scholarly journals Multiscale Quantile Correlation Coefficient: Measuring Tail Dependence of Financial Time Series

2020 ◽  
Vol 12 (12) ◽  
pp. 4908
Author(s):  
Chao Xu ◽  
Jinchuan Ke ◽  
Xiaojun Zhao ◽  
Xiaofang Zhao
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Financial Markets ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Coarse Graining ◽  
Explanatory Variables ◽  
Financial Time

In the context of the frequent occurrence of extreme events, measuring the tail dependence of financial time series is essential for maintaining the sustainable development of financial markets. In this paper, a multiscale quantile correlation coefficient (MQCC) is proposed to measure the tail dependence of financial time series. The new MQCC method consists of two parts: the multiscale analysis and the correlation analysis. In the multiscale analysis, the coarse graining approach is used to study the financial time series on multiple temporal scales. In the correlation analysis, the quantile correlation coefficient is applied to quantify the correlation strength of different data quantiles, especially regarding the difference and the symmetry of tails. One reason to adopt this method is that the conditional distribution of the explanatory variables can be characterized by the quantile regression, rather than simply by the conditional expectation analysis in the traditional regression. By applying the MQCC method in the financial markets of different regions, many interesting results can be obtained. It is worth noting that there are significant differences in tail dependence between different types of financial markets.

The multiscale analysis between stock market time series

2015 ◽  
Vol 26 (06) ◽  
pp. 1550071 ◽  
Author(s):  
Wenbin Shi ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Financial Time ◽  
Cross Correlation Analysis

This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

scholarly journals Do search engine data improve financial time series volatility predictions in different market periods? An empirical analysis on major world financial indices.

2015 ◽  
Vol 14 (5) ◽  
pp. 5759-5768
Author(s):  
Dimche Risteski ◽  
Danco Davcev
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Search Engine ◽  
Financial Time Series ◽  
Price Volatility ◽  
Volume Index ◽  
Financial Time ◽  
Market Index ◽  
Search Volume ◽  
Financial Indices

In this paper, we investigate the different influence of search engine data in different market periods on the improvement of the prediction of the financial time series volatility. We use the EGARCH and the EGARCH-SVI model. We analyze weekly data from the Dow Jones, FTSE 100 and Nikkei 225 market indices and the weekly search volume index (SVI) from google trends for market indices keywords. The main contribution of this paper is introducing limitations of the EGARCH-SVI model for forecasting the weekly volatility of the market index. Our results show that i) search engine data improve financial time series volatility predictions of the EGARCH-SVI model in market crisis periods with the bigger price volatility; and ii) search engine data is not improving the prediction of the financial time series volatility of the EGARCH-SVI model in a non-crisis periods with low price volatility in the market. This result also confirms the predictive power of the EGARCH-SVI model in crisis periods for different financial markets.

scholarly journals Wavelet Variance, Covariance and Correlation Analysis of BSE and NSE Indexes Financial Time Series

2016 ◽  
Vol 1 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Lokesh Kumar Joshi
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Market ◽  
Financial Time Series ◽  
Market Behavior ◽  
Financial Time ◽  
Index Data ◽  
Wavelet Variance ◽  
Cross Country

The mostly used measure to analyze the stock market behavior is wavelet correlation analysis. Cross-country correlations have been largely used to obtain a static estimate of the comovements of actual returns across country. In this paper wavelet based variance, covariance and correlation analysis of BSE and NSE indexes financial time series have been done using index data from April 1990 to March 2006.

scholarly journals Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

2022 ◽  
Vol 19 ◽  
pp. 432-441
Author(s):  
Amin Karimi Dastgerdi ◽  
Paolo Mercorelli
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Financial Markets ◽  
Financial Time Series ◽  
Considerable Effect ◽  
Noise Elimination ◽  
Financial Time ◽  
Learning Techniques

Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.

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