Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2016-10-13T17:45:33Z No. of bitstreams: 2 Dissertação - Luciano Ferreira da Silva - 2016.pdf: 1661946 bytes, checksum: 28fb8508d545a9e09562fc653abfc880 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-10-13T17:45:33Z (GMT). No. of bitstreams: 2 Dissertação - Luciano Ferreira da Silva - 2016.pdf: 1661946 bytes, checksum: 28fb8508d545a9e09562fc653abfc880 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-09-05 / Although the search for strenghtening regulation and control had only been explicited in the 1995 with the Managerial Reform of Ministry of Government Administration and Reform (MARE), through State's managerial reform master plan, the basic sanitation sector only achieved federal regulatory landmark with the 11.445/2007 law. This law has brought universal access to services as one of the fundamental principles and objectives pursued by organizations related to this sector, which, after analyzing financial resources needed, estimated by the Ministry of Cities in 2013, would be inconceivable without the participation of private capital. On the other hand, introducing private capital necessarily improves regulatory action, once the more transparent and stable are the rules and mechanisms behind regulatory agencies, the greater are the chances of return to investors and the impacts on cost of companies’ capital are smaller. Thus, assessment of risk impacts derived from the action of regulatory agencies is of fundamental importance to the awareness of the need for greater transparency predictability and stability of the rules. Thus, whereas the Basic Sanitation Company of the State of São Paulo (Sabesp), is commonly used as a benchmark for the sector, among other reasons because it is the largest segment, this study examined the effects of Resolutions of the Regulatory Agency of sanitation and Energy of the State of São Paulo (Asesp), the regulatory events, of economic and financial nature, from 2007 to 2015, on the clusters of volatility of return of sanitation Company shares Basico do Estado de Sao Paulo (Sabesp ) by the method TGARCH (Threshold Generalized Autoregressive Conditional Heteroscedastic). The results constitute evidence that the regulatory agency decisions can influence the volatility when it generates change in expectations and may cause changes in the risk and cost of capital. At the end has crafted a diagnosis that considered correct use of the CAPM and WACC to define the cost of capital, but pointed out that the decision of Arsesp in not recognizing a specific component for regulatory risk was inadequate. Recommends to the next tariff review cycle of Sabesp, the assessment by the Arsesp of possibility of including a specific component to reward investors due to regulatory risk. / Embora a busca pelo fortalecimento das funções de regulação e controle tenha sido explicitada em 1995, com a reforma gerencial, pelo Ministério da Administração Federal e Reforma do Estado (MARE), por meio do Plano Diretor da Reforma do Aparelho do Estado, o setor de saneamento básico só veio a contar com um marco regulatório federal, a Lei nº 11.445, em 2007. Essa lei trouxe como um dos princípios fundamentais e objetivo perseguido pelas organizações do setor a universalização do acesso aos serviços, algo que, pelo montante de recursos financeiros necessários, estimado pelo Ministério das Cidades em 2013, seria inconcebível sem a participação do capital privado. Por outro lado, a atração do capital passa necessariamente pela melhoria da ação regulatória, pois quanto mais transparente e estável forem as regras e os mecanismos de atuação das agências reguladoras, maiores serão as chances de retorno aos investidores e menores serão os impactos causados no custo de capital. Assim, a avaliação dos impactos decorrentes do risco derivado da atuação das agências reguladoras é de fundamental importância para a sensibilização quanto a necessidade de maior transparência previsibilidade e estabilidade das regras. Desse modo, considerando que a Companhia de Saneamento Básico do Estado de São Paulo (Sabesp), é comumente utilizada como um benchmark para o setor, dentre outras razões porque é a maior do segmento, este trabalho analisou os efeitos das Deliberações da Agência Reguladora de Saneamento e Energia do Estado de São Paulo (Asesp), os eventos regulatórios, de natureza econômico-financeira, no período de 2007 a 2015, sobre os clusters de volatilidade do retorno das ações da Companhia de Saneamento Básico do Estado de São Paulo (Sabesp), por meio da metodologia TGARCH (Threshold Generalized Autoregressive Conditional Heteroscedastic). Os resultados constituem-se em evidências de que as decisões da agência reguladora podem influenciar a volatilidade quando gera modificação nas expectativas, podendo provocar alterações no risco e no custo de capital. Ao final foi elaborado um diagnóstico que considerou correta a utilização do CAPM e da metodologia WACC para a definição do custo de capital, mas apontou que a decisão da Arsesp em não reconhecer um componente específico para o risco regulatório foi inadequada. Recomenda para o próximo ciclo de revisão tarifária da Sabesp, a avaliação por parte da Arsesp da possibilidade de inclusão de um componente específico para premiar o investidor em razão do risco regulatório.

