This study mainly explores the declaration effect of Capital Reduction on stock price. The samples will be those listed companies which have declared the activity of Capital Reduction, and the sample period is from March 1, 2005 to August 31, 2007. We use multiple factors model (market return, stock volume variance, the net buy-and-sell ratio of foreign investment) with ADF, Ljung-Box Q and Ljung-Box Q2 to build our model, and then apply the method of event study to explain the declaration effect of Capital Reduction. As a result, this study exhibits Capital Reduction can not offer abnormal returns during the period of three days before the declaration and three days after.

In financial econometrics the modeling of asset return series is closely related to the estimation of the corresponding conditional densities. One reason why one is interested in the whole conditional density and not only in the conditional mean, is that the conditional variance can be interpreted as a measure of time-dependent volatility of the return series. In fact, the modeling and the prediction of volatility is one of the central topics in asset pricing. In this paper we propose to estimate conditional densities semi-nonparametrically in a neural network framework. Our recurrent mixture density networks realize the basic ideas of prominent GARCH approaches but they are capable of modeling any continuous conditional density also allowing for time-dependent higher-order moments. Our empirical analysis on daily DAX data shows that out-of-sample volatility predictions of the neural network model are superior to predictions of GARCH models in that they have a higher correlation with implied volatilities. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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One of the most challenging topics in financial time series analysis is the modeling of conditional variances of asset returns. Although conditional variances are not directly observable there are numerous approaches in the literature to overcome this problem and to predict volatilities on the basis of historical asset returns. The most prominent approach is the class of GARCH models where conditional variances are governed by a linear autoregressive process of past squared returns and variances. Recent research in this field, however, has focused on modeling asymmetries of conditional variances by means of non-linear models. While there is evidence that such an approach improves the fit to empirical asset returns, most non-linear specifications assume conditional normal distributions and ignore the importance of alternative models. Concentrating on the distributional assumptions is, however, essential since asset returns are characterized by excess kurtosis and hence fat tails that cannot be explained by models with suffcient heteroskedasticity. In this paper we take up the issue of returns' distributions and contrast it with the specification of non-linear GARCH models. We use daily returns for the Dow Jones Industrial Average over a large period of time and evaluate the predictive power of different linear and non-linear volatility specifications under alternative distributional assumptions. Our empirical analysis suggests that while non-linearities do play a role in explaining the dynamics of conditional variances, the predictive power of the models does also depend on the distributional assumptions. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The general theme of this thesis is theoretical properties and evaluation of volatility models. The thesis consists of four papers. In the first chapter the moment structure of the EGARCH model is derived. The second chapter contains new results on the A-PARCH model. The third chapter is about certain stylized facts of financial time series and the idea is to investigate how well the GARCH, EGARCH and ARSV models are able to reproduce these characteristics. The fourth chapter is about evaluating the EGARCH model. A more detailed overview of the chapters follows next.  In Chapter 1 we derive the condition for the existence of moments, the expression for the kurtosis and the one for the autocorrelation function of positive powers of the absolute-valued observations for the EGARCH model. The results of the paper are useful, for example, if we want to compare the EGARCH model with the GARCH model. They reveal certain differences in the moment structure between these models. While the autocorrelations of the squared observations decay exponentially in the GARCH model, the decay rate is not exponential in the EGARCH model. While for the GARCH model the conditions for parameters allowing the existence of higher-order moments become more and more stringent for each even moment this is not the case for the EGARCH model. The explicit expressions of the autocorrelation structure of the positive powers of the absolute-valued observations of the model are particularly important in the considerations of Chapter 3 of the thesis.The A-PARCH model contains a particular positive power parameter. By letting the power parameter approach zero, the A-PARCH family of models also includes a family of EGARCH models as a special case. In Chapter 2 we derive the autocorrelation function of squared and logarithmed observations for the A-PARCH family of models and show that it may be obtained as a limiting case of a general power ARCH (GPARCH) model. An interesting thing to notice is that the autocorrelation structure of this GPARCH process, if it exists, is exponential, and that this property is retained at the limit as the power parameter approaches zero, which means that the autocorrelation function of the process of logarithms of squared observations also decay exponentially. While this is true for the logarithmed squared observations of an EGARCH process it cannot simultaneously be true for the untransformed observations defined by these processes as we in Chapter 1 have demonstrated.In order to explain the role of the power parameter we present a detailed analysis of how the autocorrelation functions of the squared observations differ across members of the GPARCH models. In an empirical example we also show that the estimated power parameter considerably improves the correspondence between the estimated autocorrelations on the one hand and the autocorrelation estimates from the model on the other. Financial time series seem to share a number of characteristic features, sometimes called stylized facts. Given a set of stylized facts, one may ask the following question: "Have popular volatility models been parameterized in such a way that they can accommodate and explain the most common stylized facts visible in the data?" Models for which the answer is positive may be viewed as suitable for practical use. In Chapter 3, possible answers to this question for the three popular models of volatility, GARCH, EGARCH and ARSV models are investigated. Model evaluation is an important part of modelling not only for the conditional mean models but for the conditional variance specifications as well. In Chapter 4 we consider misspecification tests for an EGARCH model. We derive two new misspecification tests for an EGARCH model. Because the tests of an EGARCH model against a higher-order EGARCH model and testing parameter constancy are parametric, the alternative may be estimated if the null hypothesis is rejected. This is useful for a model builder who wants to find out possible weakness of estimated specification. Furthermore, we investigate various ways of testing the EGARCH model against GARCH ones as another check of model adequacy. An empirical example shows that there is substantial evidence for parameter nonconstancy in daily return series of the Stockholm Stock Exchange. / Diss. Stockholm : Handelshögsk., 2004

