scholarly journals Testing financial time series for autocorrelation: Robust Tests

2020 ◽  
Vol 27 (3) ◽  
pp. e96
Author(s):  
Nelson Omar Muriel Torrero
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Financial Time Series ◽  
Real Data ◽  
Small Sample ◽  
Small Sample Properties ◽  
Robust Tests ◽  
Daily Stock Returns ◽  
Portmanteau Statistics ◽  
Empirical Size

Two modified Portmanteau statistics are studied under dependence assumptions common in financial applications which can be used for testing that heteroskedastic time series are serially uncorrelated without assuming independence or Normality. Their asymptotic distribution is found to be null and their small sample properties are examined via Monte Carlo. The power of the tests is studied under the MA and GARCH-in-mean alternatives. The tests exhibit an appropriate empirical size and are seen to be more powerful than a robust Box-Pierce to the selected alternatives. Real data on daily stock returns and exchange rates is used to illustrate the tests.

scholarly journals Monitoring Volatility Change for Time Series Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1312
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Dongwuk Kim
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Financial Time Series ◽  
Real Data ◽  
Support Vector ◽  
Tuning Parameters ◽  
Monitoring Process ◽  
Microsoft Corporation ◽  
Cusum Method

This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.

scholarly journals Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Katleho Makatjane ◽  
Ntebogang Moroke
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Financial Time ◽  
Johannesburg Stock Exchange ◽  
Non Linear

During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

scholarly journals QANOVA: quantile-based permutation methods for general factorial designs

Test ◽  
2021 ◽  
Author(s):  
Marc Ditzhaus ◽  
Roland Fried ◽  
Markus Pauly
Keyword(s):  
Confidence Regions ◽  
Small Sample ◽  
Factorial Designs ◽  
Population Means ◽  
Heteroscedastic Error ◽  
Special Cases ◽  
Permutation Methods ◽  
Error Distributions ◽  
Small Sample Properties ◽  
Robust Tests

AbstractPopulation means and standard deviations are the most common estimands to quantify effects in factorial layouts. In fact, most statistical procedures in such designs are built toward inferring means or contrasts thereof. For more robust analyses, we consider the population median, the interquartile range (IQR) and more general quantile combinations as estimands in which we formulate null hypotheses and calculate compatible confidence regions. Based upon simultaneous multivariate central limit theorems and corresponding resampling results, we derive asymptotically correct procedures in general, potentially heteroscedastic, factorial designs with univariate endpoints. Special cases cover robust tests for the population median or the IQR in arbitrary crossed one-, two- and higher-way layouts with potentially heteroscedastic error distributions. In extensive simulations, we analyze their small sample properties and also conduct an illustrating data analysis comparing children’s height and weight from different countries.

scholarly journals A Mathematical Modeling Framework To Detect The Optimal Financial Turning Points

2021 ◽  
Author(s):  
Fateme Yazdani ◽  
Mehdi Khashei ◽  
Seyed Reza Hejazi
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Financial Time Series ◽  
Turning Points ◽  
Real Data ◽  
Detection Methods ◽  
Modeling Framework ◽  
Detection Model ◽  
Financial Time ◽  
History Of

Abstract The financial markets have always witnessed the competition of their participants for gaining high and stable profits. The realization extent of this goal depends on the profitability of the trading points or turning points (TPs) ahead. TPs prediction problem is one of the most challenging yet important problems in the financial discipline. The first step towards predicting financial TPs is to detect TPs from the history of the corresponding financial time series. Literature indicates that the profitability of the predicted financial TPs depends on the profitability of the detected TPs. Given this, numerous efforts have been devoted to enhancing the profitability of the detected financial TPs. Nevertheless, to the best of our knowledge, none of the existing detection methods can detect the most profitable or the optimal TPs from the history of financial time series. The present study concerns this research gap and ensures detecting the optimal financial TPs by proposing a mathematical modeling framework. The proposed optimal TPs detection model in this paper will be structured concerning the three following assumptions. First, short-selling the financial asset is possible. Second, the time value for the investment money is not considered. Third, detecting consecutive buying TPs and consecutive selling TPs is not allowed. Empirical results with twenty real data sets indicate that the proposed model, in contrast to the existing TPs detection methods, detects the optimal TPs from the history of the financial time series.

Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach

2018 ◽  
Vol 17 (01) ◽  
pp. 1850006 ◽  
Author(s):  
Yongping Zhang ◽  
Pengjian Shang ◽  
Hui Xiong ◽  
Jianan Xia
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Financial Time Series ◽  
Real Data ◽  
Accurate Estimate ◽  
Phase Space Reconstruction ◽  
Visibility Graph ◽  
Reconstruction Technique ◽  
Nonequilibrium Dynamic ◽  
Time Irreversibility

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.

Graphic analysis and multifractal on percolation-based return interval series

2015 ◽  
Vol 26 (12) ◽  
pp. 1550137 ◽  
Author(s):  
A. Q. Pei ◽  
J. Wang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulated Data ◽  
Small World ◽  
Real Data ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
The Real ◽  
Return Interval ◽  
Financial Time

A financial time series model is developed and investigated by the oriented percolation system (one of the statistical physics systems). The nonlinear and statistical behaviors of the return interval time series are studied for the proposed model and the real stock market by applying visibility graph (VG) and multifractal detrended fluctuation analysis (MF-DFA). We investigate the fluctuation behaviors of return intervals of the model for different parameter settings, and also comparatively study these fluctuation patterns with those of the real financial data for different threshold values. The empirical research of this work exhibits the multifractal features for the corresponding financial time series. Further, the VGs deviated from both of the simulated data and the real data show the behaviors of small-world, hierarchy, high clustering and power-law tail for the degree distributions.

Financial Time Series Analysis via Backtesting Approach

2020 ◽  
Author(s):  
Monday Osagie Adenomon
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Returns ◽  
Stock Price ◽  
Financial Time Series ◽  
Information Criteria ◽  
Financial Time ◽  
Student T Distribution ◽  
Bank Stock ◽  
T Distribution

This book chapter investigated the place of backtesting approach in financial time series analysis in choosing a reliable Generalized Auto-Regressive Conditional Heteroscedastic (GARCH) Model to analyze stock returns in Nigeria. To achieve this, The chapter used a secondary data that was collected from www.cashcraft.com under stock trend and analysis. Daily stock price was collected on Zenith bank stock price from October 21st 2004 to May 8th 2017. The chapter used nine different GARCH models (standard GARCH (sGARCH), Glosten-Jagannathan-Runkle GARCH (gjrGARCH), Exponential GARCH (Egarch), Integrated GARCH (iGARCH), Asymmetric Power Autoregressive Conditional Heteroskedasticity (ARCH) (apARCH), Threshold GARCH (TGARCH), Non-linear GARCH (NGARCH), Nonlinear (Asymmetric) GARCH (NAGARCH) and The Absolute Value GARCH (AVGARCH) with maximum lag of 2. Most the information criteria for the sGARCH model were not available due to lack of convergence. The lowest information criteria were associated with apARCH (2,2) with Student t-distribution followed by NGARCH(2,1) with skewed student t-distribution. The backtesting result of the apARCH (2,2) was not available while eGARCH(1,1) with Skewed student t-distribution, NGARCH(1,1), NGARCH(2,1), and TGARCH (2,1) failed the backtesting but eGARCH (1,1) with student t-distribution passed the backtesting approach. Therefore with the backtesting approach, eGARCH(1,1) with student distribution emerged the superior model for modeling Zenith Bank stock returns in Nigeria. This chapter recommended the backtesting approach to selecting reliable GARCH model.

scholarly journals DAILY VARIATION AND PREDICTING STOCK MARKET RETURNS FOR THE FRANKFURTER BÖRSE (STOCK MARKET)

2008 ◽  
Vol 9 (3) ◽  
pp. 189-198 ◽  
Author(s):  
Jeffrey E. Jarrett ◽  
Janne Schilling
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Arima Model ◽  
Unit Root Test ◽  
Unit Roots ◽  
Stock Market Returns ◽  
Daily Stock Returns

In this article we test the random walk hypothesis in the German daily stock prices by means of a unit root test and the development of an ARIMA model for prediction. The results show that the time series of daily stock returns for a stratified random sample of German firms listed on the stock exchange of Frankfurt exhibit unit roots. Also, we find that one may predict changes in the returns to these listed stocks. These time series exhibit properties which are forecast able and provide the intelligent data analysts’ methods to better predict the directive of individual stock returns for listed German firms. The results of this study, though different from most other studies of other stock markets, indicate the Frankfurt stock market behaves in similar ways to North American, other European and Asian markets previously studied in the same manner.

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