No description available.

本文主要探討匯率波動對台灣出口貿易的影響，實證重點在於匯率波動估計方法之不同，是否會對出口貿易產生不同的影響。樣本期間自1990年1月至2006年12月，共204筆月資料。匯率波動的估計模型包含移動平均變異數、GARCH模型、門檻GARCH模型(TGARCH)及指數型GARCH模型(EGARCH)，本研究先估計出匯率波動因子再進一步代入出口方程式中作估計，相較於國內其他文獻，本文考慮了匯率波動不對稱情形存在的可能性，同時也考慮了變數是否呈定態的問題。實證結果顯示，台幣貶值會造成比較大的匯率波動；利用TGARCH(1,2)作為匯率波動估計模型，結果顯示匯率波動增加會刺激台灣對美國和日本的出口量。

This paper explores the possibility of gold and Bitcoin acting as safe haven investments during the Corona pandemic. To answer the research question the authors use OLS-, GARCH-, and TGARCH-models. The S&P 500 stock- and S&P U.S. Aggregate bond-indexes are used as a measure of the performance on U.S. stock- and bond-market. Safe haven assets have a negative beta during turbulent times and therefore the period of 2020-01-01 to 2022-03-31 will be analyzed. A period of five years leading up to the pandemic as well as the turbulent time period will be used as an average to enable comparison between regular and trying times. The results conclude that neither Bitcoin nor gold can be viewed as safe haven assets. However, it is found that both assets can work as diversifiers in the two markets.

The purpose of this paper is to select the best GARCH-type model for modelling the volatility of Bitcoin, Bitcoin Cash, Litecoin, Dogecoin and Ethereum. GARCH (1,1), IGARCH(1,1), EGARCH(1,1), TGARCH(1,1) and CGARCH(1,1) are used on the cryptocurrencies closing day return. We select the model with the highest Maximum Likelihood and run an OLS regression on the conditional volatility to measure the day-of-the-week effect. The findings show that EGARCH(1,1) model best suits Bitcoin, Litecoin, Dogecoin and Ethereum data and that the GARCH(1,1) model suits best Bitcoin data. The results show a significant presence of day-of-the-week effects on the conditional volatility of some days for Bitcoin, Bitcoin Cash and Ethereum. Wednesday has a significant negative effect on Bitcoin conditional volatility. Friday, Saturday and Sunday are found to be significant and positive on Bitcoin Cash conditional volatility. Finally, Saturday is found to be significant and positive on Ethereum conditional volatility.

This thesis examines the validity of the cost of carry model for pricing FTSE100 futures contracts and the relationship between FTSE100 spot and futures markets during two sub-periods characterised by different market trading systems employed by the LSE and LIFFE. The empirical work is carried out using three approaches to econometric modeling: a basic VECM for spot and futures prices, a VECM extended with a DCCTGARCH framework to account for the conditional variance-covariance structure for spot and futures prices and a threshold VECM to capture regime-dependent spot-futures price dynamics. Overall, both the basic VECM and the DCC-TGARCH analysis suggest that there are deviations from the cost of carry relationship in the first sub-sample when transactions costs in both markets are relatively high but that the cost of carry relationship tends to be valid in the second sub-sample when transactions costs are lower. This is further confirmed by the evidence of higher conditional correlations between the two markets in the second sub-sample as compared with the first, using the DCC-TGARCH analysis. This implies that the no-arbitrage cost of carry relationship between spot and futures markets is more effectively maintained by index arbitrageurs in the second period when market conditions are closer to perfect market assumptions, and hence the cost of carry model could be more reasonably used as a benchmark for pricing stock index futures. The threshold VECM analysis depicts regime-dependent price dynamics between FTSE100 spot and futures markets and leads to some interesting and important findings: arbitrage may not be practicable under some market conditions, either because it is difficult to find counterparties for the arbitrage transactions, or because there is significant risk associated with arbitrage; as a result, the cost of carry model may not always be suitable for pricing stock index futures. Furthermore, the threshold values yielded from estimating the threshold VECM reflect the average transaction costs for most arbitrageurs that are more reliable and fair than subjective estimations.

