[下载][讨论]Handout.Introduction To ARCH & GARCH Models

9891.rar (2.02 MB) 本附件包括：

• 15.pdf

2005-3-9 07:01:00 上传

[下载][讨论]Handout.Introduction To ARCH & GARCH Models

Abstract

The theories of chaos and complexity are presented as a wide-ranging new vision of the relationship between order and disorder, a new vision that is challenging many of the fundamental presuppositions of the older Newtonian world view of science. The implications of the new vision are explored in terms of their challenges to the methodological views widely espoused by capital market researchers in accounting, most notably with respect to the assumptions of linearity and predictability. Mandelbrot's early studies of economics and financial time series data, which provided many of the insights for his conception of fractals, are reviewed in terms of their challenges to the conceptual framework of the traditonal capital markets research paradigm and its extension to financial reporting issues. Contemporary studies which are reviving Mandelbrot's challenges are also reviewed, with the conclusion that they are weakening the intellectual hold of the tradtional capital markets paradigm and making it more susceptible to overthrow by a competing paradigm. Finally, an emerging new research program associated with the Santa Fe Institute (SFI) is reviewed. SFI researchers are studying financial markets as complex adaptive systems. Their preliminary findings are incompatible with the widely presumed theoretical linkage between financial reporting systems and economic efficiency, and they tend to undermine the traditonal rationale relating earnings to stock prices.

Abstract

The use of GARCH modeling in empirical finance has so far to a great extent been restricted to larger asset markets. This paper considers whether the GARCH framework can be used on a smaller, less liquid market. In particular, selected stocks on the Vancouver Stock Exchange, a smaller market in Canada, are examined. Modeling return volatility in the standard GARCH framework and returns as autoregressive fails to remove significant serial correlation in the mean. The results indicate that once the parameters are adjusted for non-synchronous trading effects, GARCH can also be successful in modeling stochastic volatility on smaller markets. Persistence in both the mean and variance are eliminated with these adjustments. In addition, for some stocks, volumes add explanatory power for explaining return volatility.

Author Keywords: GARCH; Non-synchronous trading

9892.rar (227.05 KB) 本附件包括：

• 14.pdf

2005-3-9 07:06:00 上传

[下载][讨论]Handout.Introduction To ARCH & GARCH Models

The use of GARCH models in VaR estimation

Timotheos Angelidis^{a, 1,} , Alexandros Benos^{a, ,} and Stavros Degiannakis^{b, 2, a}Department of Banking and Financial Management, University of Piraeus, 80, Karaoli & Dimitriou Street, Piraeus GR-185 34, Greece ^bDepartment of Statistics, Athens University of Economics and Business, 76, Patision Street, Athens GR-104 34, Greece Received 26 February 2004. Available online 4 November 2004.

Abstract

We evaluate the performance of an extensive family of ARCH models in modeling the daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.

Keywords: Value at Risk; GARCH estimation; Backtesting; Volatility forecasting; Quantile loss function

JEL classification: C22; C52; C53; G15

9893.rar (339.98 KB) 本附件包括：

• The use of GARCH models in VaR estimation.pdf

2005-3-9 07:12:00 上传

[下载][讨论]Handout.Introduction To ARCH & GARCH Models

Abstract

In this paper we examine the behaviour of stock returns in two emerging markets of China. These are the Shanghai and Shenzhen markets. It is found that both markets suffer from negative mean returns on Monday and Tuesday, but positive returns on Friday. In addition, we employ the bivariate GARCH model of Bollerslev [T. Bollerslev, Review of Economics and Statistics 72 (1990) 498–505] to capture the co-movements of stock returns between the markets. However, the information matrix test statistic does not support the null hypothesis of a constant conditional correlation in the stock returns.

9895.rar (55.7 KB) 本附件包括：

• Constant conditional correlation in a bivariate GARCH model.pdf

2005-3-9 07:21:00 上传

[下载][讨论]Handout.Introduction To ARCH & GARCH Models

Wavelet multiresolution analysis of high-frequency Asian FX rates这篇对我的帮助太大了

谢谢楼主啊

thanks

感谢楼主的无私分享，号召大家向楼主学习

还是激励机制管用！呵呵哈！谢楼主了！

谢谢共享!