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Journal articles on the topic 'Financial time series'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Politis, Dimitris N. "Financial time series." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 2 (2009): 157–66. http://dx.doi.org/10.1002/wics.24.

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2

Dingli, Alexiei, and Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach." International Journal of Machine Learning and Computing 7, no. 5 (2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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3

Anderson, Gordon, and Stephen Taylor. "Modelling Financial Time Series." Economic Journal 97, no. 386 (1987): 512. http://dx.doi.org/10.2307/2232901.

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4

Ruiz, Esther, and Lorenzo Pascual. "Bootstrapping Financial Time Series." Journal of Economic Surveys 16, no. 3 (2002): 271–300. http://dx.doi.org/10.1111/1467-6419.00170.

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5

Gemmill, Gordon. "Modelling financial time series." International Journal of Forecasting 4, no. 3 (1988): 496–97. http://dx.doi.org/10.1016/0169-2070(88)90115-x.

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6

Kinsella, A., and Stephen Taylor. "Modelling Financial Time Series." Statistician 36, no. 4 (1987): 433. http://dx.doi.org/10.2307/2348865.

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7

Baillie, Richard T. "Modelling financial time series." European Journal of Operational Research 32, no. 1 (1987): 156–58. http://dx.doi.org/10.1016/0377-2217(87)90287-6.

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8

Taivan, Ariuna. "Financial Development And Economic Growth Revisited: Time Series Evidence." International Journal of Trade, Economics and Finance 9, no. 3 (2018): 116–20. http://dx.doi.org/10.18178/ijtef.2018.9.3.599.

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9

Anderson, G. "Correction: Modelling Financial Time Series." Economic Journal 98, no. 391 (1988): 566. http://dx.doi.org/10.2307/2233416.

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10

Audrino, Francesco. "Synchronizing multivariate financial time series." Journal of Risk 6, no. 2 (2004): 81–106. http://dx.doi.org/10.21314/jor.2004.105.

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11

Stoyanov, Jordan. "Handbook of Financial Time Series." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (2010): 934. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_2.x.

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12

Rao, Suhasini Subba. "Handbook of Financial Time Series." Journal of Time Series Analysis 31, no. 1 (2010): 64. http://dx.doi.org/10.1111/j.1467-9892.2009.00640.x.

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13

Ziegel, Eric R. "Analysis of Financial Time Series." Technometrics 44, no. 4 (2002): 408. http://dx.doi.org/10.1198/tech.2002.s96.

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14

Lin, Liang-Ching, and Li-Hsien Sun. "Modeling financial interval time series." PLOS ONE 14, no. 2 (2019): e0211709. http://dx.doi.org/10.1371/journal.pone.0211709.

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15

Makowiec, Danuta, and Andrzej Posiewnik. "Beauty of financial time series." Physica A: Statistical Mechanics and its Applications 301, no. 1-4 (2001): 429–40. http://dx.doi.org/10.1016/s0378-4371(01)00402-2.

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16

D’Urso, Pierpaolo, Carmela Cappelli, Dario Di Lallo, and Riccardo Massari. "Clustering of financial time series." Physica A: Statistical Mechanics and its Applications 392, no. 9 (2013): 2114–29. http://dx.doi.org/10.1016/j.physa.2013.01.027.

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17

Christie-David, Rohan. "Analysis of Financial Time Series." Journal of Financial Research 25, no. 3 (2002): 445–46. http://dx.doi.org/10.1111/1475-6803.00029.

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18

Zinenko, Anna, and Alena Stupina. "Financial time series forecasting methods." ITM Web of Conferences 59 (2024): 02005. http://dx.doi.org/10.1051/itmconf/20245902005.

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The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model. These models were applied to non-stationary time series of stock quotes after bringing these series to a stationary form. In the paper, ARIMA and Fourier Expansion model were constructed, using Python development environment. The developed algorithms were tested on Russian and American stock indices using the Mean Absolute Percentage Error metric.

