scholarly journals Modeling the Behavior of Inflation Rate in Albania Using Time Series

2017 ◽  
Vol 13 (3) ◽  
pp. 7257-7263
Author(s):  
Rozana Liko
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Inflation Rate ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
One Year ◽  
Autoregressive Conditional Heteroscedasticity

In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation.   Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).

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 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.

scholarly journals ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches

2020 ◽  
Author(s):  
Raed Alzghool
Keyword(s):  
Exchange Rate ◽  
Kernel Estimation ◽  
Estimation Procedure ◽  
Conditional Variance ◽  
Estimation Methods ◽  
Garch Models ◽  
Unknown Parameters ◽  
Conditional Heteroscedasticity ◽  
Garch Processes ◽  
Autoregressive Conditional Heteroscedasticity

This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditional variance of process is unknown. The AQL estimation substitutes the variance and covariance by kernel estimation in QL. Reports of simulation outcomes, numerical cases, and applications of the methods to daily exchange rate series and weekly prices’ changes of crude oil are presented.

The Application of the Time Series Theory to Processing Data From the SBAS Receiver in Safety Mode

2012 ◽  
Author(s):  
M. Jonas
Keyword(s):  
Time Series ◽  
Statistical Tests ◽  
Point Of View ◽  
Garch Models ◽  
Autocorrelation Functions ◽  
Conditional Heteroscedasticity ◽  
New Methods ◽  
Processing Data ◽  
Wide Area Augmentation System ◽  
The Usa

Before satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) in the USA, and the European Geostationary Navigation Overlay Service (EGNOS), will be used in railway safety-related applications, it is necessary to determine reliability attributes of these systems as quality measures from the user’s point of view. It is necessary to find new methods of processing data from the SBAS system in accordance with strict railway standards. For this purposes data from the SBAS receiver with the Safety of Life Service was processed by means of the time series theory. At first, a basic statistic exploration analysis by means of histograms and boxplot graphs was done. Then correlation analysis by autocorrelation (ACF), and partial autocorrelation functions (PACF), was done. Statistical tests for the confirmation of non-stationarity, and conditional heteroscedasticity of time series were done. Engle’s ARCH test confirmed that conditional heteroscedasticity is contained. ARMA/GARCH models were constructed, and their residuals were analyzed. Autocorrelation functions and statistical tests of models residuals were done. The analysis implies that the models well cover the variance volatility of investigated time series and so it is possible to use the ARMA/GARCH models for the modeling of SBAS receiver outputs.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

scholarly journals Energy Security in Danger? A Comparative Analysis of Oil and Copper Supply

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 560
Author(s):  
Maciej Mróz
Keyword(s):  
Comparative Analysis ◽  
Crude Oil ◽  
Energy Security ◽  
New Technologies ◽  
Methodological Approach ◽  
Price Stability ◽  
Garch Models ◽  
Security Of Supply ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

This study aims to examine energy security in terms of crude oil and copper supply. While oil remains the leading energy commodity globally, copper is crucial for many new technologies, foremost for RES. Therefore, both oil and copper are extremely important for current and future energy security. This article contains a bivariate methodological approach to a comparative analysis of oil and copper supply: determining supply security with an Index of security of supply, and examines price stability with generalized autoregressive conditional heteroscedasticity (GARCH) models. This research provides evidence that there are many differences but also significant similarities between these two completely different commodities in terms of both supply security and price stability. Facing the future for RES, significant demand may cause a threat to energy security on a previously unknown scale. Therefore this instability, both supply- and price-related, appears to be the main threat to future energy security.

scholarly journals PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk)

2018 ◽  
Vol 7 (2) ◽  
pp. 110-118
Author(s):  
Dea Manuella Widodo ◽  
Sudarno Sudarno ◽  
Abdul Hoyyi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Step Function ◽  
Series Data ◽  
Model Data ◽  
Conditional Heteroscedasticity ◽  
Constant Variance ◽  
High Volatility ◽  
Autoregressive Conditional Heteroscedasticity

The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous).  In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH 

scholarly journals Non-linearities and GARCH Effects in the Emerging Stock Markets of South Asia

