Artículos de revistas: "Financial time series"

1

Politis, Dimitris N. "Financial time series". Wiley Interdisciplinary Reviews: Computational Statistics 1, n.º 2 (19 de agosto de 2009): 157–66. http://dx.doi.org/10.1002/wics.24.

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2

Dingli, Alexiei y Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach". International Journal of Machine Learning and Computing 7, n.º 5 (octubre de 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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3

Anderson, Gordon y Stephen Taylor. "Modelling Financial Time Series." Economic Journal 97, n.º 386 (junio de 1987): 512. http://dx.doi.org/10.2307/2232901.

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4

Ruiz, Esther y Lorenzo Pascual. "Bootstrapping Financial Time Series". Journal of Economic Surveys 16, n.º 3 (julio de 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, n.º 3 (enero de 1988): 496–97. http://dx.doi.org/10.1016/0169-2070(88)90115-x.

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6

Kinsella, A. y Stephen Taylor. "Modelling Financial Time Series." Statistician 36, n.º 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, n.º 1 (octubre de 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, n.º 3 (junio de 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, n.º 391 (junio de 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, n.º 2 (febrero de 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, n.º 4 (20 de septiembre de 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, n.º 1 (enero de 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, n.º 4 (noviembre de 2002): 408. http://dx.doi.org/10.1198/tech.2002.s96.

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14

Lin, Liang-Ching y Li-Hsien Sun. "Modeling financial interval time series". PLOS ONE 14, n.º 2 (14 de febrero de 2019): e0211709. http://dx.doi.org/10.1371/journal.pone.0211709.

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15

Makowiec, Danuta y Andrzej Posiewnik. "Beauty of financial time series". Physica A: Statistical Mechanics and its Applications 301, n.º 1-4 (diciembre de 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 y Riccardo Massari. "Clustering of financial time series". Physica A: Statistical Mechanics and its Applications 392, n.º 9 (mayo de 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, n.º 3 (septiembre de 2002): 445–46. http://dx.doi.org/10.1111/1475-6803.00029.

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18

Rateiwa, Ronald y Meshach Jesse Aziakpono. "Financial structure and economic performance in selected African countries: time series evidence". Banks and Bank Systems 11, n.º 2 (2 de julio de 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, Nigeria and South Africa. Firstly, cointegration test results reported in this paper show that there exists a strong relationship between the financial structure of Egypt and South Africa, and per capita GDP in these countries. However, such a relationship is weak in Nigeria, mainly attributable to its low level of financial development and the possibility of the natural resource curse emanating from the oil industry. Secondly, the evidence also strongly suggests that the nature of the relationship between the financial structure of Egypt and South Africa and per capita GDP is positive, albeit based on different measures of financial structure. In Egypt, financial structure is measured by the S-Size ratio, while, in South Africa, it is proxied by the S-Activity ratio. In Nigeria, there is no evidence suggesting that the country’s financial structure influences per capita GDP. Lastly, coefficients of the error correction term for all three countries are low, suggesting inefficiencies in the financial system and possible rigidities within the economies

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19

Caporin, Massimiliano y Giuseppe Storti. "Financial Time Series: Methods and Models". Journal of Risk and Financial Management 13, n.º 5 (28 de abril de 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, n.º 16 (2017): 9–22. http://dx.doi.org/10.15678/aoc.2017.1601.

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21

Lyubushin, Alexey Alexandrovich y Yuri Anatolievich Farkov. "Synchronous components of financial time series". Computer Research and Modeling 9, n.º 4 (agosto de 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, n.º 1 (marzo de 2008): 013119. http://dx.doi.org/10.1063/1.2841197.

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23

McAleer, Michael y Les Oxley. "The Econometrics of Financial Time Series". Journal of Economic Surveys 16, n.º 3 (julio de 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, n.º 03 (septiembre de 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 records. This paper discusses the analysis of the self-similarity of high-frequency DEM-USD exchange rate records and the 30 main German stock price records. Distributional self-similarity is found in both cases and some of its consequences are discussed.

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25

Lisi, Francesco. "Testing asymmetry in financial time series". Quantitative Finance 7, n.º 6 (diciembre de 2007): 687–96. http://dx.doi.org/10.1080/14697680701283739.

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26

Holdom, B. "From turbulence to financial time series". Physica A: Statistical Mechanics and its Applications 254, n.º 3-4 (junio de 1998): 569–76. http://dx.doi.org/10.1016/s0378-4371(98)00078-8.

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27

Gimeno, Ricardo, Benjamı́n Manchado y Román Mı́nguez. "Stationarity tests for financial time series". Physica A: Statistical Mechanics and its Applications 269, n.º 1 (julio de 1999): 72–78. http://dx.doi.org/10.1016/s0378-4371(99)00081-3.

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28

Fukuda, Kosei. "Distribution switching in financial time series". Mathematics and Computers in Simulation 79, n.º 5 (enero de 2009): 1711–20. http://dx.doi.org/10.1016/j.matcom.2008.08.012.

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29

Basalto, Nicolas, Roberto Bellotti, Francesco De Carlo, Paolo Facchi, Ester Pantaleo y Saverio Pascazio. "Hausdorff clustering of financial time series". Physica A: Statistical Mechanics and its Applications 379, n.º 2 (junio de 2007): 635–44. http://dx.doi.org/10.1016/j.physa.2007.01.011.

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30

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

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31

Abberger, Klaus. "Quantile smoothing in financial time series". Statistical Papers 38, n.º 2 (junio de 1997): 125–48. http://dx.doi.org/10.1007/bf02925220.

