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Relevant bibliographies by topics / Time-series analysis. Heteroscedasticity

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Academic literature on the topic 'Time-series analysis. Heteroscedasticity'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Time-series analysis. Heteroscedasticity"

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Ghosh, Himadri, G. Sunilkumar, and Prajneshu. "Mixture Nonlinear Time-Series Analysis : Modelling and Forecasting." Calcutta Statistical Association Bulletin 57, no. 1-2 (March 2005): 95–108. http://dx.doi.org/10.1177/0008068320050108.

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Gaussian mixture transition distribution (GMTD) models and mixture autoregressive (MAR) models are generally employed to describe those data sets that depict sudden bursts, outliers and flat stretches at irregular time epochs. In this paper , these two approaches are compared by considering weekly wholesale onion price data during April, 1998 to November, 2001. After eliminating trend, seasonal fluctuations are studied by fitting BoxJenkins airline model to residual series. To this end, null hypothesis of presence of nonseasonal and seasonal stochastic trends is tested by using OsboruChuiSmithBirchenhall (OCSB) auxiliary regression. Subsequently, appropriate filters in airline model for seasonal fluctuations are selected. Presence of autoregressive co nditional heteroscedasticity (ARCH) is tested by Naive Lagrange multiplier (Nave LM) test. Estimation of parameters is carric~d out using ExpectationMaximization (EM) algorithm and the best model is selected on the basis of Bayesian information criterion (BIC). Outofsample forecasting is performed for onestep and twostep ahead prediction by uaive approach, proposed by Wong and Li (2000). It is concluded that, for data under consideration, a threecomponent MAR model performs the best.

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Kokoszka, Piotr, Gregory Rice, and Han Lin Shang. "Inference for the autocovariance of a functional time series under conditional heteroscedasticity." Journal of Multivariate Analysis 162 (November 2017): 32–50. http://dx.doi.org/10.1016/j.jmva.2017.08.004.

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Hasanah, Primadina, Siti Qomariyah Nasir, and Subchan Subchan. "Gold Return Volatility Modeling Using Garch." Indonesian Journal of Mathematics Education 2, no. 1 (April 30, 2019): 20. http://dx.doi.org/10.31002/ijome.v2i1.1222.

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<p class="JRPMAbstractBodyEnglish">This research aims to resolve the heteroscedasticity problem in time series data by modeling and analyzing volatility the gold return using GARCH models. Heteroscedasticity means not the constant variance of residuals. The sample data is a return data from January 1, 2014 to September 23, 2016. The data analysis technique used is a stationary test, model identification, model estimation, diagnostic check, heteroscedasticity test, GARCH model estimation, and evaluation. The results showed that ARIMA (3,0,3)-GARCH (1.1) is the best model.</p>

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Kasse, Irwan, Andi Mariani, Serly Utari, and Didiharyono D. "Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model." JTAM (Jurnal Teori dan Aplikasi Matematika) 5, no. 1 (April 17, 2021): 1. http://dx.doi.org/10.31764/jtam.v5i1.3220.

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Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

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WU, EDMOND H. C., PHILIP L. H. YU, and W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS." International Journal of Neural Systems 16, no. 05 (October 2006): 371–82. http://dx.doi.org/10.1142/s0129065706000779.

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We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

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L., Wiri, Sibeate P.U., and Isaac D.E. "Markov Switching Intercept Vector Autoregressive Model (MSI(2)-VAR(2)) of Nigeria Inflation Rate and Crude Oil Price (Using Views 11)." African Journal of Mathematics and Statistics Studies 4, no. 2 (August 9, 2021): 88–100. http://dx.doi.org/10.52589/ajmss-vy1oocxz.

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To model inflation rate and crude oil prices, we used Markov Switching intercept heteroscedasticity Vector Autoregressive models. The data for this analysis was gathered from the Central Bank of Nigeria Statistical Bulletin monthly. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the period of expansion and contraction. The variable was stationary at first differences, the Augmented Dickey-Fuller test was used to screen for stationarity. The information criteria were used to test the number of regime and regime two were selected. Eight models were estimated for the MSI-VAR model. The best model was chosen based on the criterion of least information criterion, Markov-switching intercept heteroscedasticity – Vector Autoregressive model (MSIH(2)-VAR(2)) with AIC (8.596641) and SC (8.973119). The model was used to predict the series' values over a one-year cycle (12 months).

