Diss. Stockholm : Handelshögsk.

Diss. Stockholm : Handelshögsk., 2004.

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The thesis offers a study on the stock market volatility in the countries of Central Eastern Europe and South Eastern Europe. We provide a univariate GARCH modeling of the stock market indices PX, BUX, and WIG from the CEE region and CROBEX, BELEX-15, and MBI from the SEE region. Additionally, we present a bivariate GARCH models in order to examine the volatility transmissions and spillovers from the European equity market to the equity markets in CEE and SEE. Our results suggest higher persistence of volatility in the CEE countries than in SEE countries, significant leverage effect more evident in the CEE region than in the SEE region, and high synchronization in the volatility between the CEE equity markets and the European equity market. The multivariate GARCH results reveal certain statistically significant but small volatility spillovers from the European equity market to the equity market in Hungary, Poland, Serbia and Republic of Macedonia. The CEE equity markets record higher conditional correlation coefficient than the SEE countries towards the European equity market. In general, the CEE equity markets are a relatively homogenous group in terms of volatility, while the SEE equity markets are a diversified group in terms of volatility with low synchronization and correlation with the...

This dissertation deals with issues of forecasting in financial markets. The first part of my dissertation is motivated by the observation that most parametric volatility models follow Engle's (1982) original idea of modelling the volatility of asset returns as a function of only past information. However, current returns are potentially quite informative for forecasting, yet are excluded from these models. The first and second chapters of this dissertation try to address this question from both a theoretical and an empirical perspective. The second part of this dissertation deals with the important issue of forecast evaluation and selection in unstable environments, where it is known that the existing methodology can generate spurious and potentially misleading results. In my third chapter, I develop a new methodology for forecast evaluation and selection in such an environment. In the first chapter, $\textit{Real-time GARCH}$, I propose a new parametric volatility model, which retains the simple structure of GARCH models, but models the volatility process as a mixture of past and current information as in the spirit of Stochastic Volatility (SV) models. This provides therefore a link between GARCH and SV models. I show that with this new model I am able to obtain better volatility forecasts than the standard GARCH-type models; improve the empirical fit of the data, especially in the tails of the distribution; and make the model faster in its adjustment to the new unconditional level of volatility. Further, the new model offers a much needed framework for specification testing as it nests the standard GARCH models. This chapter has been published in the $\textit{Journal of Financial Econometrics}$ (Smetanina E., 2017, Real-time GARCH, $\textit{Journal of Financial Econometrics}$, 15(4), 561-601.) In chapter 2, $\textit{Asymptotic Inference for Real-time GARCH(1,1) model}$, I investigate the asymptotic properties of the Gaussian Quasi-Maximum-Likelihood estimator (QMLE) for the Real-time GARCH(1,1) model, developed in the first chapter of this dissertation. I establish the ergodicity and $\beta$-mixing properties of the joint process for squared returns and the volatility process. I also prove strong consistency and asymptotic normality for the parameter vector at the usual $\sqrt{T}$ rate. Finally, I demonstrate how the developed theory can be viewed as a generalisation of the QMLE theory for the standard GARCH(1,1) model. In chapter 3, $\textit{Forecast Evaluation Tests in Unstable Environments}$, I develop a new methodology for forecast evaluation and selection in the situations where the relative performance between models changes over time in an unknown fashion. Out-of-sample tests are widely used for evaluating models' forecasts in economics and finance. Underlying these tests is often the assumption of constant relative performance between competing models, however this is invalid for many practical applications. In a world of changing relative performance, previous methodologies give rise to spurious and potentially misleading results, an example of which is the well-known ``splitting point problem''. I propose a new two-step methodology designed specifically for forecast evaluation in a world of changing relative performance. In the first step I estimate the time-varying mean and variance of the series for forecast loss differences, and in the second step I use these estimates to construct new rankings for models in a changing world. I show that the new tests have high power against a variety of fixed and local alternatives.

