[pt] O uso do Valor em Risco (VaR) para avaliar o risco tem sido amplamente utilizado, desconsiderando o efeito do tamanho relativo das posições da carteira sob análise em relação ao mercado. Ao adotar esse modelo, está se aceitando a hipótese de que é possível liquidar suas posições ao longo do prazo para o qual foi calculado o VaR. Pode-se adotar prazos para o VaR compatíveis com o ativo menos líquido da carteira sob análise, entretanto estaremos desconsiderando efeitos diferenciados da liquidez de cada papel. Tenta-se aqui avaliar se modelos de Valor em Risco que consideram a liquidez do mercado são melhores que modelos que não consideram a liquidez. / [en] Value-at-Risk (VaR) has been widely used as a mean to gauge risk regardless of the relative size effect of the portfólio standings as analysed against the market. By adopting this sort of model, one hypothetically takes the possibility of clearing standings along a certain period of time, called horizon, for which VaR has been calculated. It is also possible to use different VaR horizons compatible with the least liquid instrument of the analysed portfólio, nonetheless, we will not be taking into account the differentiated effects of each instrument liquidity. Herein our chief goal is to access whether VaR models considering market liquidity are more adequate than those not taking it into consideration.

Στην εργασία αυτή προσεγγίζεται μια μέθοδος ιδιαίτερα γνωστή στον χρηματοπιστωτικό τομέα, με την οποία γίνεται αποτίμηση της αξίας σε κίνδυνο, Value at Risk, που είναι εκτεθειμένες μετοχές και χαρτοφυλάκιο, σύμφωνα με την συσχέτιση των αποδόσεων των περουσιακών τους στοιχείων, αλλά και το συστηματικό κίνδυνο αυτών σε σχέση με τις γενικές τάσεις της αγοράς. Χρησιμοποιώντας τις αποδόσεις 3 μετοχών, αλλά και ενός χαρτοφυλακίου μετοχών απο το Χρηματιστήριο Αξιών Αθηνών, ο βασικός στόχος της παρούσας διατριβής είναι να γίνει μια συγκρτική ανάλυση της αξίας σε κίνδυνο (VaR) για έναν επενδυτή με θέση αγοράς σε διάφορα επίπεδα εμπιστοσύνης και για δύο υποδείγματα δεσμευμένης ετεροσκεδαστικότητας (GARCH και E-GARCH). Για αυτή την συγκριτική ανάλυση, χρησιμοποιείται μια μεθοδολογία για τον έλεγχο των αποτελεσμάτων των παραπάνω υποδειγμάτων, γνωστή ως έλεγχος Kupiec. / In this Theses, we present an application well-known in the financial sector, Value at Risk, with which we measure the risk of stocks and portofolios. Using the returns of 3 stocks and a portofolio from the Greek Stock Exchange Market, the basic goal of the present theses is to make a comparative analysis of the value at risk for an investor with long rading position in various confidence levels and for two generalized autoregressive conditional heteroskedasticity models (GARCH and E-GARCH). For this comparative analysis, a methodology is used to backtest the results of the GARCH models, known as Kupiec Test.

The purpose of this thesis is to identify the best volatility model for Value-at-Risk(VaR) estimations. We estimate 1 % and 5 % VaR figures for Nordic indices andstocks by using two symmetrical and two asymmetrical GARCH models underdifferent error distributions. Out-of-sample volatility forecasts are produced usinga 500 day rolling window estimation on data covering January 2007 to December2014. The VaR estimates are thereafter evaluated through Kupiec’s test andChristoffersen’s test in order to find the best model. The results suggest thatasymmetrical models perform better than symmetrical models albeit the simpleARCH is often good enough for 1 % VaR estimates.

