Deploying wind power to a larger extent is one solution to reduce negative environmental impacts of electric power supply. However, various challenges are connected with increasing wind power penetration levels. From the perspective of transmission system operators, this includes balancing of varying as well as - to some extent - uncertain generation levels. From the perspective of power generating companies, changes in the generation mix will affect the market's merit order and, hence, their profits. This thesis focuses on provision and use of flexibility in the Nordic electricity market. First, this thesis studies wind power variations and accuracy of wind power forecasts in Sweden using statistical methods. Even though today’s wind penetration levels are still low in Sweden, power systems and electricity markets have to cope with these characteristics of variations and forecast errors to a larger extent in future. Second, it investigates to which extent an increased exchange and use of flexibility that is available in the intraday time-frame could efficiently facilitate system balancing and whether this would also be profitable from the power generating companies' perspective. Here, a simulation model is developed that reflects important aspects of production planning and trading decisions in the intraday time-frame. In a first case study, it is shown that the benefits of internal rescheduling strongly depend on the costs to adjust production plans in the intraday time-frame as compared to real-time. In a second case study, it becomes evident that trading flexibility in the intraday time-frame can reduce the need for system balancing more efficiently than internal rescheduling within each balance responsible party. Motivated by the positive gains of intraday trading and the challenge of appropriately modelling continuous intraday markets, trading activity and price development on Elbas is investigated. The results provide insights into trading behaviour on a continuous intraday market and show that trading is not always in accordance to the power system's physical situation. To the extent to which better information and adaptations in the market design could improve the market participants' base for trading decisions, policy recommendations and further research questions areas suggested. / Att använda vindkraft i en större utsträckning är en möjlighet att minska elproduktionens negativa miljöpåverkan. Det finns dock också olika utmaningar med stora mängder vindkraft. Från ett systemperspektiv gäller det till exempel att hålla balansen mellan tillförsel och konsumtion av el. Från elproducenternas perspektiv bör vindkraftens påverkan på elmarknaden nämnas eftersom det påverka aktörernas vinster. Avhandlingen titta närmare in i hur man kan få tillgång till mer flexibilitet på produktionssidan. Avhandlingen består av tre delar. För det första undersöks variationer och prognosfel av vindkraft i Sverige med hjälp av statistiska metoder. Även om andel vindkraft hittills är låg i Sverige, behöver elsystemet och elmarknader i framtiden hantera samma egenskaper av själva variationer och prognosfel som idag men i en större utsträckning. För det andra undersöks hur den flexibiliteten som finns i tidshorisonten några timmar innan leveranstimmen kan utnyttjas för att integrera vindkraften på ett sätt som är både fördelaktigt från systemets och från aktörernas perspektiv. Undersökningen sker med hjälp av en simuleringsmodell som omfattar viktiga delar i produktionsplanering och intradayhandel. I en fallstudie uppvisas att vinster av intern omplanering är i högsta grad beroende på kostnadsskillnaden mellan omplanering några timmar innan leveranstimmen och anpassning av körscheman under själva leveranstimmen. Resultat av ytterligare en fallstudie uppvisar att det är betydligt billigare och mer effektivt att använda intradayhandel istället för intern omplanering för att utnyttja den befintliga flexibiliteten och för att reducera obalanser som systemoperatörer annars behöver ta hand om under leveranstimmen. Detta är en anledning till att undersöka handelsmönster på Elbas som är en intradaymarknad med kontinuerlig handel. En annan anledning till den här tredje delen är utmaningarna i att modellera kontinuerlig intradayhandel. Studien beskriver handelsaktiviteten på Elbas och hur priserna utvecklas under handelstiden. Ett resultat är att handeln inte alltid återspeglar den fysiska situationen i elsystemet. I den utsträckningen som ett snabbare informationsflöde och förändringar i marknadsdesignen kunde förbättrar aktörernas underlag för intradayhandel, föreslås förbättringar och öppna forskningsfrågor. / <p>QC 20150911</p> / Elektra 36141: Korttidsplanering av vatten-värmekraftsystem vid stora mängder vindkraft: System-perspektivet

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

This thesis has two main objectives. It attempts to describe the process of developing an intraday discretionary trading strategy based on technical analysis and to create through the process an intraday discretionary strategy for speculative trading of contracts for difference on the OTC market. The theoretical part of the thesis is divided into three chapters providing the necessary knowledge for creation of an intraday discretionary trading strategy in the practical part of the thesis. The emphasis is put on the description of different tools and methods technical traders usually use in their strategies. The development process of the strategy is divided into three parts which are Strategy Creation, Backtest and Optimization. These parts are further divided into smaller sections dealing with different issues of the strategy development process. The final outcome of the work is structured development process of discretionary trading strategies and also highly profitable intraday discretionary strategy for trading of Gold based contracts for difference.

