台灣風險基礎資本額制度實施至今已將近七年，但風險係數卻從未調整，本研究主要針對股票指數與匯率之風險係數探討其是否有更新之必要，藉由1986年12月至2009年12月之資料，利用GARCH模型及EGARCH模型進行風險係數之估計，除了和風險基礎資本額制度相同，以風險值為考量外，另外加入條件尾端期望值，並比較其與風險值之差別。 實證結果發現，僅部分財務時間序列有顯著之槓桿效果，因此使用GARCH模型估計風險係數較為合適；所估計之風險係數，無論是股價指數或是匯率，其估計結果皆比現行標準高出許多。 / In Taiwan, Risk-based capital (RBC) is set up in 2003. From 2003 until now, no matter how the economical environment has changed, the risk factors have remained all the same.This research mainly focuses on the risk factors of stock index and foreign exchange and wants to know if the risk factors need to be changed. The data this research encompasses is from December 1986 to December 2009.The risk factors are estimated by GARCH model and EGARCH model, utilizing not only the VaR but also the conditional tail expectation (CTE). From the result, only a few financial time series have shown leverage effect, therefore it is indeed more appropriate to apply GARCH model in risk factors estimation. Moreover, the risk factors from the result of this research, whether it is stock index or foreign exchange rate, are significantly higher than the risk factors standard applicable in Taiwan at the present.

El siguiente proyecto de tesis pretende mostrar y verificar cómo las redes neuronales, en concreto, la red backpropagation son una alternativa para la predicción de la volatilidad condicional frente a los modelos econométricos clásicos de la familia GARCH. El estudio se realiza para diferentes índices bursátilies de diferentes tamaños y zonas geográficas, así como para datos tanto diarios como de alta frecuencia utilizando para la comparativa uno de los modelos más extendidos para el estudio de la volatildiad condicional en índices bursátiles como el EGARCH, dada la existencia comprobada de asimetrías en la volatildiad de dichos índices. La elección de la red neuronal backpropagation viene motivada por ser una de las redes neuronales más extendidas en su uso en finanzas por su capacidad de generalización método de aprendizaje basada en la relga delta generalizada. / Oliver Muncharaz, J. (2014). MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35803 / Alfresco

Made available in DSpace on 2016-06-02T20:06:01Z (GMT). No. of bitstreams: 1 2117.pdf: 990773 bytes, checksum: a7b62936541ab91d8ae3424f62aa0f40 (MD5) Previous issue date: 2008-08-22 / In the financial market usually notices are taken of the shares sequentially over the time in order to characterize them a time series. However, the major interest is to forecast the behavior of these shares. Motivated by this fact, a lot of models were created based on the past information considering constant averages and variance over time. Although, in financial series a feature often presented is called volatility, which can be noticed by the variance to vary in time. In order to catch this characteristic were developed the models of the family GARCH, that model the conditional variance through known information. These models were well used and have passed by many formulation modifications to be able to catch different effects, such as the effect leverage EGARCH. Thus, the goal is to estimate volatility patterns obeying the specifications of the family GARCH verifying which ones of them describe better the data inside and outside the sample. / No mercado financeiro costuma-se fazer observações sobre as carteiras sequencialmente ao longo do tempo, caracterizando uma série temporal. Contudo, o maior interesse está em prever o comportamento destas carteiras. Motivado por este fato, foram criados muitos modelos de previsão baseando-se em observações passadas considerando a média e variância constantes no tempo. Porém, nas séries financeiras uma característica muito presente é a chamada volatilidade, que pode ser observada pela variância não constante no tempo. A fim de captar esta característica, desenvolveram-se os modelos da família GARCH, que modelam a variância condicional através de informações passadas. Estes modelos foram muito utilizados e sofreram muitas modificações nas formulações para poderem captar diferentes efeitos, como o efeito de leverage (EGARCH). Assim, deseja-se estimar modelos de volatilidade obedecendo às especificações da família GARCH, verificando quais deles descrevem melhor os dados dentro e fora da amostra.