2007 ◽  
Vol 32 (3) ◽  
pp. 23-38 ◽  
Author(s):  
Raj S Dhankar ◽  
Madhumita Chakraborty
Keyword(s):  
Time Series ◽  
South Asia ◽  
Linear Dependence ◽  
Stock Markets ◽  
Theory And Practice ◽  
Future Price ◽  
Conditional Heteroscedasticity ◽  
Identical Distribution ◽  
Non Linear ◽  
Autoregressive Conditional Heteroscedasticity

Up to the beginning of the last decade, financial economics was dominated by linear paradigm, which assumed that economic time series conformed to linear models or could be wellapproximated by a linear model. However, there is increasing evidence that asset returns may be better characterized by a model which allows for non-linear behaviour. Though more efforts are now being directed towards the Asian stock markets in the light of their increasing importance to the investment world and the world economy, there is an extremely sparse literature, which utilizes recent advances in non-linear dynamics to examine the data generating process of the South-Asian stock markets. This study investigates the presence of non-linear dependence in three major markets of South Asia: India, Sri Lanka, and Pakistan. It was, however, realized that merely identifying non-linear dependence was not enough. Previous research has shown that the presence of nonlinear characteristics usually takes the form of ARCH/GARCH (Autoregressive Conditional Heteroscedasticity or Generalized Autoregressive Conditional Heteroscedasticity) type conditional heteroscedasticity. Keeping this in view, this study investigates whether the non-linear dependence is caused by predictable conditional volatility. It has been found that the simple GARCH (1, 1) model has fitted all the market return series adequately and accounted for the non-linearity found in the series. The findings reveal the following: The application of the BDS test developed by Brock, et al., (1996) strongly rejects the null hypothesis of independent and identical distribution of the return series as well as the linearly filtered return series for all the markets under study. With the possibility of linear dependence causing the rejection of independent and identical distribution (IID) being eliminated by linear filtering, the study also shows that non-stationarity of return series is also not a cause for non-IID behaviour by applying Augmented Dickey Fuller test and Phillips-Perron test. This implies the presence of non-linear dependence in the return series. For researchers in the developing countries, it is time to embrace the shift to non-linearity as it would provide a better understanding of the underlying dynamics of financial time series. However, the results are not necessarily inconsistent with efficient market hypothesis, simply because non-linearity does not essentially imply predictability as the future price changes can be predictable but only with a time horizon too short to allow for excess profits. The implications of non-linear dependence and presence of GARCH effects go beyond the issue of market efficiency. The common assumption of constant variance underlying the theory and practice of option pricing, portfolio optimization, and value-at-risk (VaR) calculations needs to be revised. If the assumed stochastic processes do not adequately depict the full complexity of the true generating processes, then any derivatives in question may be mis-priced.

Pengaruh Tingkat Inflasi, Tingkat Suku Bunga Bi, Dan Nilai Tukar Usd-Idr Terhadap Perubahan Harga Saham Sektor Perusahaan Manufaktur Di Indonesia

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Adi Cahya Stefanus ◽  
Robiyanto Robiyanto
Keyword(s):  
Exchange Rate ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Manufacturing Sector ◽  
Analysis Method ◽  
Conditional Heteroscedasticity ◽  
Macroeconomic Factors ◽  
Stock Price Index ◽  
Autoregressive Conditional Heteroscedasticity

The objective of this study is to find out how macroeconomic factors such as exchange rate, BI rate and inflation rate can affect the manufacturing  sector stock price index in IDX from 2011 until 2018. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) is used as the analysis method in this research to find the fittest model. The result, only exchange rate that no significant effect to manufacturing sector stock, price index, Inflation and BI rate have significant effect to manufacturing sector stock price index.

Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhi Sharma ◽  
Vaibhav Aggarwal ◽  
Miklesh Prasad Yadav
Keyword(s):  
Global Financial Crisis ◽  
Emerging Countries ◽  
Expected Returns ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Volatility Models ◽  
Non Linear ◽  
Forecasting Volatility ◽  
Autoregressive Conditional Heteroscedasticity

PurposeSeveral empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approachThe volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.FindingsThe findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implicationsThis study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/valueEarlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

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