1997年7月2日泰國宣布放棄泰銖釘住美元而改採管理浮動匯率制度，乃掀起東南亞國家貨幣貶值的危機﹔同時這些國家股價亦遭滑鐵盧之災。東南亞國家金融風暴（亞洲金融風暴）的面貌是﹕幣值與股價同時大幅下滑。但1980年代末期，台灣股價暴漲與新台幣對美金匯率之升值幾乎是同時間發生的。新台幣升值幅度相當的大，且新台幣升值是漸進的，這予投機者以機會。亞洲金融風暴後，央行力守一美元匯兌新台幣在28.6元的匯率，動用近50億美元，打擊投機客，並同時調降存款準備率，放出1350億元的強力貨幣，予股市一股強力的活水。其目的是守住匯價，亦可使股價止跌。然而，市場的表現不是如此，1997年9月與10月股價卻直直下落。 　　研究期間為1990年1月1日至1999年1月31日止，並分成三子期間，以利比較風暴後兩市場互動關係，資料為發行量加權平均股價指數收盤價及新台幣對美元的銀行間平均收盤即期匯率之日資料；類股指數之研究期間為1997年7月1日至1999年1月31日止。運用GARCH模型進行實證分析，並利用一般化動差估計式(Generalized Method of Moments Estimator; GMM) 來估計所建構的迴歸式，以期達成下列目的﹕ 1. 確認台灣股票市場及外匯市場之互動結構關係。 2. 亞洲金融風暴後，其結構關係變化為何？ 3. 確認亞洲金融風暴後，台灣外匯市場對股票市場之各類股的互動結構 關係。 實証結果為： 1. 亞洲金融風暴前，有匯率對股市的報酬率波動外溢效果，亞洲金融風暴後，此報酬率波動外溢效果較風暴前減少，而其它影響股市的因素（非關匯率因素）反而逐漸增強。 2. 股匯市從單向關係（只有匯市影響股市）演變成雙向互動關係，且股市對匯市影響力增強。 3. 金融保險類、水泥窯製類及造紙類，此三類最不受匯市的影響。 4. 塑膠化工類、營造建材類、食品類及紡織纖維類，此四類受當期匯市報酬率的負影響，亦即新台幣升值，此四類股股價會上漲。而機電類股，則受滯延4期匯市報酬率的負影響。 5. 營造建材類，報酬率波動受到其他因素（非關匯率因素）影響很大。 目錄 第一章 緒論 第一節 研究背景與動機……………………………………… 1 第二節 研究目的……………………………………………… 6 第三節 研究限制……………………………………………… 7 第二章 相關理論探討 第一節 匯率的意義、種類及其影響因素…………………… 8 第二節 匯率變動對股票價格的影響………………………… 11 第三節 效率資本市場理論…………………………………… 14 第三章 相關文獻探討 第一節 國外相關文獻………………………………………… 16 第二節 國內相關文獻………………………………………… 19 第四章 研究方法 第一節 相關模型……………………………………………… 25 第二節 分析程序與方法……………………………………… 27 第五章 資料來源與處理 第一節 資料來源與研究期間………………………………… 34 第二節 資料處理……………………………………………… 34 第三節 基本統計分析………………………………………… 35 第六章 實証結果與分析 第一節 股市與匯市報酬率及報酬率波動外溢效果實証結果 46 第二節 各類股與匯市報酬率及報酬率波動外溢效果實証結果 ………………………………………………………… 72 第七章 結論與建議……………………………………………… 76 參考文獻…………………………………………………………… 108 表次 表5-1 股匯市報酬率之基本檢定統計量……………………… 37 表5-2 1997/7/1至1999/1/31各類股之基本檢定統計量…… 41 表6-1 1990/1/1至1999/1/31外溢效果（使用Pearson 交叉相關檢定）………………………………………… 48 表6-2 1990/1/1至1999/1/31外溢效果（使用GMM估計式）… 53 表6-3 1990/1/1至1994/12/3外溢效果（使用GMM估計式）… 58 表6-4 1995/1/1至1997/6/30外溢效果（使用GMM估計式）… 63 表6-5 1997/7/1至1999/1/31外溢效果（使用GMM估計式）… 68 表6-6 1997/7/1至1999/1/31外溢效果（匯市與金融保險類股 ，使用GMM估計式）……………………………………… 74 表6-7 1997/7/1至1999/1/31外溢效果（匯市與水泥窯製類股 ，使用GMM估計式）……………………………………… 78 表6-8 1997/7/1至1999/1/31外溢效果（匯市與塑膠化工類股 ，使用GMM估計式）……………………………………… 82 表6-9 1997/7/1至1999/1/31外溢效果（匯市與營造建材類股 ，使用GMM估計式）……………………………………… 86 表6-10 1997/7/1至1999/1/31外溢效果（匯市與機電類股 ，使用GMM估計式）……………………………………… 90 表6-11 1997/7/1至1999/1/31外溢效果（匯市與食品類股 ，使用GMM估計式）……………………………………… 94 表6-12 1997/7/1至1999/1/31外溢效果（匯市與造紙類股 ，使用GMM估計式）……………………………………… 98 表6-13 1997/7/1至1999/1/31外溢效果（匯市與紡織纖維類股 ，使用GMM估計式）……………………………………… 102

This paper discusses the performance of modeling and forecasting volatility ofdaily stock returns of A-shares in Shanghai Stock Exchange. The volatility is modeledby GARCH family models which are GARCH, EGARCH and GJR-GARCHmodels with three distributions, namely Gaussian distribution, student-t distributionand generalized error distribution (GED). In order to determine the performanceof forecasting volatility, we compare the models by using the Root MeanSquared Error (RMSE). The results show that the EGARCH models work so wellin most of daily stock returns and the symmetric GARCH models are better thanasymmetric GARCH models in this paper.

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Zugl.: Dortmund, Univ., Diss., 2003