Relevant bibliographies by topics / Financial time series model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Financial time series model'

Author: Grafiati
Published: 20 February 2023

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Journal articles on the topic "Financial time series model"

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Lupekesa, Chipasha Salome Bwalya, Johannes Tshepiso Tsoku, and Lebotsa Daniel Metsileng. "Econometric Modelling of Financial Time Series." International Journal of Management, Entrepreneurship, Social Science and Humanities 5, no. 2 (December 30, 2022): 52–70. http://dx.doi.org/10.31098/ijmesh.v5i2.622.

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This paper examines the relationship between assets, capital, liabilities and liquidity in South Africa using the Johansen cointegration analysis and the GARCH model using times data for the period 02/2005 to 06/2018. The results obtained from the study suggests that the time series are integrated of order one, I(1). The findings from the Johansen cointegration test indicated that the variables have a long run cointegrating relationship. Furthermore, the results from the GARCH model revealed that the estimated model has statistically significant coefficients at 5% significance level. Additionally, results revealed that assets have a positive relationship with capital, liabilities and liquidity. This implies that a percentage increase in assets will result to a percentage increase in capital, liabilities and liquidity. The results also revealed that shocks decay quickly in the future and that the conditional variance is explosive. The diagnostic tests revealed that the estimated models show the characteristics of a well specified model. The recommendations for future studies were formulated. Keywords: ARCH model; Cointegration; Financial time series; GARCH model; VECM; Volatility
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Ince, Huseyin, and Fatma Sonmez Cakir. "Analysis of financial time series with model hybridization." Pressacademia 4, no. 3 (September 30, 2017): 331–41. http://dx.doi.org/10.17261/pressacademia.2017.700.

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Jiang, Hui, and Zhizhong Wang. "GMRVVm–SVR model for financial time series forecasting." Expert Systems with Applications 37, no. 12 (December 2010): 7813–18. http://dx.doi.org/10.1016/j.eswa.2010.04.058.

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Feng, Y., J. Beran, and K. Yu. "Modelling financial time series with SEMIFAR GARCH model." IMA Journal of Management Mathematics 18, no. 4 (April 26, 2007): 395–412. http://dx.doi.org/10.1093/imaman/dpm024.

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Richards, Gordon R. "A fractal forecasting model for financial time series." Journal of Forecasting 23, no. 8 (2004): 586–601. http://dx.doi.org/10.1002/for.927.

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Alhnaity, Bashar, and Maysam Abbod. "A new hybrid financial time series prediction model." Engineering Applications of Artificial Intelligence 95 (October 2020): 103873. http://dx.doi.org/10.1016/j.engappai.2020.103873.

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Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (January 28, 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
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Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (July 2016): 159–80. http://dx.doi.org/10.1017/apr.2016.48.

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AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.
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Muhammad Najamuddin and Samreen Fatima. "Hybrid BRNN-ARIMA Model for Financial Time Series Forecasting." Sukkur IBA Journal of Computing and Mathematical Sciences 6, no. 1 (July 21, 2022): 62–71. http://dx.doi.org/10.30537/sjcms.v6i1.1027.

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The accurate forecasting of time series is difficult and for exchange rate more difficult as well. Because it is difficult to predict as they continuously fluctuate during trading hours. Exchange rate forecasting plays a vital financial problem in recent era. It is extensively acknowledged that exchange rate stability implies that macroeconomic stability. In this study, a hybrid model is proposed to forecast exchange rates. Bayesian regularized neural network (BRNN) model is assembled with Autoregressive integrated moving average model (ARIMA) and develop hybrid BRNN-ARIMA model. Furthermore, the comparison of proposed hybrid model has been done with standalone BRNN, standalone ARIMA and random walk model. Quarterly exchange rate data from 1970Q1 to 2021Q2 of six countries comprises developed (UK, Canada, and Singapore) and developing (Pakistan, India, and Malaysia) are forecast. To evaluate the performance of these models RMSE, MAE and MAPE are applied. The results indicate that the proposed hybrid BRNN-ARIMA model outperforms the other studied model in forecasting exchange rates.
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Kwak, Nae Won, and Dong Hoon Lim. "Financial time series forecasting using AdaBoost-GRU ensemble model." Journal of the Korean Data And Information Science Society 32, no. 2 (March 31, 2021): 267–81. http://dx.doi.org/10.7465/jkdi.2021.32.2.267.

