scholarly journals WHY DOES THE STANDARD GARCH(1, 1) MODEL WORK WELL?

2007 ◽  
Vol 18 (07) ◽  
pp. 1223-1230 ◽  
Author(s):  
G. R. JAFARI ◽  
A. BAHRAMINASAB ◽  
P. NOROUZZADEH
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Garch Models ◽  
Time Interval ◽  
Optimal Parameters ◽  
Financial Time ◽  
Wide Range ◽  
Stochastic Data ◽  
New Criterion ◽  
Infinite Range

The AutoRegressive Conditional Heteroskedasticity (ARCH) and its generalized version (GARCH) family of models have grown to encompass a wide range of specifications, each of them is designed to enhance the ability of the model to capture the characteristics of stochastic data, such as financial time series. The existing literature provides little guidance on how to select optimal parameters, which are critical in efficiency of the model, among the infinite range of available parameters. We introduce a new criterion to find suitable parameters in GARCH models by using Markov length, which is the minimum time interval over which the data can be considered as constituting a Markov process. This criterion is applied to various time series and its results support the known idea that GARCH(1, 1) model works well.

scholarly journals Forecasting the volatility of Financial Time Series by Tree Ensembles

2019 ◽  
Vol 12 (3) ◽  
pp. 82-89
Author(s):  
O. S. Vidmant
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Capital Investment ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Time Interval ◽  
Short Term ◽  
Computing Power ◽  
Financial Time

The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. The article discusses the possibility of improving the efficiency of calculations in predicting the volatility by the models of tree ensembles using various methods of data analysis. As the key points of efficiency growth, the author studied the possibility of aggregation of financial time series data using several methods of calculation and prediction of variance: Standard, EWMA, ARCH, GARCH, and also analyzed the possibility of simplifying the calculations while reducing the correlation between the series. The author demonstrated the application of calculation methods on the basis of an array of historical price data (Open, High, Low, Close) and volume indicators (Volumes) of futures trading on the RTS index with a five-minute time interval and an annual set of historical data. The proposed method allows to reduce the cost of computing power and time for data processing in the analysis of short-term positions in the financial markets and to identify risks with a certain level of confidence probability.

Financial Data Mining Using Flexible ICA-GARCH Models

2010 ◽  
pp. 255-272
Author(s):  
Philip L.H. Yu ◽  
Edmond H.C. Wu ◽  
W.K. Li
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Risk Management ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Independent Component ◽  
Garch Models ◽  
Financial Time

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.

scholarly journals Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-149
Author(s):  
G.R. Pasha ◽  
Tahira Qasim ◽  
Muhammad Aslam
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Gjr Model ◽  
Forecasting Volatility ◽  
Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

scholarly journals Stable GARCH models for financial time series

1995 ◽  
Vol 8 (5) ◽  
pp. 33-37 ◽  
Author(s):  
A.K. Panorska ◽  
S. Mittnik ◽  
S.T. Rachev
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time

DETECTING AND MODELING TAIL DEPENDENCE

2004 ◽  
Vol 07 (03) ◽  
pp. 269-287 ◽  
Author(s):  
FABIO BELLINI ◽  
GIANNA FIGÀ-TALAMANCA
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Financial Data ◽  
Garch Models ◽  
Serial Dependence ◽  
Simple Method ◽  
Financial Time ◽  
Empirical Investigations

The aim of this work is to develop a nonparametric tool for detecting dependence in the tails of financial data. We provide a simple method to locate and measure serial dependence in the tails, based on runs tests. Our empirical investigations on many financial time series reveal a strong departure from independence for daily logreturns, which is not filtered out by usual Garch models.

scholarly journals Volatility of cryptocurrencies

Notitia ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 13-23
Author(s):  
Branimir Cvitko Cicvarić
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Information Criterion ◽  
Garch Models ◽  
Arch Model ◽  
Model Estimate ◽  
Conditional Heteroscedasticity ◽  
Financial Time ◽  
Egarch Model ◽  
Autoregressive Conditional Heteroscedasticity

Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.

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