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.

scholarly journals Use GARCH Models to Build a Econometric Model to Predict Average Daily Closing Prices of the Iraqi Stock Exchange for the Period 2013-2016

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 385-400
Author(s):  
Dr. Abed Ali Hamad ◽  
Dr. Ahmad Hussein Battal
Keyword(s):  
Stock Prices ◽  
Econometric Model ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Information Criterion ◽  
Garch Models ◽  
Financial Time ◽  
Egarch Model ◽  
Schwarz Criterion ◽  
Statistical Criteria

This research aims to build a standard model for the analysis and prediction of the average daily closing price fluctuations for companies registered in the Iraq Stock Exchange for the period 07/01/2013 to 30/06/2016, using the conditional generalized Heteroscedasticity Generalized Autoregressive (GARCH) models. As these models deal with the fluctuations that occur in the financial time series. The results of the analysis showed that the best model for predicting the volatility of average closing prices in the Iraq Stock Exchange is the EGARCH model (3,1), depending on the statistical criteria used in the preference between the models (Akaike Information Criterion, Schwarz Criterion), and these models can provide information for investors in order to reduce the risk resulting from fluctuations in stock prices in the Iraqi financial market.

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

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.

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