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

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9715.rar (76.81 KB) 本附件包括：

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2005-3-7 00:09:00 上传

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

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关键词：GARCH Models introduction troduction handout models 下载 models ARCH introduction handout

9717.rar (75.27 KB) 本附件包括：

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2005-3-7 00:21:00 上传

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

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• a smooth transition Arch model for asset return.pdf

2005-3-7 01:24:00 上传

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

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Re-examining the asymmetric predictability of conditional variances: The role of sudden changes in variance

Bradley T. Ewing^{a, 1,} and Farooq Malik^{b, , a}Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, USA ^bDepartment of Economics and Finance, Pennsylvania State University–Berks Campus, P.O. Box 7009, Reading, PA 19610-6009, USA Received 19 February 2004; accepted 21 October 2004. Available online 8 January 2005.

Abstract

The existence of “spillover effects” in financial markets is well documented and multivariate time series techniques have been used to study the transmission of conditional variances among large and small market value firms. Earlier research has suggested that volatility surprises to large capitalization firms are a reliable predictor of the volatility of small capitalization firms. A related line of research has examined how regime shifts in volatility may account for a considerable amount of the persistence in volatility. However, these studies have focused on univariate modeling and many have imposed regime changes on a priori grounds. This paper re-examines the asymmetry in the predictability of the volatilities of large versus small market value firms allowing for sudden changes in variance. Our method of analysis extends the existing literature in two important ways. First, recent advances in time series econometrics allow us to detect the time periods of sudden changes in volatility of large cap and small cap stocks endogenously using the iterated cumulated sums of squares (ICSS) algorithm. Second, we directly incorporate the information obtained on sudden changes in volatility in a Bivariate GARCH model of small and large cap stock returns. Our findings indicate that accounting for volatility shifts considerably reduces the transmission in volatility and, in essence, removes the spillover effects. We conclude that ignoring regime changes may lead one to significantly overestimate the degree of volatility transmission that actually exists between the conditional variances of small and large firms.

Keywords: Volatility; Capitalization; Bivariate GARCH; ICSS algorithm

JEL classification: F3

9886.rar (167.09 KB) 本附件包括：

• 1.pdf

2005-3-9 06:48:00 上传

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

Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997

Jeyanthi Karuppiah^a and Cornelis A. Los^{b, , a}Nanyang Business School, S3-01B-62, Nanyang Technological University, 639798, Singapore ^bDepartment of Finance, College of Business Administration and Graduate School of Management, Kent State University, Kent, OH 44242 0001, United States Available online 6 August 2004.

Abstract

Foreign exchange (FX) pricing processes are nonstationary: Their frequency characteristics are time dependent. Most do not conform to Geometric Brownian Motion (GBM), because they exhibit a scaling law with Hurst exponents between zero and 0.5 and fractal dimensions between 1.5 and 2. Wavelet multiresolution analysis (MRA), with Haar wavelets, is used to analyze these time and scale dependencies (self-similarity) of intraday Asian currency spot exchange rates. We use the ask and bid quotes of the currencies of eight Asian countries (Japan, Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, and Thailand) and, for comparison, of Germany for the crisis period May 1, 1998–August 31, 1997, provided by Telerate (U.S. dollar is the numéraire). Their time-scale-dependent spectra, which are localized in time, are observed in wavelet scalograms. The FX increments are characterized by the irregularity of their singularities. Their degrees of irregularity are measured by homogeneous Hurst exponents. These critical exponents are used to identify the global fractal dimension, relative stability, and long-term dependence, or long-term memory, of each Asian FX series. The invariance of each identified Hurst exponent is tested by comparing it at varying time and scale (frequency) resolutions. It appears that almost all investigated FX markets show antipersistent pricing behavior. The anchor currencies of the D-mark and Japanese Yen (JPY) are ultraefficient in the sense of being most antipersistent or “fast mean-reversing.” This is a surprising result because most financial analyst either assume neutral or persistent behavior in the financial markets, based on earlier research by Granger in the 1960s. This is a pedagogical paper explaining the most rational methodology for the identification of long-term memory in financial time series.

