No / We study the price and liquidity effects following the FTSE 100 index revisions. We employ the standard GARCH(1,1) model to allow the residual variance of the single index model (SIM) to vary systematically over time and use a Kalman filter approach to model SIM coefficients as a random walk process. We show that the observed price effect depends on the abnormal return estimation methods. Specifically, the OLS-based abnormal returns indicate that the price effect associated with the index revision is temporary, whereas both SIM with random coefficients and GARCH(1,1) model suggest that both additions and deletions experience permanent price change. Added (removed) stocks exhibit permanent (temporary) change in trading volume and bid-ask spread. The analysis of the spread components suggests that the permanent change associated with additions is a result of non-information-related liquidity. We interpret the permanent price effect of additions and deletions combined with the permanent (temporary) shift in liquidity of added (removed) stocks as evidence in favour of the imperfect substitution hypothesis with some non-information-related liquidity effects in the case of additions.

No / This study investigates the impact of LIFFE's introduction of individual equity futures contracts on the risk characteristics of the underlying stocks trading on the LSE. We employ the Fama and French three-factor model (TFM) to measure the change in the systematic risk of the underlying stocks which arises subsequent to the introduction of futures contracts. A GJR-GARCH(1,1) specification is used to test whether the futures contract listing affects the permanent and/or the transitory component of the residual variance of returns, and a control sample methodology isolates changes in the risk components that may be caused by factors other than futures contract innovation. The observed increase (decrease) in the impact of current (old) news on the residual variance implies that futures contract listing enhances stock market efficiency. There is no evidence that futures innovation impacts on either the systematic risk or the permanent component of the residual variance of returns.

No / This study assesses whether the widely documented momentum profits can be ascribed to time-varying risk as described by a GJR-GARCH(1,1)-M model. Consistent with rational pricing in efficient markets, we reveal that momentum profits are a compensation for time-varying unsystematic risks, common to the winner and loser stocks. We also find that, because losers have a higher propensity than winners of disclose bad news, negative return shocks increase their volatility more than it increases that of the winners. The volatility of the losers is also found to respond to news more slowly, but eventually to a greater extent, than that of the winners. Following Hong et al. (2000), we interpret this as a sign that managers of loser firms are reluctant to disclosing bad news, while managers of winner firms are eager to releasing good news

This thesis consists of three studies which cover topics in the trading volume-market return volatility linkage, stock market return-aggregate mutual fund flow relationship as well as market return volatility-aggregate mutual fund flow interaction. Chapter 2 investigates the issue of volume-volatility linkage in the US market for the period 1990-2012 (S&P 500) and 1992-2012 (Dow Jones). We construct four sub-samples depending on three different structural points (the Asian Financial Crisis, the Dot-Com Bubble and the 2007 Financial Crisis). By employing univariate and bivariate GARCH processes, we find positive (negative) bidirectional linkages between these two aforementioned variables in various cases of the estimation, while a mixed one is observed in the remainder of these cases. Chapter 3 examines the issue of temporal ordering of the range-based stock market return (S&P 500 index) and aggregate mutual fund flow in the U.S. market for the period 1998-2012. We construct nine sub-samples represented by three fundamental cases of the whole data set. In addition, we take into consideration three essential indicators when splitting the whole data set, which are the 2000 Dot-Com Bubble, the 2007 Financial Crisis as well as the 2009 European Sovereign Debt Crisis. We examine the dynamics of the return-flow interaction by employing bivariate VAR model with various specifications of GARCH approach. Our principal findings display a bidirectional mixed feedback between stock market return and aggregate mutual fund flow for the majority of the sub-samples obtained. Nevertheless, we provide limited evidence of a positive bi-directional causality between return and flow. Chapter 4 investigates the dynamic relation between S&P 500 return volatility and U.S. aggregate mutual fund flow for the period spanning between 1998 and 2012. We assess the dynamics of the volatility-flow linkage by employing a bivariate VAR model with the GARCH approach which allows for long memory in the mean and the variance equations. In addition to the sub-samples obtained in chapter 3, we generate two measurements of volatility. Our baseline results indicate a variety of bidirectional mixed causalities between market return volatility and aggregate mutual fund flow in several sub-samples. In addition, we observe a negative/positive bi-directional relationship between volatility and flow in the rest of the sub-periods. Summarizing, a range of our findings are in line with the empirical underpinnings that most likely predict a significant linkage between the aforementioned variables. Finally, most of the bidirectional effects are found to be quite robust to the dynamics of the various GARCH processes employed in this thesis.

We examine the short-term price reaction of 424 UK stocks to large one-day price changes. Using the GJR-GARCH(1,1), we find no statistical difference amongst the cumulative abnormal returns (CARs) of the Single Index, the Fama–French and the Carhart–Fama–French models. Shocks ⩾5% are followed by a significant one-day CAR of 1% for all the models. Whilst shocks ⩽−5% are followed by a significant one-day CAR of −0.43% for the Single Index, the CARs are around −0.34% for the other two models. Positive shocks of all sizes and negative shocks ⩽−5% are followed by return continuations, whilst the market is efficient following larger negative shocks. The price reaction to shocks is unaffected when we estimate the CARs using the conditional covariances of the pricing variables.