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Dissertations / Theses on the topic "Financial time series model"

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Yin, Jiang Ling. "Financial time series analysis." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.

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Karanasos, Menelaos. "Essays on financial time series models." Thesis, Birkbeck (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286252.

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Mashikian, Paul Stephan. "Multiresolution models of financial time series." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43483.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (leaves 89-92).
by Paul Stephan Mashikian.
M.Eng.
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Nacaskul, Poomjai. "Evolutionary optimisation and financial model-trading." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298802.

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Wong, Wing-mei. "Some topics in model selection in financial time series analysis." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23273112.

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Coroneo, Laura. "Essays on modelling and forecasting financial time series." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210284.

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This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates.

The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.

The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.

The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.


Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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王詠媚 and Wing-mei Wong. "Some topics in model selection in financial time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225366.

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Yiu, Fu-keung, and 饒富強. "Time series analysis of financial index." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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Haas, Markus. "Dynamic mixture models for financial time series /." Berlin : Pro Business, 2004. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=012999049&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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YAN, HONGXUAN. "Generalised linear Gegenbauer long memory models for time series of counts with financial and insurance applications." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19660.

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The contribution of this thesis is on developing new models and applying them to analyse data in finance and insurance. The proposed generalised linear Gegenbauer autoregressive moving average models (GLGARMA) incorporate GARMA into the mean functions of the four different count distributions within a GLM framework under the parameter-driven and observation-driven approaches. Model properties in the time domain and spectral density function in the frequency domain are studied and compared to seasonal long memory model. The approximated spectral density function of the PD GLGARMA model is derived to facilitate Whittle likelihood estimation. To estimate and forecast these models, we adopt a Bayesian approach implemented using the R package Rstan. Various model selection criteria including deviance information criterion are evaluated to select some best-fitting models to undertake forecasting of future events. We test 136 open interest series for the types of long memory structures. The GLGARMA models outperform these models in both in-sample fittings and out-of-sample forecasts for each type of long memory structures. The prevalence of long memory in death count series is demonstrated. We extend this Lee-Carter type GLGARMA resulting in three modelling components, namely, period effect, graduation effect and long memory. Results show that the long memory structures enhance the accuracy of in-sample fitting, out-of-sample forecast and life expectancy. A stationary long memory mortality model with long memory cohort effect (LMLM) model is proposed. The in-sample fitting, out-of-sample forecast performances and life expectancies are calculated to exhibit the enhancement by adopting LMLM model. Assuming both constant interest rate and stochastic interest rate model with four dependency models, the annuity pricing and guaranteed annuity options demonstrate the enhancement of adopting LMLM model.
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Books on the topic "Financial time series model"

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Jens-Peter, Kreiß, Davis Richard A, Andersen Torben Gustav, and SpringerLink (Online service), eds. Handbook of Financial Time Series. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Modelling financial time series. 2nd ed. New Jersey: World Scientific, 2008.

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Modelling financial time series. Chichester [West Sussex]: Wiley, 1986.

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Mills, T. C. Econometric modelling of financial time series. Cambridge: Cambridge University Press, 1995.

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Tsay, Ruey S. Analysis of financial time series: Financial econometrics. New York: Wiley, 2002.

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Analysis of financial time series. 2nd ed. Hoboken, N.J: Wiley, 2005.

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Tsay, Ruey S. Analysis of Financial Time Series. New York: John Wiley & Sons, Ltd., 2005.

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Analysis of financial time series. New York: Wiley, 2002.

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Christian, Dunis, and Zhou Bin 1956-, eds. Nonlinear modelling of high frequency financial time series. Chichester [England]: Wiley, 1998.

