Value at Risk has over the last couple of decades become one of the most widely used measures of market risk. Several methods to compute this measure have been suggested. In this paper, we evaluate the use of the GARCH(1,1)-, EGARCH(1,1)- and the APARCH(1,1) model for estimation of this measure under the assumption that the conditional error distribution is normally-, t-, skewed t- and NIG-distributed respectively. For each model, the 95% and 99% one-day Value at Risk is computed using rolling out-of-sample forecasts for three equity indices. These forecasts are evaluated with Kupiec´s test for unconditional coverage test and Christoffersen’s test for conditional coverage. The results imply that the models generally perform well. The APARCH(1,1) model seems to be the most robust model. However, the GARCH(1,1) and the EGARCH(1,1) models also provide accurate predictions. The results indicate that the assumption of conditional distribution matters more for 99% than 95% Value at Risk. Generally, a leptokurtic distribution appears to be a sound choice for the conditional distribution.

This diploma thesis studies extreme value theory and its application in finan- cial risk management, when focusing on computation of well-known risk measure - Value at Risk (VaR). The first part of the thesis reviews theoretical background. In particular, it rigorously discusses the extreme value theory when emphasi- zing fundamentals theorems and their consequences followed by the summary of methods based on this theory, specifically, Block Maxima method, Hill met- hod and Peaks over Threshold method. Moreover, specific issues that may arise in such applications and ways how to deal with these problems are described. The second part of the thesis contains extensive empirical study, which together with theoretical foundings applies each of the examined method to real market data of the closing prices of Dow Jones Industrial Average stock index, stocks of JPMorgan and stock index Russell 2000 in order to compare methods based on extreme value theory together with the classic methodology RiskMetrics. 1

In this paper, we utilize White's Reality Check, White (2000), and Hansen's SPA test, Hansen (2004), to evaluate technical trading rules while quantifying the data-snooping bias. Secondly, we discuss the result with Probability of Backtest Overfitting framework, introduced by Bailey et al. (2015). Hence, the study presents a comprehensive test of momentum trading across the US futures markets from 2004 to 2016. The evidence indicates that technical trading rules have not been pro?table in the US futures markets after correcting for the data snooping bias.

This thesis is devoted to the technical analysis with the emphasis on design, testing and using of trading systems. Its objective is to find out whether it is possible for a trader to design and trade his own profitable trading system with widely accessible tools and methods. First part of the thesis is focused on the chart analysis and description of candlestick charts including their rate of profit success, all based on hands-on experience in a real market. It continues with a breakdown of most used methods based on moving averages. The second chapter fully describes main stage of trading system development and follows up with third chapter on practical application of the theoretical assumption on the real market conditions, i.e. to design a profitable trading system

A main subject of the presented master thesis is trading and investing in capital, commodities and foreign exchange markets over the world with support of technical analysis constructed by artificial intelligence. The thesis also produces step-by-step guide to stock and futures trading, building a successful trading system and gaining profits from invested capital.

This master’s thesis deals with the problems of a technical analysis and its use. The first part of thesis describes theoretical background of the technical analysis and basic concepts and principles of the currency market Forex. The second part is devoted to analyzing the current situation in the environment of currency market. The output of the thesis is a desktop application for the support of technical analysis. The design and development of the application is described in the last part of this thesis.

The aim of the diploma thesis Proposal of an automated trading system for a retail investor is to propose and create an automated trading system in the forex foreign exchange market environment. Basing on an analysis of the current scientific knowledge about the topic and greatly focusing on stability and profitability of the system, a functional automated trading system fit for a retail investor is proposed. A practical application of this system on a portfolio of selected currency pairs is included in the thesis.

In this thesis the performance of the quantile based CAV iaR models is evaluated and compared with GARCH models for predicting the Value at Risk. This is done by one step ahead out of sample prediction. The one step ahead out of sample prediction is done for the 500 observations at the end of the sample. To calculate the predictions a rolling forecast is used. This means that the sample that is used to do the one step ahead predictions is equally sized for all 500 predictions. Then tests are performed to evaluate the predictive power of the forecasts. The tests that are used to evaluate the predictions are: the dynamic quantile test, the Kupiec test and the Christoffersens test. The data that is used in the analysis are two stock indexes and one exchange rate index. What is concluded from the thesis is that the models perform good in general for the Stockholmsb ̈orsen data. For the First north data the 1% V aR produced too high risk predictions so the exceedance rate became too low. For the 5% V aR the predictions were more accurate. For the exchange rate data the predictions from the models were generally good as well.

