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Relevant bibliographies by topics / Conditional heteroscedasticity

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Academic literature on the topic 'Conditional heteroscedasticity'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Conditional heteroscedasticity"

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Bollerslev, Tim, and Eric Ghysels. "Periodic Autoregressive Conditional Heteroscedasticity." Journal of Business & Economic Statistics 14, no. 2 (1996): 139. http://dx.doi.org/10.2307/1392425.

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Bollerslev, Tim, and Eric Ghysels. "Periodic Autoregressive Conditional Heteroscedasticity." Journal of Business & Economic Statistics 14, no. 2 (1996): 139–51. http://dx.doi.org/10.1080/07350015.1996.10524640.

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Platanios, Emmanouil A., and Sotirios P. Chatzis. "Gaussian Process-Mixture Conditional Heteroscedasticity." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 5 (2014): 888–900. http://dx.doi.org/10.1109/tpami.2013.183.

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Xiao, Zhijie, and Roger Koenker. "Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models." Journal of the American Statistical Association 104, no. 488 (2009): 1696–712. http://dx.doi.org/10.1198/jasa.2009.tm09170.

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Dissanayake, Gnanadarsha, and Shelton Peiris. "Generalized Fractional Processes with Conditional Heteroscedasticity." Sri Lankan Journal of Applied Statistics 12 (December 2, 2012): 1. http://dx.doi.org/10.4038/sljastats.v12i0.4964.

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Wu, JianHong, and LiXing Zhu. "Diagnostic checking for conditional heteroscedasticity models." Science China Mathematics 53, no. 10 (2010): 2773–90. http://dx.doi.org/10.1007/s11425-010-3152-2.

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Mukherjee, Kanchan. "Generalized R-estimators under conditional heteroscedasticity." Journal of Econometrics 141, no. 2 (2007): 383–415. http://dx.doi.org/10.1016/j.jeconom.2006.10.002.

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Rossetti, Nara, Marcelo Seido Nagano, and Jorge Luis Faria Meirelles. "A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries." Journal of Economics, Finance and Administrative Science 22, no. 42 (2017): 99–128. http://dx.doi.org/10.1108/jefas-02-2017-0033.

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Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

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Otto, Philipp, Wolfgang Schmid, and Robert Garthoff. "Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity." Spatial Statistics 26 (August 2018): 125–45. http://dx.doi.org/10.1016/j.spasta.2018.07.005.

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Wong, C. "Testing for threshold autoregression with conditional heteroscedasticity." Biometrika 84, no. 2 (1997): 407–18. http://dx.doi.org/10.1093/biomet/84.2.407.

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Dissertations / Theses on the topic "Conditional heteroscedasticity"

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Aguilar, Mike Renault Eric. "Essays in financial econometrics GMM and conditional heteroscedasticity /." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1869.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2008.<br>Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Economics." Discipline: Economics; Department/School: Economics.

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Odusami, Babatunde Olatunji. "A Study of Conditional Volatilities in Financial Markets using Generalized Conditional Heteroscedasticity Jump Models." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/1049.

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In this manuscript, I investigate the time-varying volatilities and co-volatilities in the fixed income and equities market using jump augmented stochastic volatility models. The results highlights that the fact that jumps are inherent in financial markets and have implications for the dynamics of volatilities and co-volatilities of financial assets over time. Jump augmented models provide a superior description of instantaneous market conditions and a promising avenue for future research in areas of asset pricing, portfolio selection, and risk management.

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Henry, Marc. "Long memory in time series : semiparametric estimation and conditional heteroscedasticity." Thesis, London School of Economics and Political Science (University of London), 1999. http://etheses.lse.ac.uk/1581/.

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This dissertation considers semiparametric spectral estimates of temporal dependence in time series. Semiparametric frequency domain methods rely on a local parametric specification of the spectral density in a neighbourhood of the frequency of interest. Therefore, such methods can be applied to the analysis of singularities in the spectral density at frequency zero to identify long memory. They can also serve as the basis for the estimation of regular parts of the spectrum. One thereby avoids inconsistency that might arise from misspecification of dynamics at frequencies other than the frequency under focus. In case of long financial time series, the loss of efficiency with respect to fully parametric methods (or full band estimates) may be offset by the greater robustness properties. However, if semiparametric frequency domain methods are to be valid tools for inference on financial time series, they need to allow for conditional heteroscedasticity which is now recognized as a dominant feature of asset returns. This thesis provides a general specification which allows the time series under investigation to exhibit this type of behaviour. Two statistics are considered. The weighted periodogram statistic provides asymptotically normal point estimates of the spectral density at zero frequency for weakly dependent processes. The local Whittle (or local frequency domain maximum likelihood) estimate provides asymptotically normal estimates of long memory in possibly strongly dependent processes. The asymptotic results hold irrespective of the behaviour of the spectral density at non zero frequencies. The asymptotic variances are identical to those that obtain under conditional homogeneity in the distribution of the innovations to the observed process. In semiparametric frequency domain estimation, the choice of bandwidth is crucial. Indeed, it determines the asymptotic efficiency of the procedure. Optimal choices of bandwidth are derived, balancing asymptotic bias and asymptotic variance. Feasible versions of these optimal band-widths are proposed, and their performance is assessed in an extensive Monte Carlo study where the innovations to the observed process are simulated under numerous parametric submodels of the general specification, covering a wide range of persistence properties both in the levels and in the squares of the observed process. The techniques described above are applied to the analysis of temporal dependence and persistence in intra-day foreign exchange rate returns and their volatilities. While no strong indication of returns predictability is found in the former, a clear pattern arises in the latter, indicating that intra-day exchange rate returns are well described as martingale differences with weakly stationary and fractionally cointegrated long memory volatilities.

