scholarly journals Análise de propriedades das séries temporais dos ativos que compõem o índice IBOVESPA

2021 ◽  
Vol 9 (2) ◽  
pp. 5-35
Author(s):  
César Daltoé Berci ◽  
Ceslo Pascoli Bottura
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Statistical Properties ◽  
Point Of View ◽  
Financial Time ◽  
Analysis Tools ◽  
Financial Transactions ◽  
Computing Capacity

Several characteristics of financial time series are of interest both from an academic point of view, which is intended to analyze the dynamics of the data and its numerical properties, as well as from investors point of view, who use this knowledge to generate profit in their financial transactions. By applying several analysis tools and using a massive computing capacity, the numerical and statistical properties of the assets that compose the IBOVESPA index were evaluated. Given the relevance and scope of the analyzed time series, the results obtained from this analysis can serve as a basis for the characterization of financial time series

Statistische Besonderheiten von Finanzzeitreihen / Statistical Properties of Financial Time Series

2002 ◽  
Vol 222 (2) ◽  
Author(s):  
Walter Krämer
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Statistical Properties ◽  
Financial Time

ZusammenfassungFinanzmarktdaten wie Zinsen, Aktien- oder Wechselkurse und andere spekulative Preise setzen sich durch verschiedene Besonderheiten von sonstigen ökonomischen Zeitreihendaten ab. Dieser Artikel untersucht die Konsequenzen dieser Besonderheiten für die rationale Bewertung von Finanzinstrumenten und für verschiedene, in finanzwirtschaftlichen Anwendungen angewandte statistische Schätzungen und Tests.

scholarly journals Average crossing time: An alternative characterization of mean aversion and reversion

2021 ◽  
Vol 12 (3) ◽  
pp. 903-944 ◽  
Author(s):  
John B. Donaldson ◽  
Rajnish Mehra
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Mean Reversion ◽  
Statistical Characteristics ◽  
Alternative Measure ◽  
Financial Time ◽  
Asset Pricing Models ◽  
Crossing Time ◽  
Alternative Characterization

This study compares and contrasts the multiple characterizations of mean reversion in financial time series as regards the restrictions they imply. This is accomplished by translating them into statements about an alternative measure, the “Average Crossing Time” or ACT. We argue that the ACT measure, per se, provides not only a useful benchmark for the degree of mean reversion/aversion, but also an intuitive, and easily quantified sense of one time series being “more strongly mean‐reverting/averting” than another. We conclude our discussion by deriving the ACT measure for a wide class of stochastic processes and detailing its statistical characteristics. Our analysis is principally undertaken within a class of well‐understood production based asset pricing models.

scholarly journals An Exploratory Study of Geospace Perturbations Using Financial Analysis Tools in the Context of Complex Systems

Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 239
Author(s):  
Pavlos I. Zitis ◽  
Stelios M. Potirakis ◽  
Georgios Balasis ◽  
Konstantinos Eftaxias
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Financial Analysis ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Analysis Approach ◽  
Financial Time ◽  
Analysis Methods ◽  
Analysis Tools ◽  
High Volatility

In the frame of complex systems research, methods used to quantitatively analyze specific dynamic phenomena are often used to analyze phenomena from other disciplines on the grounds that are governed by similar dynamics. Technical analysis is considered the oldest, currently omnipresent, method for financial markets analysis, which uses past prices aiming at the possible short-term forecast of future prices. This work is the first attempt to explore the applicability of technical analysis tools on disturbance storm time (Dst) index time series, aiming at the identification of similar features between the Dst time series during magnetic storms (MSs) and asset price time series. We employ the following financial analysis tools: simple moving average (SMA), Bollinger bands, and relative strength index (RSI), formulating an analysis approach based on various features, appearing in financial time series during high volatility periods, that could be found during the different phases of the evolution of an MS (onset, main development, and recovery phase), focusing on the temporal sequence they occur. The applicability of the proposed analysis approach is examined on several MS events and the results reveal similar behavior with the financial time series in high volatility periods. We postulate that these specialized data analysis methods could be combined in the future with other statistical and complex systems time series analysis methods in order to form a useful toolbox for the study of geospace perturbations related to natural hazards.

Econophysics: financial time series from a statistical physics point of view

2000 ◽  
Vol 279 (1-4) ◽  
pp. 443-456 ◽  
Author(s):  
Vasiliki Plerou ◽  
Parameswaran Gopikrishnan ◽  
Bernd Rosenow ◽  
Luis A.N. Amaral ◽  
H.Eugene Stanley
Keyword(s):  
Time Series ◽  
Statistical Physics ◽  
Financial Time Series ◽  
Point Of View ◽  
Financial Time

scholarly journals Diligence in determining the appropriate form of stationarity

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
André Heymans ◽  
Chris Van Heerden ◽  
Jan Van Greunen ◽  
Gary Van Vuuren
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Statistical Properties ◽  
Series Data ◽  
Financial Time ◽  
Course Of Action ◽  
Out Of Sample ◽  
Managerial Implications ◽  
Value Add

Orientation: One of the most vexing problems of modelling time series data is determining the appropriate form of stationarity, as it can have a significant influence on the model’s explanatory properties, which makes interpreting the results problematic.Research purpose: This article challenged the assumption that most financial time series are first differenced stationary. The common difference first, ask questions later approach was revisited by taking a more systematic approach when analysing the statistical properties of financial time series data.Motivation for the study: Since Nelson and Plosser’s (1982) argued that many macroeconomic time series are difference stationary, many econometricians simply differenced data in order to achieve stationarity. However, the inherent properties of time series data have changed over the past 30 years. This necessitates a proper evaluation of the properties of data before deciding on the appropriate course of action, in order to avoid over-differencing which causes variables to lose their explanatory ability that leads to spurious results.Research approach, design and method: This article introduced a rigorous process that enables econometricians to determine the most appropriate form of stationarity, which is led by the underlying statistical properties of several financial and economic variables.Main findings: The results highlighted the importance of consulting the d parameter to makea more informed decision, rather than only assuming that the data are I(1). Evidence also suggested that the appropriate form of stationarity can vary, but emphasises the importance to consider a series to be fractionally differenced.Practical/managerial implications: Only when data are correctly classified and transformed accordingly will the data be neither under- nor over-differenced, thus enhancing the validity of the results generated by statistical models.Contribution/value-add: By utilising this rigorous process, econometricians will be able to generate more accurate out-of-sample forecasts, as already proven by Van Greunen, Heymans,Van Heerden and Van Vuuren (2014).

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