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32

Zhang, Hong y Ke Qiang Dong. "Fractal Properties of Financial Time Series". Key Engineering Materials 439-440 (junio de 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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33

Elliman, Dave. "Pattern recognition and financial time-series". Intelligent Systems in Accounting, Finance and Management 14, n.º 3 (julio de 2006): 99–115. http://dx.doi.org/10.1002/isaf.279.

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34

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

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35

Guerrero, Víctor M. y Adriana Galicia-Vázquez. "Trend estimation of financial time series". Applied Stochastic Models in Business and Industry 26, n.º 3 (13 de febrero de 2009): 205–23. http://dx.doi.org/10.1002/asmb.763.

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36

Lupekesa, Chipasha Salome Bwalya, Johannes Tshepiso Tsoku y Lebotsa Daniel Metsileng. "Econometric Modelling of Financial Time Series". International Journal of Management, Entrepreneurship, Social Science and Humanities 5, n.º 2 (30 de diciembre de 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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37

Sproul, Thomas W. "Time scale and fractionality in financial time series". Agricultural Finance Review 76, n.º 1 (3 de mayo de 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 futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality. Findings – Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do. Research limitations/implications – Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence. Originality/value – This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought.

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38

Kiesel, Rüdiger, Magda Mroz y Ulrich Stadtmüller. "Time-varying copula models for financial time series". Advances in Applied Probability 48, A (julio de 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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39

Carbone, A., G. Castelli y H. E. Stanley. "Time-dependent Hurst exponent in financial time series". Physica A: Statistical Mechanics and its Applications 344, n.º 1-2 (diciembre de 2004): 267–71. http://dx.doi.org/10.1016/j.physa.2004.06.130.

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40

Wu, Chunchi, Chihwa Kao y Cheng F. Lee. "Time-Series Properties of Financial Series and Implications for Modeling". Journal of Accounting, Auditing & Finance 11, n.º 2 (abril de 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 successful smoothing for these series. We show that smoothing may reduce volatility of financial series but it cannot produce a deterministic growth trend. Implications of nonstationarity for financial modeling are explored.

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41

Seemann, Lars, Jia-Chen Hua, Joseph L. McCauley y Gemunu H. Gunaratne. "Ensemble vs. time averages in financial time series analysis". Physica A: Statistical Mechanics and its Applications 391, n.º 23 (diciembre de 2012): 6024–32. http://dx.doi.org/10.1016/j.physa.2012.06.054.

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42

Kushnir, Mykola y Kateryna Tokarieva. "Financial time series modelling: return on assets". Technology audit and production reserves 5, n.º 2(49) (31 de octubre de 2019): 50–55. http://dx.doi.org/10.15587/2312-8372.2019.183868.

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43

Gruevski, Ilija. "Basic Time Series Models in Financial Forecasting". Journal of Economics 6, n.º 1 (2021): 76–89. http://dx.doi.org/10.46763/joe216.10076g.

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44

Lee, Bong-Soo y Terence C. Mills. "The Econometric Modelling of Financial Time Series." Journal of Finance 50, n.º 1 (marzo de 1995): 387. http://dx.doi.org/10.2307/2329254.

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45

Walden, Andrew y T. C. Mills. "The Econometric Modelling of Financial Time Series." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, n.º 3 (1994): 508. http://dx.doi.org/10.2307/2983542.

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46

Van Zyl-Bulitta, Verena Helen, R. Otte y JH Van Rooyen. "Layer histogram patterns in financial time series". Corporate Ownership and Control 6, n.º 3 (2009): 137–46. http://dx.doi.org/10.22495/cocv6i3p13.

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This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behavior of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states.

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47

Sako, Kady, Berthine Nyunga Mpinda y Paulo Canas Rodrigues. "Neural Networks for Financial Time Series Forecasting". Entropy 24, n.º 5 (7 de mayo de 2022): 657. http://dx.doi.org/10.3390/e24050657.

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Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.

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48

Dogariu, Mihai, Liviu-Daniel Ştefan, Bogdan Andrei Boteanu, Claudiu Lamba, Bomi Kim y Bogdan Ionescu. "Generation of Realistic Synthetic Financial Time-series". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 4 (30 de noviembre de 2022): 1–27. http://dx.doi.org/10.1145/3501305.

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Financial markets have always been a point of interest for automated systems. Due to their complex nature, financial algorithms and fintech frameworks require vast amounts of data to accurately respond to market fluctuations. This data availability is tied to the daily market evolution, so it is impossible to accelerate its acquisition. In this article, we discuss several solutions for augmenting financial datasets via synthesizing realistic time-series with the help of generative models. This problem is complex, since financial time series present very specific properties, e.g., fat-tail distribution, cross-correlation between different stocks, specific autocorrelation, cluster volatility and so on. In particular, we propose solutions for capturing cross-correlations between different stocks and for transitioning from fixed to variable length time-series without resorting to sequence modeling networks, and adapt various network architectures, e.g., fully connected and convolutional GANs, variational autoencoders, and generative moment matching networks. Finally, we tackle the problem of evaluating the quality of synthetic financial time-series. We introduce qualitative and quantitative metrics, along with a portfolio trend prediction framework that validates our generative models’ performance. We carry out experiments on real-world financial data extracted from the US stock market, proving the benefits of these techniques.

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49

Kumar, Rajesh. "Mining the Time Series for Financial Gain". International Journal of Computer Applications 92, n.º 3 (18 de abril de 2014): 1–5. http://dx.doi.org/10.5120/15986-4910.

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50

Hylleberg, Svend y Terence C. Mills. "The Econometric Modelling of Financial Time Series." Economic Journal 105, n.º 431 (julio de 1995): 1038. http://dx.doi.org/10.2307/2235181.

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Artículos de revistas sobre el tema "Financial time series"

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