Multiscale Decomposition and Spectral Analysis of Sector ETF Price Dynamics

We present a multiscale analysis of the price dynamics of U.S. sector exchange-traded funds (ETFs). Our methodology features a multiscale noise-assisted approach, called the complementary ensemble empirical mode decomposition (CEEMD), that decomposes any financial time series into a number of intrinsic mode functions from high to low frequencies. By combining high-frequency modes or low-frequency modes, we show how to filter the financial time series and estimate conditional volatilities. The results show the different dynamics of the sector ETFs on multiple timescales. We then apply Hilbert spectral analysis to derive the instantaneous energy-frequency spectrum of each sector ETF. Using historical ETF prices, we illustrate and compare the properties of various timescales embedded in the original time series. Through the new metrics of the Hilbert power spectrum and frequency deviation, we are able to identify differences among sector ETF and with respect to SPY that were not obvious before.

Download Full-text

Spectral analysis informs the proper frequency in the sampling of financial time series data

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2011.02.016 ◽

2011 ◽

Vol 390 (11) ◽

pp. 2067-2073 ◽

Cited By ~ 1

Author(s):

Cleiton Taufemback ◽

Sergio Da Silva

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Time Series Data ◽

Financial Time Series ◽

Series Data ◽

Financial Time ◽

Proper Frequency

Download Full-text

Detection of low-frequency large-amplitude jump in financial time series

2007 46th IEEE Conference on Decision and Control ◽

10.1109/cdc.2007.4434218 ◽

2007 ◽

Cited By ~ 1

Author(s):

Hui Peng ◽

Genshiro Kitagawa ◽

Yoshiyasu Tamura ◽

Yoko Tanokura ◽

Min Gan ◽

...

Keyword(s):

Time Series ◽

Large Amplitude ◽

Financial Time Series ◽

Low Frequency ◽

Financial Time

Download Full-text

Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

IEEE Access ◽

10.1109/access.2020.2996981 ◽

2020 ◽

Vol 8 ◽

pp. 117133-117145

Author(s):

Henry Daniel Chacon ◽

Emre Kesici ◽

Peyman Najafirad

Keyword(s):

Neural Networks ◽

Time Series ◽

Empirical Mode Decomposition ◽

Recurrent Neural Networks ◽

Prediction Accuracy ◽

Financial Time Series ◽

Time Series Prediction ◽

Ensemble Empirical Mode Decomposition ◽

Financial Time ◽

Mode Decomposition

Download Full-text

Asset allocation methods using spectral analysis of financial time series

10.14711/thesis-b484318 ◽

1995 ◽

Author(s):

Larry Pak-lun Yu

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Asset Allocation ◽

Financial Time Series ◽

Financial Time ◽

Allocation Methods

Download Full-text

A spectral analysis based heteroscedastic model for the estimation of value at risk

The Journal of Risk Finance ◽

10.1108/jrf-01-2017-0012 ◽

2018 ◽

Vol 19 (3) ◽

pp. 295-314

Author(s):

Yang Zhao

Keyword(s):

At Risk ◽

Time Series ◽

Spectral Analysis ◽

Value At Risk ◽

Financial Time Series ◽

Estimation Method ◽

Reliable Estimate ◽

Content Type ◽

Financial Time ◽

Proposed Model

Purpose This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR). Design/methodology/approach The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR. Findings The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory. Practical implications The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models. Originality/value Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.

Download Full-text

Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices

International Journal of Financial Engineering ◽

10.1142/s2424786321410085 ◽

2021 ◽

pp. 2141008

Author(s):

Tim Leung ◽

Theodore Zhao

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Original Time Series ◽

Frequency Spectra ◽

Mode Decomposition ◽

Original Time ◽

Instantaneous Frequencies ◽

Instantaneous Energy

In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

Download Full-text