This thesis is concerned with the modelling of the term structure of interest rates, with a particular focus on empirical aspects of the modelling. In this thesis, we explore the ??-parameterised (?? being the length of time to maturity) term structure of interest rates, corresponding to the traditional T-parameterised (T being the time of maturity) term structure of interest rates. The constructions of Australian yield curves are illustrated using generic yield curves produced by the Reserve Bank of Australia based on bonds on issue and by constructed yield curves of the Commonwealth Bank of Australia derived from swap rates. The data used to build the models is Australian Treasury yields from January 1996 to December 2001 for maturities of 1, 2, 3, 5 and 10 years, and the second data used to validate the model is Australian Treasury yields from July 2000 to April 2004 for maturities of all years from 1-10. Both data were supplied by the Reserve Bank of Australia. Initially, univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH), with models of individual yield increment time series are developed for a set of fixed maturities. Then, a multivariate Matrix-Diagonal GARCH model with multivariate asymmetric t-distribution of the term structure of yield increments is developed. This model captures many important properties of financial data such as volatility mean reversion, volatility persistency, stationarity and heavy tails. There are two innovations of GARCH modelling in this thesis: (i) the development of the Matrix-Diagonal GARCH model with multivariate asymmetric t-distribution using meta-elliptical distribution in which the degrees of freedom of each series varies with maturity, and the estimation is given; (ii) the development of a GARCH model of term structure of interest rates (TS-GARCH). The TS-GARCH model describes the parameters specifying the GARCH model and the degrees of freedom using simple smooth functions of time to maturity of component series. TS-GARCH allows an empirical description of complete interest rate yield curve increments therefore allowing the model to be used for interpolation to additional maturity beyond those used to construct the model. Diagnostics of TS-GARCH model are provided using Australian Treasury bond yields.

This thesis is concerned with the modelling of the term structure of interest rates, with a particular focus on empirical aspects of the modelling. In this thesis, we explore the ??-parameterised (?? being the length of time to maturity) term structure of interest rates, corresponding to the traditional T-parameterised (T being the time of maturity) term structure of interest rates. The constructions of Australian yield curves are illustrated using generic yield curves produced by the Reserve Bank of Australia based on bonds on issue and by constructed yield curves of the Commonwealth Bank of Australia derived from swap rates. The data used to build the models is Australian Treasury yields from January 1996 to December 2001 for maturities of 1, 2, 3, 5 and 10 years, and the second data used to validate the model is Australian Treasury yields from July 2000 to April 2004 for maturities of all years from 1-10. Both data were supplied by the Reserve Bank of Australia. Initially, univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH), with models of individual yield increment time series are developed for a set of fixed maturities. Then, a multivariate Matrix-Diagonal GARCH model with multivariate asymmetric t-distribution of the term structure of yield increments is developed. This model captures many important properties of financial data such as volatility mean reversion, volatility persistency, stationarity and heavy tails. There are two innovations of GARCH modelling in this thesis: (i) the development of the Matrix-Diagonal GARCH model with multivariate asymmetric t-distribution using meta-elliptical distribution in which the degrees of freedom of each series varies with maturity, and the estimation is given; (ii) the development of a GARCH model of term structure of interest rates (TS-GARCH). The TS-GARCH model describes the parameters specifying the GARCH model and the degrees of freedom using simple smooth functions of time to maturity of component series. TS-GARCH allows an empirical description of complete interest rate yield curve increments therefore allowing the model to be used for interpolation to additional maturity beyond those used to construct the model. Diagnostics of TS-GARCH model are provided using Australian Treasury bond yields.

Zugl.: Karlsruhe, Univ., Diss., 2004

Zugl.: Fribourg, Univ., Diss., 2008 u.d.T.: Ardia, David: Bayesian estimation of single regime and regime switching GARCH models / Lizenzpflichtig

Zugl.: Braunschweig, Techn. Univ., Diss., 2009