Day-ahead spot electricity markets are the most transparent spot markets where one can find integrated supply and demand curves of the market players for each settlement period. Since it is an indicator for the market players and regulators, in this thesis we model the spot electricity prices. Logarithmic daily average spot electricity prices are modeled as a summation of a deterministic function and multi-factor stochastic process. Randomness in the spot prices is assumed to be governed by three jump processes and a Brownian motion where two of the jump processes are mean reverting. While the Brownian motion captures daily regular price movements, the pure jump process models price shocks which have long term effects and two Ornstein Uhlenbeck type jump processes with different mean reversion speeds capturing the price shocks that affect the price level for relatively shorter time periods. After removing the seasonality which is modeled as a deterministic function from price observations, an iterative threshold function is used to filter the jumps. The threshold function is constructed on volatility estimation generated by a GARCH(1,1) model. Not only the jumps but also the mean reverting returns following the jumps are filtered. Both of the filtered jump processes and residual Brownian components are estimated separately. The model is applied to Austrian, Italian, Spanish and Turkish electricity markets data and it is found that the weekly forecasts, which are generated by the estimated parameters, turn out to be able to capture the characteristics of the observations. After examining the future contracts written on electricity, we also suggest a decision technique which is built on risk premium theory. With the help of this methodology derivative market players can decide on taking whether a long or a short position for a given contract. After testing our technique, we conclude that the decision rule is promising but needs more empirical research.

自從90年代以來，許多機構因為金融商品的操縱不當或是金融風暴的衝擊數度造成全球金融市場的動盪，使得風險管理的重要性與日俱增，而量化風險模型的準確性也益受重視，基於財務資料的相關性質如異質變異、厚尾現象等，本文主要結合AR(1)-GARCH(1,1)模型、極值理論、copula函數三種模型應用在風險值的估算，且將報酬分配的假設區分為三類，一是無母數模型的歷史模擬法，二是基於常態分配假設下考量隨機波動度的有母數模型，三是利用歷史資料配適尾端分配的極值理論法來對聯電、鴻海、國泰金、中鋼四檔個股和台幣兌美元、日圓兌美元、英鎊兌美元三種外匯資料作一日風險值、十日風險值、組合風險值的測試。 實證結果發現，在一日風險值方面，95%信賴水準下以動態風險值方法表現相對較好，99%信賴水準下動態極值理論法和動態歷史模擬法皆有不錯的估計效果；就十日風險值而言，因為未來十日資產的報酬可能受到特定事件影響，所以估計上較為困難，整體看來在99%信賴水準下以條件GPD＋蒙地卡羅模擬的表現相對較理想；以組合風險值來說， copula、Clayton copula+GPD marginals模擬股票或外匯組合的聯合分配不論在95%或99%信賴水準下對其風險值的估計都獲得最好的結果；雖然台灣個股股價受到上下漲跌幅7%的限制，台幣兌美元的匯率也受到央行的干涉，但以極值理論來描述資產尾端的分配情形相較於假設其他兩種分配仍有較好的估計效果。

Submitted by Stefano Toto (stefanototo92@gmail.com) on 2015-03-24T18:06:58Z No. of bitstreams: 1 FInal version Stefano Toto .pdf: 2666174 bytes, checksum: a92ae5ee1fd88876c05d33145bf36d74 (MD5) / Approved for entry into archive by Luana Rodrigues (luana.rodrigues@fgv.br) on 2015-03-30T13:27:41Z (GMT) No. of bitstreams: 1 FInal version Stefano Toto .pdf: 2666174 bytes, checksum: a92ae5ee1fd88876c05d33145bf36d74 (MD5) / Made available in DSpace on 2015-03-30T13:36:05Z (GMT). No. of bitstreams: 1 FInal version Stefano Toto .pdf: 2666174 bytes, checksum: a92ae5ee1fd88876c05d33145bf36d74 (MD5) Previous issue date: 2015-02-27 / This research is to be considered as an implementation of Goetzmann and Jorion (1999). In order to provide a more realistic scenario, we have implemented a Garch (1,1) approach for the residuals of returns and a multifactor model thus to better replicate the systematic risk of a market. The new simulations reveal some new aspects of emerging markets’ expected returns: the unpredictability of the emerging markets’ returns with the global factor does not depend on the year of emergence and that the unsystematic risk explains the returns of emerging markets for a much larger period of time. The results also reveal the high impact of Exchange rate, Commodities index and of the Global factor in emerging markets’ expected return.

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.

本論文主要在探討以移動平均法則為基礎的簡單技術分析指標，以及時間序列模型在台灣股票市場是否具有獲利能力，研究期間為1987/01/01-2006/12/31共20年的樣本期間。我們發現只有使用(1,50,0)和(1,50,0.01) 這兩個移動平均交易法則時才有顯著的報酬；並以AR(1)-GARCH(1,1)-M作為時間序列的預測模型。研究發現在股價上漲的時候，技術分析指標的確有較好的預測能力；而在股價下跌時，利用時間序列模型有較佳的獲利能力。因為技術分析指標與時間序列模型分別捕捉到不同的資訊，將兩預測工具結合在一起應該可以得到一個更好的組合預測指標。本文的實證研究發現此一組合預測指標，不管是在多頭或空頭期間時，都可以比使用單一分析工具獲得更高報酬。