Keywords: Foreign exchange markets; Antipersistence; Long-term dependence; Multiresolution analysis; Wavelets; Time-scale analysis; Scaling laws; Irregularity analysis; Randomness; Asia

JEL classification: C22; F31; G14; G15; O53

9887.rar (1.51 MB) 本附件包括：

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2005-3-9 06:49:00 上传

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

Price behavior in China's wheat futures market

Wen DU^, and H. Holly WANG Department of Agricultural and Resource Economics, Washington State University, P.O. Box 646210, Pullman, WA 99164-6210, USA Accepted 3 March 2004. Available online 8 June 2004.

Abstract

Wheat futures prices have been playing an active role in China's agricultural price system since the contract's debut at the China Zhengzhou Commodity Exchange (CZCE). This paper analyzes CZCE wheat futures prices from 2000 to 2002 quantitatively. Results show the prices have unit root and time-varying variances. Alternative ARCH, GARCH, and ARMA models are fitted to the data resulting in the selection of AR(1), ARCH(2), and GARCH(1,1) models. Comparisons of these three models indicate that ARCH/GARCH describes the prices better than ARMA model, and GARCH further improves upon ARCH. Out-of-sample prediction performance also confirms this result.

Author Keywords: Futures price; China; GARCH; Wheat; Prediction

G13; Q14

Article Outline

1. Introduction

2. Models

2.1. ARMA model

2.2. Conditional heteroskedasticity models

3. Data description

4. Results

4.1. Selection of model specification

4.1.1. ARMA model

4.1.2. Conditional heteroskedasticity model

4.2. Model comparison

4.3. Forecasting

5. Conclusions

References

9888.rar (272.95 KB) 本附件包括：

• 3.pdf

2005-3-9 06:51:00 上传

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

Abstract

Recent empirical studies have shown that the chaotic behaviour and excess volatility of financial series are the result of interactions between heterogeneous investors. In our article, we propose verifying this hypothesis. Thus, we use the Chen et al. [Testing for non-linear structure in an artificial financial market. Working Paper, University of Bonn (2000).] model to show that the modification of the agents' homogeneity hypothesis can drive to stochastic chaotic evolution of price series. Then, through an econometric procedure, we try to identify the underlying process of the Paris Stock Exchange returns series (CAC40). To this end, we apply several different tests: (1) dealing with long-memory components derives from the fractional integration test of Geweke and Porter-Hudak (GPH) [J. Time Ser. Anal. 4 (1983) 221.] and (2) dealing with chaotic structures comes from the work on correlation dimension of Grassberger and Procaccia [Physica 9D (1983) 189.] and the Lyapunov exponents method of Gençay and Dechert [Physica D (1992) 142.]. Finally, we forecast the CAC40 returns series using the recent methods of Principal Components Regression (PCR) and Radial Basis Functions (RBF). We conclude with the implications of the presence of chaotic structures in stock markets and future research on ARCH and chaotic models' relationships.

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• 8.pdf

2005-3-9 06:53:00 上传

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

[此贴子已经被作者于2005-3-9 6:57:29编辑过]

A test for constant correlations in a multivariate GARCH model

Y. K. Tse^, Department of Economics, National University of Singapore, Singapore 119260, Singapore Received 1 April 1998; revised 1 December 1998; accepted 1 October 1999. Available online 28 July 2000.

Abstract

We introduce a Lagrange Multiplier (LM) test for the constant-correlation hypothesis in a multivariate GARCH model. The test examines the restrictions imposed on a model which encompasses the constant-correlation multivariate GARCH model. It requires the estimates of the constant-correlation model only and is computationally convenient. We report some Monte Carlo results on the finite-sample properties of the LM statistic. The LM test is compared against the Information Matrix (IM) test due to Bera and Kim (1996). The LM test appears to have good power against the alternatives considered and is more robust to nonnormality. We apply the test to three data sets, namely, spot-futures prices, foreign exchange rates and stock market returns. The results show that the spot-futures and foreign exchange data have constant correlations, while the correlations across national stock market returns are time varying.

Author Keywords: Constant correlation; Information matrix test; Lagrange multiplier test; Monte Carlo experiment; Multivariate conditional heteroscedasticity

JEL classification codes: C12

9890.rar (160.64 KB) 本附件包括：

• 11.pdf

2005-3-9 06:55:00 上传

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