Res-IRF is an energy-economy model of heating consumption in French dwellings developed at CIRED and calibrated against 2012. It is meant to project the evolution of the building stock and the heating demand by 2050 in response to socio-economic parameters, such as energy price and population, and public policies, such as thermal regulations or incentives for renovation. Res-IRF captures the relevant determinants of household decisions related to energy efficiency improvements and energy demand (sufficiency). The aim of the work presented in this report is to calibrate the model against a past year, to run it from this start date to 2012, and to compare the simulation results with observed data on this period. After an overview of the French residential sector in the last 40 years, this report aims at presenting the model and how it was calibrated against year 1984 and adjusted to the past situation of the building stock. Then, the results of a sensitivity analysis on key parameters of the model are compared to reality and it is discussed how the model can be improved to fit the data better. The main results show that the model accurately replicates the evolution of the building stock until 2012. However, the results do not fit well the data of repartition of heating fuels, especially for fuel oil and natural gas. This may be due to the structure of the model which allows fuel switch only for renovating dwellings; then it could miss possible fuel switches from fuel oil to natural gas without renovation due to the expansion of the natural gas network in France between 1980 and 2000. Furthermore, the actual unit consumption, which is a key output of the model, is well replicated by the model, with an error of 5 to 10%. / Res-IRF är en energi-ekonomi modell av värmebehovet i franska byggnader utvecklad av CIRED och kalibrerad mot data för 2012. Det är avsett att förutsäga utvecklingen för byggnadsbeståndet och värmebehovet fram till 2050 med utgångspunkt från socio-ekonomiska parametrar såsom energipriser och befolkningsmängd, politiska beslut som regleringar rörande uppvärmningssektorn och incitament för renoveringar. Res-IRF fångar upp de relevanta faktorer som påverkar hushållens beslut relaterade till förbättringar av energieffektiviteten och energibehoven. Målet med arbetet som presenteras i denna rapport är att kalibrera modellen mot ett redan passerat år, att köra modellen från startåret till 2012, och att jämföra simuleringsresultaten med verkliga observationer för denna period. Efter en översikt över den franska bostadssektorn de senaste 40 åren, följer i rapporten en presentation av modellen och hur den kalibrerades mot året 1984 och sedan anpassats till det dåvarande läget i byggnadsbeståndet. Därefter jämförs resultaten av en känslighetsanalys av nyckelparametrar i modellen med verkligt utfall och en diskussion följer om hur modellen kan förbättras för att bättre passa verkliga data. Huvudresultaten visar att modellen på ett korrekt sätt avbildar utvecklingen av byggnadsbeståndet fram till 2012. Däremot ger resultaten inte god överensstämmelse vad gäller fördelning av bränslen, speciellt inte fördelningen mellan olja och naturgas. Detta kan bero på modellens struktur, som tillåter bränslebyte bara vid renovering; därmed missar den bränslebyten som görs utan samtidig renovering, som tillkommit på grund av utbyggnaden av naturgasnäten i Frankrike mellan 1980 och 2000. Vidare visar modellen god överensstämmelse vad gäller energitillförsel per enhet, vilket är en nyckelparameter bland resultaten från modellen. Denna parameter predikteras med ett fel av 5 till 10%.

Within Central Counterparty Clearing, the Clearing House collects Initial Margin from its Clearing Members. The Initial Margin can be calculated in many ways, one of which is by applying the commonly used risk measure Value-at-Risk. However, Value-at-Risk has one major flaw, namely its inability to encapsulate Tail Risk. Due to this, there has for long been a desire to replace Value-at-Risk with Expected Shortfall, another risk measure that has shown to be much better suited to encapsulate Tail Risk. That said, Value-at-Risk is still used over Expected Shortfall, something which is mainly due to the fact that there is no consensus regarding how one should backtest Expected Shortfall. The goal of this thesis is to evaluate some of the most commonly proposed methods for backtesting Expected Shortfall. In doing this, several non-parametric backtests of Expected Shortfall are investigated using simulated data as well as market data from different types of securities. Moreover, this thesis aims to shed some light on the differences between Value-at-Risk and Expected Shortfall, highlighting why a change of risk measure is not as straightforward as one might believe. From the investigations of the thesis, several backtests are found to be sufficient for backtesting the Initial Margin with Expected Shortfall as the risk measure, the so called Minimally Biased Relative backtest showing the overall best performance of the looked at backtests. Further, the thesis visualizes how Value-at-Risk and Expected Shortfall are two risk measures that are inherently different in a real-world setting, emphasizing how one should be careful making conversions between the two based upon parametric assumptions.