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The econometric modelling of financial time series. Cambridge: Cambridge University Press, 1993.

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Book chapters on the topic "Financial time series model"

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Old, Oliver. "Financial time series." In Modeling Time-Varying Unconditional Variance by Means of a Free-Knot Spline-GARCH Model, 13–31. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-38618-4_2.

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Leeb, Hannes, and Benedikt M. Pötscher. "Model Selection." In Handbook of Financial Time Series, 889–925. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_39.

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Borak, Szymon, Wolfgang Karl Härdle, and Brenda López Cabrera. "Financial Time Series Models." In Statistics of Financial Markets, 123–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_11.

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Borak, Szymon, Wolfgang Karl Härdle, and Brenda López-Cabrera. "Financial Time Series Models." In Universitext, 131–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33929-5_11.

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Lee, Cheng-Few, Hong-Yi Chen, and John Lee. "Time Series: Analysis, Model, and Forecasting." In Financial Econometrics, Mathematics and Statistics, 279–316. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9429-8_10.

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Borak, Szymon, Wolfgang Karl Härdle, and Brenda López Cabrera. "ARIMA Time Series Models." In Statistics of Financial Markets, 135–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_12.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "ARIMA Time Series Models." In Statistics of Financial Markets, 255–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_12.

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Che-Ngoc, Ha, Tai Vo-Van, Quoc-Chanh Huynh-Le, Vu Ho, Thao Nguyen-Trang, and Minh-Tuyet Chu-Thi. "An Improved Fuzzy Time Series Forecasting Model." In Econometrics for Financial Applications, 474–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73150-6_38.

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Lee, Cheng-Few, John C. Lee, and Alice C. Lee. "Time Series: Analysis, Model, and Forecasting." In Statistics for Business and Financial Economics, 927–72. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5897-5_18.

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Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. "Models for financial series." In Hidden Markov Models for Time Series, 259–73. Second edition / Walter Zucchini, Iain L. MacDonald, and Roland Langrock. | Boca Raton : Taylor & Francis, 2016. | Series: Monographs on statistics and applied probability ; 150 | “A CRC title.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-20.

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Conference papers on the topic "Financial time series model"

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Kelany, Omnia, Sherin Aly, and Mohamed A. Ismail. "Deep Learning Model for Financial Time Series Prediction." In 2020 14th International Conference on Innovations in Information Technology (IIT). IEEE, 2020. http://dx.doi.org/10.1109/iit50501.2020.9299063.

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Jingtao Yao and Chew Lim Tan. "Time dependent directional profit model for financial time series forecasting." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861475.

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Zhang, Haiying, Qiaomei Liang, Rongqi Wang, and Qingqiang Wu. "Stacked Model with Autoencoder for Financial Time Series Prediction." In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201745.

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Araujo, Ricardo de A., Adriano L. I. Oliveira, and Silvio Meira. "A prediction model for high-frequency financial time series." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280487.

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Khandelwal, Ina, Udit Satija, and Ratnadip Adhikari. "Efficient financial time series forecasting model using DWT decomposition." In 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2015. http://dx.doi.org/10.1109/conecct.2015.7383917.

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Chaozhi, Cheng, Gao Yachun, and Jingwei Ni. "Financial Time Series Prediction Model Based Recurrent Neural Network." In 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2020. http://dx.doi.org/10.1109/iccwamtip51612.2020.9317371.

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Raimundo, Milton Saulo, and Jun Okamoto. "SVR-wavelet adaptive model for forecasting financial time series." In 2018 International Conference on Information and Computer Technologies (ICICT). IEEE, 2018. http://dx.doi.org/10.1109/infoct.2018.8356851.

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Guan, Yu Jie. "Financial time series forecasting model based on CEEMDAN-LSTM." In 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC). IEEE, 2022. http://dx.doi.org/10.1109/ctisc54888.2022.9849780.

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Ni, He. "Topology Regressive Distributed Model for Financial Time Series Prediction." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.619.