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19

Caporin, Massimiliano, and Giuseppe Storti. "Financial Time Series: Methods and Models." Journal of Risk and Financial Management 13, no. 5 (2020): 86. http://dx.doi.org/10.3390/jrfm13050086.

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The statistical analysis of financial time series is a rich and diversified research field whose inherent complexity requires an interdisciplinary approach, gathering together several disciplines, such as statistics, economics, and computational sciences. This special issue of the Journal of Risk and Financial Management on “Financial Time Series: Methods & Models” contributes to the evolution of research on the analysis of financial time series by presenting a diversified collection of scientific contributions exploring different lines of research within this field.

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20

Hadaś-Dyduch, Monika. "Approximating Financial Time Series with Wavelets." Argumenta Oeconomica Cracoviensia, no. 16 (2017): 9–22. http://dx.doi.org/10.15678/aoc.2017.1601.

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21

Lyubushin, Alexey Alexandrovich, and Yuri Anatolievich Farkov. "Synchronous components of financial time series." Computer Research and Modeling 9, no. 4 (2017): 639–55. http://dx.doi.org/10.20537/2076-7633-2017-9-4-639-655.

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22

Zanin, Massimiliano. "Forbidden patterns in financial time series." Chaos: An Interdisciplinary Journal of Nonlinear Science 18, no. 1 (2008): 013119. http://dx.doi.org/10.1063/1.2841197.

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23

McAleer, Michael, and Les Oxley. "The Econometrics of Financial Time Series." Journal of Economic Surveys 16, no. 3 (2002): 237–43. http://dx.doi.org/10.1111/1467-6419.00168.

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24

EVERTSZ, CARL J. G. "FRACTAL GEOMETRY OF FINANCIAL TIME SERIES." Fractals 03, no. 03 (1995): 609–16. http://dx.doi.org/10.1142/s0218348x95000539.

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A simple quantitative measure of the self-similarity in time-series in general and in the stock market in particular is the scaling behavior of the absolute size of the jumps across lags of size k. A stronger form of self-similarity entails that not only this mean absolute value, but also the full distributions of lag-k jumps have a scaling behavior characterized by the above Hurst exponent. In 1963, Benoit Mandelbrot showed that cotton prices have such a strong form of (distributional) self-similarity, and for the first time introduced Lévy’s stable random variables in the modeling of price r

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25

Yarushkina, Nadezhda, Aleksey Filippov, and Anton Romanov. "Contextual Analysis of Financial Time Series." Mathematics 13, no. 1 (2024): 57. https://doi.org/10.3390/math13010057.

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The evaluation of the financial state of small and medium-sized companies is a pressing issue today. This article introduces a novel method to evaluate a company’s financial state, implemented as a module within a decision support system. This component uses fuzzy logic and knowledge engineering methods. The article describes an ontological model that provides the framework for data analysis and financial time-series modeling. The ontological framework simplifies the representation of the trends in the financial indicators under analysis. Integrating an ontology and a set of fuzzy rules makes

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26

Lisi, Francesco. "Testing asymmetry in financial time series." Quantitative Finance 7, no. 6 (2007): 687–96. http://dx.doi.org/10.1080/14697680701283739.

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27

Holdom, B. "From turbulence to financial time series." Physica A: Statistical Mechanics and its Applications 254, no. 3-4 (1998): 569–76. http://dx.doi.org/10.1016/s0378-4371(98)00078-8.

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28

Gimeno, Ricardo, Benjamı́n Manchado, and Román Mı́nguez. "Stationarity tests for financial time series." Physica A: Statistical Mechanics and its Applications 269, no. 1 (1999): 72–78. http://dx.doi.org/10.1016/s0378-4371(99)00081-3.

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29

Fukuda, Kosei. "Distribution switching in financial time series." Mathematics and Computers in Simulation 79, no. 5 (2009): 1711–20. http://dx.doi.org/10.1016/j.matcom.2008.08.012.

