DeepAI AI Chat
Log In Sign Up

Dynamic correlations at different time-scales with Empirical Mode Decomposition

by   Noemi Nava, et al.

The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales, at different lags and over time. We uncover a rich heterogeneity of interactions which depends on the time-scale and has important led-lag relations which can have practical use for portfolio management, risk estimation and investments.


page 10

page 11

page 14

page 15


Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices

We study the price dynamics of cryptocurrencies using adaptive complemen...

Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time

Unlike price fluctuations, the temporal structure of cryptocurrency trad...

Inspection of methods of empirical mode decomposition

Empirical Mode Decomposition is an adaptive and local tool that extracts...

Capturing episodic impacts of environmental signals

Environmental scientists frequently rely on time series of explanatory v...

Clustering volatility regimes for dynamic trading strategies

We develop a new method to find the number of volatility regimes in a no...

Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data

Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King e...