This work is about trading futures markets. It defines rules of two different intraday strategies with the same basic trading idea on e-mini NASDAQ 100 market. First strategy is more mechanical, second is more discrete. These strategies are tested via backtest and papertrading. The results of tests are evaluated and based on that is chosen one strategy, which is then applied on live trading. Results from live trading are compared with the results of previous tests for a purpose finding a difference between them and identifying the causes of these differences.

This thesis deals with the possibilities of electronic stock trading of currency pairs. It analyzes the basic conditions and criteria which trader needs to meet to be able to participate on this market. This master’s thesis describes the use of technical indicators and fundamental messages through electronic trading platforms. Further thesis graphically compares the types of trading according to the time horizonts and then concludes suggestions and recommendations.

Electricity needs to be consumed when it is produced, making sure that supply closely meets demand at all times. To account for the rapidly changing operational status and the need for increasing the flexibility of power systems, financial instruments have been put in place creating markets where electricity is traded as a commodity across different time frames; from months or days to minutes before, or even after, planned delivery. In this work, the focus is placed on the short-term electricity markets and particularly on forecasting the intraday volume-weighted average price of the last three hours of trading of hourly power products. To this end, two state-of-the-art recurrent neural network architectures, namely the Temporal Fusion Transformer and the DeepAR network, are compared against well-established statistical models, such as the Linear Regression, ARX and SARIMAX models, with respect to their forecast accuracy on each of the 24 hourly delivery products. Two different experimental setups are applied, with one utilizing two input features drawn specifically from the findings of relevant literature and the other blindly exploiting all available streams of information in either their raw or aggregated form. All models are trained individually per hourly product per experimental setup to support a fair and decisive comparison, leading to 240 unique model instances being trained in total. Furthermore, the input feature importance is inferred by exploiting the inbuilt attention mechanism of the Temporal Fusion Transformer architecture. Finally, by using various realworld historical market data originating from the Nord Pool power exchange as well as from the Svenska Kraftnät, available up until the day of delivery, it is shown that the statistical models outperform both contemporary neural network architectures, with the latter suffering from the inability to generalize to elevated price levels—which are absent from the training dataset. / El måste förbrukas när den produceras, och se till att utbudet alltid motsvarar efterfrågan. För att ta hänsyn till den snabbt föränderliga operativa statusen och behovet av att öka flexibiliteten i kraftsystemen har finansiella instrument införts för att skapa marknader där el handlas som en vara över olika tidsramar; från månader eller dagar till minuter före, eller till och med efter, planerad leverans. I detta arbete läggs fokus på de kortsiktiga elmarknaderna och särskilt på att prognostisera det intradagsvolymvägda genomsnittspriset för de senaste tre timmarnas handel med timkraftprodukter. För detta ändamål jämförs två toppmoderna återkommande neurala nätverksarkitekturer, nämligen Temporal Fusion Transformer och DeepAR-nätverket, mot väletablerade statistiska modeller, såsom modellerna Linear Regression, ARX och SARIMAX, med avseende på deras prognosnoggrannhet för var och en av 24-timmarsleveransprodukterna. Två olika experimentella uppsättningar tillämpas, där den ena använder två indatafunktioner som hämtats specifikt från resultaten av relevant litteratur och den andra utnyttjar blint alla tillgängliga informationsströmmar i antingen deras råa eller aggregerade form. Alla modeller tränas individuellt per timprodukt per experimentuppställning för att stödja en rättvis och avgörande jämförelse, vilket leder till att 240 unika modellinstanser tränas totalt. Dessutom härleds ingångsfunktionens betydelse genom att utnyttja den inbyggda uppmärksamhetsmekanismen i Temporal Fusion Transformer-arkitekturen. Slutligen, genom att använda olika verkliga historiska marknadsdata från elbörsen Nord Pool såväl som från Svenska Kraftnät, tillgängliga fram till leveransdagen, visas att de statistiska modellerna överträffar både moderna neurala nätverksarkitekturer, med sistnämnda lider av oförmågan att generalisera till förhöjda prisnivåer — som saknas i utbildningsdataset.