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30

Basalto, Nicolas, Roberto Bellotti, Francesco De Carlo, Paolo Facchi, Ester Pantaleo, and Saverio Pascazio. "Hausdorff clustering of financial time series." Physica A: Statistical Mechanics and its Applications 379, no. 2 (2007): 635–44. http://dx.doi.org/10.1016/j.physa.2007.01.011.

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31

Kanjamapornkul, Kabin, Richard Pinčák, and Erik Bartoš. "Cohomology theory for financial time series." Physica A: Statistical Mechanics and its Applications 546 (May 2020): 122212. http://dx.doi.org/10.1016/j.physa.2019.122212.

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32

Abberger, Klaus. "Quantile smoothing in financial time series." Statistical Papers 38, no. 2 (1997): 125–48. http://dx.doi.org/10.1007/bf02925220.

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33

Zhang, Hong, and Ke Qiang Dong. "Fractal Properties of Financial Time Series." Key Engineering Materials 439-440 (June 2010): 683–87. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.683.

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In this paper, we analyze the stock of Nanjing Panda Electronics Co Ltd for the 44-year period, from May 2, 1996, to October 9, 2009, a total of 3200 trading days. Using the Box-counting dimension method, we find that the financial data have different power law exponents in the plot for the number of box and diameter of box, which indicates the multifractality exist in the time series. In order to investigate the latent properties in the data, the width and maximum of the singular spectrum are calculated. The results show the strong degree of multifractality in the time series.

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34

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 (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. Additiona

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35

Buonocore, R. J., T. Aste, and T. Di Matteo. "Measuring multiscaling in financial time-series." Chaos, Solitons & Fractals 88 (July 2016): 38–47. http://dx.doi.org/10.1016/j.chaos.2015.11.022.

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36

Guerrero, Víctor M., and Adriana Galicia-Vázquez. "Trend estimation of financial time series." Applied Stochastic Models in Business and Industry 26, no. 3 (2009): 205–23. http://dx.doi.org/10.1002/asmb.763.

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37

Elliman, Dave. "Pattern recognition and financial time-series." Intelligent Systems in Accounting, Finance and Management 14, no. 3 (2006): 99–115. http://dx.doi.org/10.1002/isaf.279.

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38

Vlad, Sorin, and Mariana Vlad. "Nonlinear Analysis of Financial Time Series." Ovidius University Annals. Economic Sciences Series 22, no. 2 (2023): 478–83. http://dx.doi.org/10.61801/ouaess.2022.2.64.

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39

Kipel, Ali, Cüneyt Yurt, Yıldırım Adigüzel, Zehra Sude Sari, and Mehmet Fatih Akay. "TIME SERIES BASED FINANCIAL FORECASTING MODELS." International Journal of Professional Business Review 10, no. 3 (2025): e05382. https://doi.org/10.26668/businessreview/2025.v10i3.5382.

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Objective: The purpose of this study is to develop financial component forecasting models that enable more accurate financial planning, allowing businesses to gain a competitive advantage. Theoretical Framework: Time series-based Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Multi-Quantile Recurrent Neural Network (MQRNN), and Autoregressive Integrated Moving Average (ARIMA) models have been developed for forecasting financial components. Method: Forecasting models have been developed for two different months (June and July) using a dataset containing 291 rows of weekly data

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40

Sproul, Thomas W. "Time scale and fractionality in financial time series." Agricultural Finance Review 76, no. 1 (2016): 76–93. http://dx.doi.org/10.1108/afr-01-2016-0008.

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Purpose – Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem. Design/methodology/approach – Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity f

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41

Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (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

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42

Carbone, A., G. Castelli, and H. E. Stanley. "Time-dependent Hurst exponent in financial time series." Physica A: Statistical Mechanics and its Applications 344, no. 1-2 (2004): 267–71. http://dx.doi.org/10.1016/j.physa.2004.06.130.