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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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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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Blazevic, Darko, and Fredrik Marcusson. "Volatility Evaluation Using Conditional Heteroscedasticity Models on Bitcoin, Ethereum and Ripple." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252570.

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This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.<br>Den här rapporten undersöker om bättre anpassade volatilitetsmodeller leder till bättre prognoser av volatiliteten för olika heteroskedastiska modeller, i detta fall ARCH, GARCH, EGARCH och GJR-GARCH, med olika innovationsdistributioner. Modellerna anpassas för Bitcoin, Ethereum och Ripple under 2016-01-01 till 2017-01-01 och därefter görs endagsprognoser under perioden 2018-01-01 till 2018-12-31. Studien undersöker tre olika teman bestående av modellstruktur, komplexitet av modeller och relationen mellan en god passning och god prognos. För att evaluera passningen för modellerna används AIC och BIC och för prognoserna används förlustfunktionerna MSE, MAE och R2log som evaluering av prognosen mot den valda volatilitetsproxyn Parkinson. Resultaten visar på att innovationsdistributioner med tyngre svansar än normalfördelningen generellt leder till bättre passning, medan man för prognoserna inte kan dra en sådan slutsats. Vidare visas det att GARCH-modellerna påvisade bättre resultat både för passning och prognoser än dem mer simpla ARCH-modellerna. För Ethereum var samma modell bäst för samtliga förlustfunktioner medan Bitcoin visar olika modeller för respektive förlustfunktion. För Ripple kan inte heller någon generalitet påvisas mellan passning och prognoser.

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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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Books on the topic "Conditional heteroscedasticity"

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Bera, Anil K. On the formulation of a general structure for conditional heteroskedasticity. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1989.

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Bera, Anil K. Conditional and unconditional heteroscedasticity in the market model. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1986.

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Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. National Bureau of Economic Research, 1994.

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Hurn, A. S. Noise traders, imitation, and conditional heteroscedasticity in asset returns: A theoretical framework. Glasgow University Department of Political Economy, 1994.

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Kodaira, Ryoichi. Autoregressive conditional heteroscedasticity in the Japanese short-term money market rates (Gensaki rates). typescript, 1994.

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Robinson, P. M. Long and short memory conditional heteroscedasticity in estimating the memory parameter of levels. Suntory Centre, 1998.

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

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

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Ho, M. S. Multivariate tests of a continuous time equilibrium arbitrage pricing theory with conditional heteroscedasticity and jumps. University of Cambridge, Department of Applied Economics, 1992.

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

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Book chapters on the topic "Conditional heteroscedasticity"

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

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Čížek, Pavel. "Modelling conditional heteroscedasticity in nonstationary series." In Statistical Tools for Finance and Insurance. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18062-0_3.

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Chang, Bao Rong. "Novel Prediction Approach – Quantum-Minimum Adaptation to ANFIS Outputs and Nonlinear Generalized Autoregressive Conditional Heteroscedasticity." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_113.

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"Conditional Heteroscedasticity:." In Financial Econometrics. Princeton University Press, 2018. http://dx.doi.org/10.2307/j.ctv9hvt42.9.

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"Conditional heteroscedasticity models." In Diagnostic Checks in Time Series. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203485606.ch6.

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"Conditional heteroscedasticity models." In Diagnostic Checks in Time Series. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203485606-7.

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"Conditional Heteroscedasticity and Applications in Finance." In Forecasting with Exponential Smoothing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-71918-2_19.

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Alzghool, Raed. "ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93726.

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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.

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

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Geweke, John. "Exact inference in models with autoregressive conditional heteroscedasticity." In Dynamic Econometric Modeling. Cambridge University Press, 1988. http://dx.doi.org/10.1017/cbo9780511664342.006.

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Conference papers on the topic "Conditional heteroscedasticity"

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Chatzis, Sotirios P. "Recurrent latent variable conditional heteroscedasticity." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952649.

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Ke, Jinchuan, Rong Zhang, and Zhe Chen. "Auto-Regressive Conditional Heteroscedasticity Analysis of Portfolio Volatilities." In 2009 International Conference on Information and Financial Engineering, ICIFE. IEEE, 2009. http://dx.doi.org/10.1109/icife.2009.36.

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Chen, Hao, Jie Wu, and Shan Gao. "A Study of Autoregressive Conditional Heteroscedasticity Model in Load Forecasting." In 2006 International Conference on Power System Technology. IEEE, 2006. http://dx.doi.org/10.1109/icpst.2006.321620.

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"Modeling the Conditional Heteroscedasticity and Leverage Effect in the Chinese stock markets." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d2.yin.

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Sin, Kuek Jia, Chin Wen Cheong, and Tan Siow Hooi. "Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980990.

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Střelec, Luboš, and Milan Stehlík. "Robust testing for normality of error terms with presence of autocorrelation and conditional heteroscedasticity." In ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences. Author(s), 2017. http://dx.doi.org/10.1063/1.4972747.

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Ilbeigi, Mohammad, Alireza Joukar, and Baabak Ashuri. "Modeling and Forecasting the Price of Asphalt Cement Using Generalized Auto Regressive Conditional Heteroscedasticity." In Construction Research Congress 2016. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479827.071.

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Chi Xie and Lin Yao. "Portfolio Value-at-Risk estimating on markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model." In 2012 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2012. http://dx.doi.org/10.1109/iciii.2012.6339654.

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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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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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