本文以日內資料研究台灣證券交易所於2005年3月1日實施股價升降單位新制後，市場交易因子與股價報酬波動率的變化；延伸討論市場參與者對新訊息之反應，進而評估實施股價升降單位新制之成效。本文首先比較四種常用來衡量報酬波動率的方法，並從中挑選出最穩健的測度方式；接著藉此分析股價日報酬波動率與市場交易因子之間的關係；最後，由於日內股價報酬波動的軌跡呈現U型曲線，為突顯波動較劇烈之時段股價報酬波動率是否亦隨股價升降單位縮小而趨緩，故著眼交易日開盤後一小時及收盤前一小時，再次檢驗上述關係。實證結果支持股價升降單位縮小使實現波動率大幅降低且交易筆數密切影響股價報酬波動率，且不論在日資料與日內資料都呈現相似結論；並發現愈接近開、收盤的時間點，股價報酬波動率降低比例亦愈大，顯示升降單位新制達成政策目的。 / In this study, we address the impact of the tick size reduction on the Taiwan Stock Exchange on March 1, 2005. We propose to investigate the variations of trading activities and return volatility, discuss investors' behaviors to the new information and evaluate the tick size reduction by analyzing intraday data. First, we select the most robust volatility measure for our study from four commonly used ones. Second, we examine the relationship between daily return volatility and trading activities. Eventually, due to the commonly observed U-shaped pattern of intraday return volatility, we re-examine the intraday relation between return volatility and trading activities. Our empirical results based on the robust realized volatility confirm that both daily and intraday return volatility decline significantly after the tick size reduction, and number of trades is a prominent trading factor in explaining realized volatility. More interestingly, we observe that the percentage decrease in realized volatility is most pronounced for trading sessions near the beginning or the ending of each trading day. Overall, our empirical findings support the arguments for tick size reduction intended by policymakers.

Denna studie presenterar ett branschspecifikt perspektiv som tillägg till forskningen angående faktorer som påverkar underprissättning i börsintroduktioner. Mätningarna har ämnat att undersöka om det föreligger branscher som har avvikande förstadagsavkastning i förhållande till genomsnittet för samtliga branscher. Studien ämnade också att undersöka om avkastningsvariationerna för börsintroduktioner kan förklaras av att bolag tillhör olika branscher. Det underliggande argumentet för hypoteserna byggs från tidigare forskning som stödjer att bolag inom olika branscher står inför olika förutsättningar vilket kan påverka värderingen inför en börsintroduktion. Som tillägg i studiens huvudsakliga forskningsproblem har även en nyare typ av mätning tillämpats för en djupare analys av börsintroduktioner. Denna mätning ger indikationer till investerare om vilken del av börsintroduktioner som genererar mest avkastning. Studiens mätningar visade att det inte rådde signifikanta avkastningsavvikelser för någon bransch i förhållande till genomsnittsavkastningen för samtliga branscher. Mätningarna kunde inte heller påvisa att variationer i förstadagsavkastning förklaras av att bolag tillhör olika branscher då förklaringsgraden för dessa variabler var låga. Däremot påvisades signifikanta skillnader i genomsnittliga avkastningar mellan specifika branscher i samband med studiens regression. Detta indikerar att ytterligare undersökningar borde göras i syfte att undersöka genomsnittliga förstadagsavkastningar mellan specifika branscher. / This paper adds an industry-specific perspective to the research concerning factors that affect underpricing in Initial Public Offerings. The measurements are intended to investigate whether there are industries that have deviating initial returns in relation to the average for all industries. The study also intends to examine whether the return variations for Initial Public Offerings can be explained by the fact that companies belong to different industries. The underlying argument for the hypotheses is built from previous research that supports that companies in different industries are faced with different conditions when they are to be valued for their Initial Public Offerings. In addition to the study's main research problems, a more recent type of measurement has also been applied for a deeper analysis of IPOs. The more recent type of measurement gives indications to investors over which time around an Initial Public Offerings the most money is earned. The study's measurements showed no significant return deviations for any industry in relation to the average return for all industries. Nor could it be demonstrated that variations in the first day return are explained with that companies belong to different industries since the degree of explanation for these variables were low. However, significant differences were found in average returns between specific industries in connection with the study's regression. This indicates that further investigations should be carried out to examining the average first day yield between specific industries.