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43

Rateiwa, Ronald, and Meshach Jesse Aziakpono. "Financial structure and economic performance in selected African countries: time series evidence." Banks and Bank Systems 11, no. 2 (2016): 45–60. http://dx.doi.org/10.21511/bbs.11(2).2016.05.

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In this paper, the authors investigate the long-debated question of whether or not a country’s financial structure matters for economic performance and, if so, how exactly it matters. The study uses the Johansen cointegration and vector error correction modelling framework within a country-specific setting to examine empirically the existence of a long-run equilibrium relationship between the financial structure of a country and per capita GDP and the causality thereof. The empirical assessment is based on evidence from selected African countries over the period 1971-2013, notably Egypt, Niger

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44

Wu, Chunchi, Chihwa Kao, and Cheng F. Lee. "Time-Series Properties of Financial Series and Implications for Modeling." Journal of Accounting, Auditing & Finance 11, no. 2 (1996): 277–303. http://dx.doi.org/10.1177/0148558x9601100207.

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This paper investigates the time-series properties of a wide range of corporate financial and accounting series. Unit root tests developed by Dickey and Fuller (1979) are applied to these series. The results support the hypothesis that most of these series contain both permanent (random walk) and transitory components. The results show that most financial series are dominated by a random walk component. However, for some series, such as net sales, net income, earnings per share, and returns on investments, there is a relatively significant stationary component, which suggests the presence of s

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45

Mykola, Kushnir, and Tokarieva Kateryna. "FINANCIAL TIME SERIES MODELLING: RETURN ON ASSETS." Technology audit and production reserves 5, no. 2 (49) (2019): 50–55. https://doi.org/10.15587/2312-8372.2019.183868.

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<em>The paper discovers certain aspects of financial time series, in particular, modeling of return on assets. The object of research is a system of indicators for analyzing the returns of financial time series. There is a key feature that distinguishes the analysis of financial time series from the analysis of other time series, as financial theory and its empirical time series contain an element of uncertainty. As a result of this additional uncertainty, statistical theory and its methods and models play an important role in the analysis of financial time series.</em> <em>One of the most pro

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46

Sarantsev, Andrey. "IID Time Series Testing." Theory of Stochastic Processes 27(43), no. 1 (2023): 41–52. http://dx.doi.org/10.3842/tsp-8836211480-29.

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Traditional white noise testing, for example the Ljung-Box test, studies only the autocorrelation function (ACF). Time series can be heteroscedastic and therefore not i.i.d. but still white noise (that is, with zero ACF). An example of heteroscedasticity is financial time series: times of high variance (financial crises) can alternate with times of low variance (calm times). Here, absolute values of time series terms are not white noise. We could test for white noise separately for original and absolute values, for example using Ljung-Box tests for both. In this article, we create an omnibus t

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47

Seemann, Lars, Jia-Chen Hua, Joseph L. McCauley, and Gemunu H. Gunaratne. "Ensemble vs. time averages in financial time series analysis." Physica A: Statistical Mechanics and its Applications 391, no. 23 (2012): 6024–32. http://dx.doi.org/10.1016/j.physa.2012.06.054.

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48

Kushnir, Mykola, and Kateryna Tokarieva. "Financial time series modelling: return on assets." Technology audit and production reserves 5, no. 2(49) (2019): 50–55. http://dx.doi.org/10.15587/2312-8372.2019.183868.

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49

Gruevski, Ilija. "Basic Time Series Models in Financial Forecasting." Journal of Economics 6, no. 1 (2021): 76–89. http://dx.doi.org/10.46763/joe216.10076g.

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50

Lee, Bong-Soo, and Terence C. Mills. "The Econometric Modelling of Financial Time Series." Journal of Finance 50, no. 1 (1995): 387. http://dx.doi.org/10.2307/2329254.

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