Submitted by Fabiola Costa (famedina06@hotmail.com) on 2010-08-24T14:18:56Z No. of bitstreams: 1 Dissertacao_Fabiola_Medina_Costa.pdf: 981365 bytes, checksum: 368c8b3a6a54c3a8e7c0f62130bcf2a3 (MD5) / Approved for entry into archive by Vitor Souza(vitor.souza@fgv.br) on 2010-08-24T14:39:21Z (GMT) No. of bitstreams: 1 Dissertacao_Fabiola_Medina_Costa.pdf: 981365 bytes, checksum: 368c8b3a6a54c3a8e7c0f62130bcf2a3 (MD5) / Made available in DSpace on 2010-08-24T17:42:48Z (GMT). No. of bitstreams: 1 Dissertacao_Fabiola_Medina_Costa.pdf: 981365 bytes, checksum: 368c8b3a6a54c3a8e7c0f62130bcf2a3 (MD5) Previous issue date: 2010-05-28 / This paper proposes the historical simulation model to calculate the VaR, considering return ajusted by the realized volatility measured from intraday returns. The database consists of five most liquid share among the different segments of Bovespa Index. For the proposed methodology we used two of the empirical theories of the empirical literature - adjusted historical simulation and realized volatility. The Kupiec tes and Christoffersen test are used to analized and veryfy the proposed methodology performance. / O presente trabalho propõe para o cálculo VaR o modelo de simulação histórica, com os retornos atualizados pela volatilidade realizada calculada a partir de dados intradiários. A base de dados consiste de cinco ações entre as mais líquidas do Ibovespa de distintos segmentos. Para a metodologia proposta utilizamos duas teorias da literatura empírica – simulação histórica ajustada e volatilidade realizada. Para análise e verificação do desempenho da metodologia proposta utilizamos o Teste de Kupiec e o Teste de Christoffersen.

Topic of this master's thesis is a summarization of the documents on the web. First, it deals with the issues of acquiring text from the web using wrapper. An overview of wrappers used as an inspiration for the future implementation is stated. This paper also includes various methods for creating summary (Luhn`s, Edmundson`s and KPC) from the text data. Application design for the text data extraction and summarization is also part of this paper. Application is based on Java platform and Swing graphic library.

In the financial industry, it has been increasingly popular to measure risk. One of the most common quantitative measures for assessing risk is Value-at-Risk (VaR). VaR helps to measure extreme risks that an investor is exposed to. In addition to acquiring information of the expected loss, VaR was introduced in the regulatory frameworks of Basel I and II as a standardized measure of market risk. Due to necessity of measuring VaR accurately, this thesis aims to be a contribution to the research field of applying GARCH-models to financial time series in order to forecast the conditional variance and find accurate VaR-estimations. The findings in this thesis is that GARCH-models which incorporate the asymmetric effect of positive and negative returns perform better than a standard GARCH. Further on, leptokurtic distributions have been found to outperform normal distribution. In addition to various models and distributions, various rolling windows have been used to examine how the forecasts differ given window lengths.

The role of risk management has gained momentum in recent years most notably after the recent financial crisis. This thesis uses a quantitative approach to evaluate the theory of value at risk which is considered a benchmark to measure financial risk. The thesis makes use of both parametric and non parametric approaches to evaluate the effectiveness of VAR as a standard tool in measuring risk of stock portfolio. This study uses the normal distribution, student t-distribution, historical simulation and the exponential weighted moving average at 95% and 99% confidence levels on the stock returns of Sonny Ericsson, Three Months Swedish Treasury bill (STB3M) and Nordea Bank. The evaluations of the VAR models are based on the Kupiec (1995) Test. From a general perspective, the results of the study indicate that VAR as a proxy of risk measurement has some imprecision in its estimates. However, this imprecision is not all the same for all the approaches. The results indicate that models which assume normality of return distribution display poor performance at both confidence levels than models which assume fatter tails or have leptokurtic characteristics. Another finding from the study which may be interesting is the fact that during the period of high volatility such as the financial crisis of 2008, the imprecision of VAR estimates increases. For the parametric approaches, the t-distribution VAR estimates were accurate at 95% confidence level, while normal distribution approach produced inaccurate estimates at 95% confidence level. However both approaches were unable to provide accurate estimates at 99% confidence level. For the non parametric approaches the exponentially weighted moving average outperformed the historical simulation approach at 95% confidence level, while at the 99% confidence level both approaches tend to perform equally. The results of this study thus question the reliability on VAR as a standard tool in measuring risk on stock portfolio. It also suggest that more research should be done to improve on the accuracy of VAR approaches, given that the role of risk management in today’s business environment is increasing ever than before. The study suggest VAR should be complemented with other risk measures such as Extreme value theory and stress testing, and that more than one back testing techniques should be used to test the accuracy of VAR.