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Xie, Pin Jie, Chen Chen Huang, and Xian You Pan. "Characteristic Analysis of the Electricity Price Fluctuation: An Empirical Analysis Based on California’s Day-Ahead Market." Advanced Materials Research 1070-1072 (December 2014): 1534–40. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.1534.

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The paper deals with the Day-ahead Market of California between Apr. 1st, 1998 and Jan. 31, 2001 and divided each day to high-load period and low-load period, described the characteristics of electricity price fluctuation by ARCH models. The results showed that ARCH models under t-distribution matched the volatility of the sample series quite well, captured the series’ heteroscedasticity and the obvious peak and fat tail effectively; the total risk of the day-ahead market in the sample was high, the impacts from external information on the conditional variances was permanent and sustainable, the impacts could not disappear in a short time once the price were fluctuated; the daily mean price fluctuation and low-load period price fluctuation were not asymmetric; while high-load period were significantly asymmetrical.

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Orman, Turgut, and İlkay Dellal. "Cointegration Analysis of Exchange Rate Volatility and Agricultural Exports in Turkey: an Ardl Approch." Turkish Journal of Agriculture - Food Science and Technology 9, no. 6 (July 4, 2021): 1180–85. http://dx.doi.org/10.24925/turjaf.v9i6.1180-1185.4456.

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This study aims to reveal the impact of exchange rate volatility on agricultural exports of Turkey by using the Autoregressive Distributed Lag Model. While quarterly time series data covering period of 2001: Q1 to 2018: Q4 were used to carry out analyses, Exponential Generalized Autoregressive Conditional Heteroscedasticity (1.1) is used to acquire exchange rate volatility series. The research findings showed that agricultural export is cointegrated with exchange rate volatility, producer price index and real effective exchange rate. Furthermore, our findings indicate that increases in real effective exchange rate have a statistically significant positive influence on the export volume whereas exchange rate volatility has negative impact on it.

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Akbar, Dody, Sarce B. Awom, and Siti Aisah Bauw. "Pengaruh Pendidikan Dan Kesehatan Terhadap Pertumbuhan Ekonomi Di Kabupaten Teluk Bintuni Periode 2010-2018." JFRES: Journal of Fiscal and Regional Economy Studies 4, no. 1 (March 30, 2021): 8–14. http://dx.doi.org/10.36883/jfres.v4i1.45.

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This study aims to determine the effect of education and health on economic growth in Teluk Bintuni Regency for the 2010-2018 period. This type of research is quantitative research. This research uses time series data and secondary data collection techniques. Analysis of the data using the Coefficient of Determination Test Heteroscedasticity Test f Test t test. The results of this study show (X1) Education and (X2) Health have a positive and significant effect on (Y) Economic Growth.

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Sun, Kaiying. "Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model." International Journal of Financial Research 8, no. 3 (June 12, 2017): 154. http://dx.doi.org/10.5430/ijfr.v8n3p154.

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In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

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Dissertations / Theses on the topic "Time-series analysis. Heteroscedasticity"

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凌仕卿 and Shiqing Ling. "Stationary and non-stationary time series models with conditional heteroscedasticity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31236005.

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Ling, Shiqing. "Stationary and non-stationary time series models with conditional heteroscedasticity /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18611953.

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Kwan, Chun-kit. "Statistical inference for some financial time series models with conditional heteroscedasticity." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39794027.

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黃香 and Heung Wong. "Topics in conditional heteroscedastic time series modelling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31234513.

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Wong, Heung. "Topics in conditional heteroscedastic time series modelling /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14035492.

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Li, Guodong. "On some nonlinear time series models and the least absolute deviation estimation." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B3878239X.

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Li, Guodong, and 李國棟. "On some nonlinear time series models and the least absolute deviation estimation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B3878239X.

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Kwan, Chun-kit, and 關進傑. "Statistical inference for some financial time series models with conditional heteroscedasticity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39794027.