<p>The work is focused on the Value at Risk and the Expected Shortfallcalculation. We assume the returns to be based on two pillars - the white noise and the stochastic volatility. We assume that the white noise follows the NIG distribution and the volatility is modeled using the nGARCH, NIG-GARCH, tGARCH and the non-parametric method. We apply the models into the stocks of three Banks of the Nordic market. We consider the daily and the intraday returns with the frequencies 5, 10, 20 and 30 minutes. We calculate the one step ahead VaR and ES for the daily and the intraday data. We use the Kupiec test and the Markov test to assess the correctness of the models. We also provide a new concept of improving the daily VaR calculation by using the high frequency returns. The results show that the intraday data can be used to the one step ahead VaR and the ES calculation. The comparison of the VaR for the end of the following trading day calculated on the basis of the daily returns and the one computed using the high frequency returns shows that using the intraday data can improve the VaR outcomes.</p>

The work is focused on the Value at Risk and the Expected Shortfallcalculation. We assume the returns to be based on two pillars - the white noise and the stochastic volatility. We assume that the white noise follows the NIG distribution and the volatility is modeled using the nGARCH, NIG-GARCH, tGARCH and the non-parametric method. We apply the models into the stocks of three Banks of the Nordic market. We consider the daily and the intraday returns with the frequencies 5, 10, 20 and 30 minutes. We calculate the one step ahead VaR and ES for the daily and the intraday data. We use the Kupiec test and the Markov test to assess the correctness of the models. We also provide a new concept of improving the daily VaR calculation by using the high frequency returns. The results show that the intraday data can be used to the one step ahead VaR and the ES calculation. The comparison of the VaR for the end of the following trading day calculated on the basis of the daily returns and the one computed using the high frequency returns shows that using the intraday data can improve the VaR outcomes.

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。

本文主要在探討影響台灣股票市場波動的因素，除了考慮以之前學者設定的 VAR(12)模型研究，另外以 SUR(5)模型來討論股市波動與基本面、交易面間的關係；最後，再以自我迴歸異質條件變異數模型來分析股市波動的特性。最重要的是，我們會根據誤差項的各類檢定結果來判定研究股市波動性質的最佳模型。 在聯立方程式的估計中，我們發現代表資訊到達指標的兩變數--週轉率與成交量成長率--會影響股票市場的波動。另外，我們找出交易面（成交量成長率）可能會影響基本面（匯率），這也就是說，在研究股市波動時，我們不需要特別區分變數的屬性。 在 GARCH 模型及 TGARCH 模型中，我們仍然可發現週轉率與成交量成長率會影響股市條件平均數或條件變異數；除此之外，好壞消息對股市日報酬率條件變異數（條件波動）應有不同的影響效果（壞消息的影響力較快反應）。而股市自身風險係數雖然統計檢定上不顯著異於零，但若未加入條件平均數的估計式，則可能會使模型得到較差的誤差項檢定結果，顯見股市自身風險應為影響投資人設定期望報酬率水準的重要因素之一。 從上述估計結果，我們可以知道，若散戶投資人能正確解讀市場上出現的各種新資訊之背後意義，將可使成交量成長率或週轉率（大部份可能代表無意義或不正確的交易行為）的變動幅度降低，進而有效地減少股票市場中股價異常波動的現象。 / My essay's topic focuses on discussing the factors that influence stock market volatility in Taiwan's stock market. Besides VAR(12) model as previous researchers have studied, I tries to set up SUR(5) models analyzing the relationship among the stock market volatility、the foundamental variables'volatilities and trading activities; Then I cited ARCH models ( autoregressive conditional heteroskedisticity models ) to find out the characteristics of stock market volatility. Most important of all, according to each misspecification test ( residual test ), I would specify the better models to describe the stock market volatility. In the estimations of system equations ( VAR(12)and SUR(5)models ), first I found that turnover rate and the growth rate of trading volume, which represent the information arrival indexes, could effect stock return's monthly conditional variance. Second, I especially found out the evidence that trading activities (trading volume growth) would probably have an impact on the macroeconomic variable ( exchange rate volatility ). It shows that we don't need to distinguish the attributes of those factors which could influence stock market volatility. In GARCH and TGARCH model, the positive influences of turnover and trading volume growth on daily stock return's conditional mean and conditional variance ( conditional volatility ) are still obvious, Within these TGARCH model, I discovered that bad news and good news could have different influences on stock market volatility ( the impact of bad news which resulted in downward movements of stock market volatility appeared faster that the good news'which caused upward movements). Stock market's self-risk(σ_t-1^{^2}) is statistically insignificant different from zero in GARCH models, but when I omitted this variable in daily stock return's conditional mean estimation equation, standardized residual might not obey the assumption of normal distribution. It apparently told us that the stock market's self-risk term ( σ_t-1^{^2} ) is one of the critical factors which influences investors to estimate expected return level. From those results above, we realized that if investors could precisely understand the real meanings of new information conveying in the stock market, it might decrease the levels of turnover and trading volume growth ( which could sometimes represent meaningless or inexact trading activities ), then effectively reduce the abnormal volatility phenomenon in stock market.