Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm

2014 ◽  
Vol 232 (2) ◽  
pp. 375-382 ◽  
Author(s):  
Yih-Wenn Laih
Keyword(s):  
Time Series ◽  
Sequence Alignment ◽  
Financial Time Series ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Alignment Algorithm ◽  
Financial Time ◽  
Sequence Alignment Algorithm

Pricing Basket Options with Optimum Wavelet Correlation Measures

2011 ◽  
pp. 34-61
Author(s):  
Christopher Zapart ◽  
Satoshi Kishino ◽  
Tsutomu Mishina
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Correlation Coefficients ◽  
Adaptive Wavelet ◽  
Basket Options ◽  
Financial Time ◽  
Standard Linear ◽  
Delta Hedging ◽  
Correlation Measures ◽  
Different Time Scales

This chapter describes a new procedure for designing optimum correlation measures for financial time series. The technique attempts to overcome some of the limitations in existing methods by looking at correlations among wavelet features extracted at different time scales from the underlying time series. New correlation coefficients are further optimised with help of artificial neural networks and genetic algorithms using a nonparametric adaptive wavelet thresholding scheme. The approach is applied to the problem of pricing basket options for which the pricing formula depends on accurate measurements of correlations between portfolio constituents. When compared with standard linear approaches (i.e., RiskMetrics™), an optimised predictive wavelet correlation measure offers potentially large reductions (over 50% in some cases) in static delta-hedging errors.

scholarly journals Finding Hidden Pattern of Financial Time Series Based on Score Matrix in Sequence Alignment

2018 ◽  
Vol 8 (12) ◽  
pp. 1439-1456
Author(s):  
Yong Shi ◽  
Ye-Ran Tang ◽  
Wen Long ◽  
Ying-Jie Tian ◽  
Wen-Ning Yang
Keyword(s):  
Time Series ◽  
Sequence Alignment ◽  
Financial Time Series ◽  
Financial Time ◽  
Score Matrix

Efficient filtering of financial time series and extreme value theory

2005 ◽  
Vol 7 (2) ◽  
pp. 63-84 ◽  
Author(s):  
Kaj Nyström ◽  
Jimmy Skoglund
Keyword(s):  
Time Series ◽  
Extreme Value Theory ◽  
Financial Time Series ◽  
Value Theory ◽  
Extreme Value ◽  
Financial Time

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

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