4 GARCH Models 7 4.1 Basic GARCH Specifications . . . . . . . . . . . . . . . . . . . 8 4.2 Diagnostic Checking . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Regressors in the Variance Equation . . . . . . . . . . . . . . . 12 4.4 The GARCH–M Model . . . . . . . . . . . . . . . . . . . . . . 12 4.5 The Threshold GARCH (TARCH) Model . . . . . . . . . . . . 12 4.6 The Exponential GARCH (EG...

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the ...

This is an early version of a set of notes for the course on Time Series Analysis offered at Lund University. Any and all comments and suggestions are most welcome and appreciated. Glossary and Abbreviations 1-D, 2-D, etc. One-dimensional, two-dimensional, etc. ACF Auto-covariance function AR Autoregressive ARIMA Integrated ARMA ARMA Autoregressive moving average ARMAX ARMA with exogenous input...

This work characterized mega plasmid pSY153-MDR, carrying blaIMP-45 and armA, from a multidrug-resistant (MDR) Pseudomonas putida isolate from the urine of a cerebral infarction patient in China. The backbone of pSY153-MDR was closely related to Pseudomonas plasmids p12969-DIM, pOZ176, pBM413, pTTS12, and pRBL16, and could not be assigned to any of the known incompatibility groups. The accessor...

In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete imag...

In recent work, ARMARKOV representations have been proposed as an extension of ARMA representations of nite-dimensional linear time-invariant systems. ARMARKOV representations have the same form as ARMA representations, but explicitly involve Markov parameters. This paper generalizes ARMA least-squares time-domain identi cation to ARMARKOV representations. The ARMARKOV/least-squares identi cati...

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could distinguished. Motivated this idea, our study aims to distinguish between financial volatility. We firstly modeled volatility using Fractionally Integrated Exponential GARCH (FIEGARCH) Markov Regime-Switching EGARCH ...