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Stockhammar, Pär. "Some Contributions to Filtering, Modeling and Forecasting of Heteroscedastic Time Series." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-38627.

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Heteroscedasticity (or time-dependent volatility) in economic and financial time series has been recognized for decades. Still, heteroscedasticity is surprisingly often neglected by practitioners and researchers. This may lead to inefficient procedures. Much of the work in this thesis is about finding more effective ways to deal with heteroscedasticity in economic and financial data. Paper I suggest a filter that, unlike the Box-Cox transformation, does not assume that the heteroscedasticity is a power of the expected level of the series. This is achieved by dividing the time series by a moving average of its standard deviations smoothed by a Hodrick-Prescott filter. It is shown that the filter does not colour white noise. An appropriate removal of heteroscedasticity allows more effective analyses of heteroscedastic time series. A few examples are presented in Paper II, III and IV of this thesis. Removing the heteroscedasticity using the proposed filter enables efficient estimation of the underlying probability distribution of economic growth. It is shown that the mixed Normal - Asymmetric Laplace (NAL) distributional fit is superior to the alternatives. This distribution represents a Schumpeterian model of growth, the driving mechanism of which is Poisson (Aghion and Howitt, 1992) distributed innovations. This distribution is flexible and has not been used before in this context. Another way of circumventing strong heteroscedasticity in the Dow Jones stock index is to divide the data into volatility groups using the procedure described in Paper III. For each such group, the most accurate probability distribution is searched for and is used in density forecasting. Interestingly, the NAL distribution fits best also here. This could hint at a new analogy between the financial sphere and the real economy, further investigated in Paper IV. These series are typically heteroscedastic, making standard detrending procedures, such as Hodrick-Prescott or Baxter-King, inadequate. Prior to this comovement study, the univariate and bivariate frequency domain results from these filters are compared to the filter proposed in Paper I. The effect of often neglected heteroscedasticity may thus be studied.

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Al, zghool Raed Ahmad Hasan. "Estimation for state space models quasi-likelihood and asymptotic quasi-likelihood approaches /." Access electronically, 2008. http://ro.uow.edu.au/theses/91.

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Books on the topic "Time-series analysis. Heteroscedasticity"

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Estimation in conditionally heteroscedastic time series models. Berlin: Springer, 2005.

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Zaffaroni, Paolo. Contemporaneous aggregation of GARCH processes. Roma: Banca d'Italia, 2002.

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Luger, Richard. Exact non-parametric tests for a random walk with unknown drift under conditional heteroscedasticity. Ottawa, Ont: Bank of Canada, 2001.

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Luger, Richard. Exact non-parametric tests for a random walk with unknown drift under conditional heteroscedasticity. Ottawa, Ont: Bank of Canada, 2001.

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Sattarhoff, Cristina. Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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Sattarhoff, Cristina. Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2012.

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Book chapters on the topic "Time-series analysis. Heteroscedasticity"

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Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. "Autoregressive Conditional Heteroscedasticity." In Introduction to Modern Time Series Analysis, 281–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33436-8_8.

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"Time Series Models Of Heteroscedasticity." In Time Series Analysis, 277–318. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75959-3_12.

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Mukherjee, Kanchan. "A Review of Robust Estimation under Conditional Heteroscedasticity." In Time Series Analysis: Methods and Applications, 123–54. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-444-53858-1.00006-5.

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Yu, Philip L. H., Edmond H. C. Wu, and W. K. Li. "Financial Data Mining Using Flexible ICA-GARCH Models." In Dynamic and Advanced Data Mining for Progressing Technological Development, 255–72. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-908-3.ch011.

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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.

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Conference papers on the topic "Time-series analysis. Heteroscedasticity"

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Jonas, M. "The Application of the Time Series Theory to Processing Data From the SBAS Receiver in Safety Mode." In 2012 Joint Rail Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/jrc2012-74033.

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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.

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Li, Qianru, Christophe Tricaud, Rongtao Sun, and YangQuan Chen. "Great Salt Lake Surface Level Forecasting Using FIGARCH Model." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34909.

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedasticity should be included in time series with high volatility.

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