The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index sepa- rately. Unlike other works in this eld of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a partic- ular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.

This essay investigates how realized variance affects the GARCH-models (GARCH, EGARCH, GJRGARCH) when added as an external regressor. The GARCH models are estimated with three different distributions; Normal-, Student’s t- and Normal inverse gaussian distribution. The results are ambiguous - the models with realized variance improves the model fit, but when applied to forecasting, the models with realized variance are performing similar Value at Risk predictions compared to the models without realized variance.

The five essays in this dissertation are all concerned with how commodity price uncertainty affects the valuation of real and financial assets.  Focusing on the stochastic process approximating the price process of the commodity, a time-inhomogeneous mean reverting process is suggested and used in the valuation of a pulp mill.  Also an analytic approximation and a parameter estimation procedure to a stochastic volatility option-pricing model are developed.  Generally, the large valuation differences and hedging errors that occur for different assumptions about the price process indicate the importance of an appropriately specified price process.  The dissertation provides examples of this. The question of whether commodity prices are mean reverting or follow a random walk is also studied.  Using a large database with close to 300 different commodities, econometric tests favour a random walk.  There are very few exceptions.  However, when applied to an option pricing model, the time-inhomogeneous mean reverting process gives smaller hedging errors than the traditional Black-Scholes model based on a random walk.  The results are therefore inconclusive, although mean reversion seems more predominant than econometric tests reveal. / Diss. Stockholm : Handelshögskolan, 2003

近年來，金融資產報酬波動的推估一直是重要的研究課題。然而，過去的波動不對稱研究均集中在股票市場，探討外匯市場波動不對稱性的實證研究並不多，但若忽略其不對稱效果將影響未來波動預測的正確性。因此，本研究利用近十六年來美元及日圓兌台幣匯率日資料，以傳統的波動不對稱性指數型GARCH模型(EGARCH Model)、門檻型GARCH模型(TGARCH, GJR GARCH Model)，亦延用異質自我相關迴歸模型(HAR-RV Model)及修正型異質自我相關迴歸模型(Modified HAR-RV Model)分別探討美元及日圓兌台幣匯率波動是否存在不對稱現象及其不對稱程度，並加以分析。實證研究中，上述四種模型均顯示美元及日圓兌台幣匯率波動的確具有不對稱效果；美元兌台幣匯率波動，與股票市場一致，報酬率與波動度間呈負向關係，當台幣相對美元升值時，波動度較高；而日圓兌台幣匯率波動，與美元匯率變動方向相反，報酬率與波動度間呈正向關係，當台幣相對日圓貶值時，波動度較高。此外，以異質自我相關迴歸模型實證分析中，日波動落後項的影響力明顯大於週、月、季波動落後項，與Muller, et al. (1997)、Corsi (2004)及Andersen, et al. (2005)實證研究結果類似。

This dissertation thesis consists of three essays on new EU foreign exchange markets (FX), i.e. the Czech koruna, Polish zloty and Hungarian forint. In the first two essays, the impact of foreign macroeconomic news announcements and central banks' monetary policy settings on the value and volatility of examined exchange rates is analyzed. In the third chapter, the conditional comovements and volatility spillovers on new EU FX markets is examined. The aim of this thesis is to contribute to the existing empirical literature by providing new evidence of the examined currencies during periods, which have not been examined yet (after the Global financial crisis (GFC), during the EU debt crisis and during currency interventions in the Czech Republic). The first essay (Chapter 2) examines the impact of Eurozone/Germany and US macroeconomic news announcements and monetary policy settings of the ECB and the Fed on the value of new EU member states' currencies. It is a complex analysis of 1-minute intraday dataset performed by event study methodology (ESM). We observe different reactions of exchange rates in pair with the US dollar on the US macroeconomic announcements and Euro-expressed FX rates on Germany macro news during the EU debt crisis and after it. We also provide evidence of leaking news, showing...