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Zheng, Hua, Li Xie, and Lizi Zhang. "Intelligence Model for the Sensitivity Analysis of Financial Time Series." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.446.

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Reports on the topic "Financial time series model"

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Soloviev, V., V. Saptsin, and D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1305.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns.
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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Transport and Telecommunication Institute, 2010. http://dx.doi.org/10.31812/0564/1145.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov chains [1] and simple first-order ones is the existing of after effect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the “generalized state” as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns [2]. According to the fundamental principles of quantum measurement theories, the measurement procedure impacts not only on the result of the measurement, but also on the state of the measured system, and the behaviour of this system in the future remains undefined, despite of the precision of the measurement. This statement, in our opinion, is general and is true not only for physical systems, but to any complex systems [3].
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Соловйов, Володимир Миколайович, Vladimir Saptsin, and Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, March 2009. http://dx.doi.org/10.31812/0564/1131.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&P 500, RTS, PFTS and other.
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Соловйов, В. М., В. В. Соловйова, and Д. М. Чабаненко. Динаміка параметрів α-стійкого процесу Леві для розподілів прибутковостей фінансових часових рядів. ФО-П Ткачук О. В., 2014. http://dx.doi.org/10.31812/0564/1336.

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Modem market economy of any country cannot successfully behave without the existence of the effective financial market. In the conditions of growing financial market, it is necessary to use modern risk-management methods, which take non-gaussian distributions into consideration. It is known, that financial and economic time series return’s distributions demonstrate so-called «heavy tails», which interrupts the modeling o f these processes with classical statistical methods. One o f the models, that is able to describe processes with «heavy tails», are the а -stable Levi processes. They can slightly simulate the dynamics of the asset prices, because it consists o f two components: the Brownian motion component and jump component. In the current work the usage of model parameters estimation procedure is proposed, which is based on the characteristic functions and is applied for the moving window for the purpose of financial-economic system’ s state monitoring.
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Bielinskyi, Andrii O., Oleksandr A. Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов, Андрій Іванович Білінський, and О. А. Сердюк. Econophysics of cryptocurrency crashes: a systematic review. Криворізький державний педагогічний університет, December 2021. http://dx.doi.org/10.31812/123456789/6974.

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
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Bielinskyi, Andrii O., Serhii V. Hushko, Andriy V. Matviychuk, Oleksandr A. Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов, Андрій Іванович Білінський, Андрій Вікторович Матвійчук, and О. А. Сердюк. Irreversibility of financial time series: a case of crisis. Криворізький державний педагогічний університет, December 2021. http://dx.doi.org/10.31812/123456789/6975.

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The focus of this study to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic. Their complexity and different aspects of nonlinear properties, such as time irreversibility, vary over time and for a long-range of scales. Therefore, our work presents approaches to measure the complexity and irreversibility of the time series. To the presented methods we include Guzik’s index, Porta’s index, Costa’s index, based on complex networks measures, Multiscale time irreversibility index and based on permutation patterns measures. Our study presents that the corresponding measures can be used as indicators or indicator-precursors of crisis states in stock markets.
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Li, Degui, Oliver Linton, and Zudi Lu. A flexible semiparametric model for time series. Institute for Fiscal Studies, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2812.

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8

Osipov, Gennadij Sergeevich, Natella Semenovna Vashakidze, and Galina Viktorovna Filippova. Basics of forecasting financial time series based on NeuroXL Predictor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-7.

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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Markov chains applications to the financial-economic time series predictions. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1189.

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In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world’s stock market indices are presented.
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Diakonova, Marina, Corinna Ghirelli, Luis Molina, and Javier J. Pérez. The economic impact of conflict-related and policy uncertainty shocks: the case of Russia. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23707.

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We show how policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of “conflict”, relating to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model. We show that the indicators provide useful information for forecasting GDP in the short run, even when controlling for a comprehensive set of standard high-frequency macro-financial variables. The second approach, is a SVAR model. We show that negative shocks to the selected indicators lead to economic slowdown, with a persistent drop in GDP growth and a short-lived but large increase in country risk.
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