scholarly journals A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 115 ◽  
Author(s):  
Xin Liu ◽  
Jiang Wu ◽  
Chen Yang ◽  
Wenjun Jiang
Keyword(s):  
Time Series ◽  
Portfolio Selection ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Simulation Studies ◽  
Lower Tail ◽  
Financial Time ◽  
The Matrix ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

scholarly journals Using a Financial Training Criterion Rather than a Prediction Criterion

1997 ◽  
Vol 08 (04) ◽  
pp. 433-443 ◽  
Author(s):  
Yoshua Bengio
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Portfolio Selection ◽  
Financial Training ◽  
Financial Time Series ◽  
Traditional Approach ◽  
Financial Time ◽  
Squared Error ◽  
Noisy Time Series ◽  
Gains And Losses

The application of this work is to decision making with financial time series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time series that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading. Experiments were performed on portfolio selection with 35 Canadian stocks.

scholarly journals Financial Time Series: Market Analysis Techniques Based on Matrix Profiles †

2021 ◽  
Vol 5 (1) ◽  
pp. 45
Author(s):  
Eoin Cartwright ◽  
Martin Crane ◽  
Heather J. Ruskin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Market Analysis ◽  
Analysis Techniques ◽  
Financial Time ◽  
Motif Identification ◽  
Initial Identification ◽  
The Matrix ◽  
Individual Market ◽  
Time Periods

The Matrix Profile (MP) algorithm has the potential to revolutionise many areas of data analysis. In this article, several applications to financial time series are examined. Several approaches for the identification of similar behaviour patterns (or motifs) are proposed, illustrated, and the results discussed. While the MP is primarily designed for single series analysis, it can also be applied to multi-variate financial series. It still permits the initial identification of time periods with indicatively similar behaviour across individual market sectors and indexes, together with the assessment of wider applications, such as general market behaviour in times of financial crisis. In short, the MP algorithm offers considerable potential for detailed analysis, not only in terms of motif identification in financial time series, but also in terms of exploring the nature of underlying events.

DETECTING AND MODELING TAIL DEPENDENCE

2004 ◽  
Vol 07 (03) ◽  
pp. 269-287 ◽  
Author(s):  
FABIO BELLINI ◽  
GIANNA FIGÀ-TALAMANCA
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Financial Data ◽  
Garch Models ◽  
Serial Dependence ◽  
Simple Method ◽  
Financial Time ◽  
Empirical Investigations

The aim of this work is to develop a nonparametric tool for detecting dependence in the tails of financial data. We provide a simple method to locate and measure serial dependence in the tails, based on runs tests. Our empirical investigations on many financial time series reveal a strong departure from independence for daily logreturns, which is not filtered out by usual Garch models.

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