Bakgrund: I och med att Basel II har instiftats i Sverige så måste finansiella institutioner beräkna sin marknadsrisk på sina portföljer. Detta kan göras genom olika VaR metoder. Dessa ger dock olika uppskattningar på marknadsrisken. De finansiella instituten får använda sig av den metod som de anser reflektera marknadsrisken bäst. Det finns dock ingen metod som utsetts till standard. Syfte: Syftet med detta arbete är att jämföra olika VaR-metoders skattning av marknadsrisken utifrån verkligt utfall, för att urskilja vilken metod som är funktionsdugligast. Avgränsningar: Denna undersökning inkluderar fyra olika VaR metoder. Dessa är Historisk Simulation, Delta-Normal, RiskMetrics och GARCH(1,1). VaR metoderna kommer att undersökas på portföljer som endast består av svenska aktier noterade på Stockholmsbörsens Large-, Mid- eller Small Cap lista. Metod: Vi har konstruerat fyra olika portföljer som vi sedermera har beräknat VaR för mellan 1998-04-01 t.o.m. 2008-04-01. Dessa uppskattningar har sedermera jämförts, m.h.a. backtesting, med det verkliga utfallet för portföljerna. Utifrån detta har vi analyserat vilken form av metod som är funktionsdugligast. Resultat, slutsatser: Vi kan konstatera att ingen av de metoder som vi har undersökt är godkända enligt vår backtesting. Om vi bortser från detta så verkar RiskMetrics vara funktionsdugligast då denna metod innehar få överträdelser och uppskattar marknadsrisken på ett effektivt sätt. Detta samtidigt som RiskMetrics är stabilast under hela undersökningsperioden.

<p>Bakgrund: I och med att Basel II har instiftats i Sverige så måste finansiella institutioner beräkna sin marknadsrisk på sina portföljer. Detta kan göras genom olika VaR metoder. Dessa ger dock olika uppskattningar på marknadsrisken. De finansiella instituten får använda sig av den metod som de anser reflektera marknadsrisken bäst. Det finns dock ingen metod som utsetts till standard.</p><p>Syfte: Syftet med detta arbete är att jämföra olika VaR-metoders skattning av marknadsrisken utifrån verkligt utfall, för att urskilja vilken metod som är funktionsdugligast.</p><p>Avgränsningar: Denna undersökning inkluderar fyra olika VaR metoder. Dessa är Historisk Simulation, Delta-Normal, RiskMetrics och GARCH(1,1). VaR metoderna kommer att undersökas på portföljer som endast består av svenska aktier noterade på Stockholmsbörsens Large-, Mid- eller Small Cap lista.</p><p>Metod: Vi har konstruerat fyra olika portföljer som vi sedermera har beräknat VaR för mellan 1998-04-01 t.o.m. 2008-04-01. Dessa uppskattningar har sedermera jämförts, m.h.a. backtesting, med det verkliga utfallet för portföljerna. Utifrån detta har vi analyserat vilken form av metod som är funktionsdugligast.</p><p>Resultat, slutsatser: Vi kan konstatera att ingen av de metoder som vi har undersökt är godkända enligt vår backtesting. Om vi bortser från detta så verkar RiskMetrics vara funktionsdugligast då denna metod innehar få överträdelser och uppskattar marknadsrisken på ett effektivt sätt. Detta samtidigt som RiskMetrics är stabilast under hela undersökningsperioden.</p>

本篇文章將採用長期記憶模型之一的HYGARCH模型，搭配1985年廣場協議後的日圓匯率期貨資料來估計日圓期貨匯率買入和放空部位的日報酬風險值，探討控管日圓匯率期貨在使用上的風險。為了更準確地計算風險值，本文採用常態分配、學生t分配以及偏態學生t分配來作模型估計以及風險值之計算。 本文實證的結果將有兩方面的貢獻：首先，實證結果顯示當我們採用厚尾分配估計風險值時，樣本內風險值的估計誤差會與信賴水準的高低呈正比的現象，證明在極端的風險值估計上，厚尾分配均有較佳的表現。其次，與其他使用HYGARCH模型研究日圓匯率的文章相較，本文在風險控管層面上所提供的偏態學生t分配，於估計風險值時，比起只考慮厚尾的對稱學生t分配將來得更為有效，其不但在估計誤差上較小，而且根據Kupiec檢定法，其在樣本內的風險值估計也有較好的表現。此外，本文也將多方證明此資料的偏態分配屬於右偏。 / In order to manage the exposure of the dollar/yen futures returns with regarding the long memory behavior in volatility, we use the HYGARCH(1,d,1) model with the data after the Plaza Accord to compute daily Value-at-Risk (VaR) of long and short trading positions. To take into account the fat-tail situation in financial time series, we estimate the model under the normal, Student-t, and skewed Student-t distributions. The contribution of this article is twofold. First, the empirical results show that the bias of in-sample VaR increases as the confidence level increases when VaR is calculated with a fat-tail distribution. Second, we provide a better distribution, the skewed Student-t innovation, for estimating the HYGARCH model for the Japanese yen in respect of risk management because the bias under the skewed Student-t innovation is smaller than that under the Student-t distribution, and in-sample VaR of the models with a skewed Student-t distribution outperforms based on Kupiec test. In addition, we get the innovation skewed to the right through the in-sample VaR analysis.