan användas för att förbättra en systematisk tradingstrategi. Markov chain Monte Carlo ger en kraftfull regulariserande effekt, samt lovande skattningar av osäkerhet som kan användas för att förbättra en systematisk tradingstrategi. Dock lider Markov chain Monte Carlo av en enorm beräkningsmässig komplexitet i ett så högdimensionellt problem som neurala nätverk.