Global evidence of time-frequency dependency of temperature and environmental quality from a wavelet coherence approach

2020 ◽  
Author(s):  
Andrew Adewale Alola ◽  
Dervis Kirikkaleli
Keyword(s):  
Environmental Quality ◽  
Wavelet Coherence ◽  
Frequency Dependency ◽  
Time Frequency ◽  
Global Evidence

scholarly journals Time–frequency dependency of financial risk and economic risk: evidence from Greece

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Dervis Kirikkaleli
Keyword(s):  
Financial Risk ◽  
Causal Link ◽  
Wavelet Coherence ◽  
Economic Risk ◽  
Frequency Dependency ◽  
Time Frequency ◽  
Long Run ◽  
Short Run ◽  
The One ◽  
Of Greece

Abstract This study aims to shed some light on the one of the most popular phenomena in the economics and finance literature—nexus between economic growth and financial development—for the case of Greece over 1990Q1 to 2018Q4 within the framework of risk. In other words, this study investigates the causal link between financial risk and economic risk in Greece using wavelet coherence tests while answering the following questions: (i) does financial risk lead to economic risk in Greece and/or does economic risk lead to financial risk in Greece, and (ii) if so, why? The wavelet coherence approach allows the study to capture the long-run and short-run causal linkages among the time series variables since the approach combines time and frequency domain causalities. The findings from wavelet coherence supports the Schumpeter hypothesis since the findings proves that there is unidirectional causality from financial risk to economic risk in Greece (i) between 1995 and 1998; (ii) between 2003 and 2013; (iii) between 2013 and 2017 at different frequency levels. The findings clearly reveal how financial risk is important predictor for economic risk in Greece over the period of 1990–2018.

scholarly journals Dynamic Responses of a Metro Train-Bridge System under Train-Braking: Field Measurements and Data Analysis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 735
Author(s):  
Xuhui He ◽  
Kehui Yu ◽  
Chenzhi Cai ◽  
Yunfeng Zou ◽  
Xiaojie Zhu
Keyword(s):  
Field Measurements ◽  
Dynamic Responses ◽  
Wavelet Coherence ◽  
Distribution Method ◽  
Bogie Frame ◽  
Time Frequency ◽  
Rigid Frame ◽  
Rigid Frame Bridge ◽  
Acceleration Signals ◽  
Bridge System

This paper focuses on the dynamic responses of a metro train–bridge system under train-braking. Experiments were performed on the elevated Metro Line 21 of Guangzhou (China). A continuous, three-span, rigid-frame bridge (42 m + 65 m + 42 m) and a standard B-type metro train were selected. The acceleration signals were measured at the center-points of the main span and one side-span, and the acceleration signals of the car body and the bogie frame were measured simultaneously. The train–bridge system’s vibration characteristics and any correlations with time and frequency were investigated. The Choi–Williams distribution method and wavelet coherence were introduced to analyze the obtained acceleration signals of the metro train–bridge system. The results showed that the Choi–Williams distribution provided a more explicit understanding of the time–frequency domain. The correlations between different parts of the bridge and the train–bridge system under braking conditions were revealed. The present study provides a series of measured dynamic responses of the metro train–bridge system under train-braking, which could be used as a reference in further investigations.

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

scholarly journals Application of the cross wavelet transform and wavelet coherence to geophysical time series

2004 ◽  
Vol 11 (5/6) ◽  
pp. 561-566 ◽  
Author(s):  
A. Grinsted ◽  
J. C. Moore ◽  
S. Jevrejeva
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Statistical Significance ◽  
Wavelet Coherence ◽  
The Arctic ◽  
Arctic Oscillation Index ◽  
Time Frequency ◽  
Cross Wavelet Transform ◽  
The Cross ◽  
Cross Wavelet

Abstract. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).

Dynamic correlation of Eastern and Western markets and forecasting: Scale-by-scale wavelet-based approach

2017 ◽  
Vol 04 (04) ◽  
pp. 1750040 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Moving Average ◽  
Wavelet Coherence ◽  
Vector Autoregressive ◽  
Frequency Space ◽  
Time Frequency ◽  
Dynamic Correlation ◽  
Frequency Scale ◽  
Highly Correlated

In this paper, dynamic four-dimensional (4D) correlation of eastern and western markets is analyzed. A wavelet-based scale-by-scale analysis method has been introduced to model and forecast stock market data for strongly correlated time intervals. The daily data of stock markets of SP500, FTSE and DAX (western markets) and NIKKEI, TAIEX and KOSPI (eastern markets) are obtained from 2009 to the end of 2016 and their co-movement dependencies on time–frequency space using 4D multiple wavelet coherence (MWC) are determined. Once the data is detached into levels of different frequencies using scale-by-scale continuous wavelet transform, all of the time series possessing the same frequency scale are selected, inversed and forecasted using multivariate model, vector autoregressive moving average (VARMA). It is concluded that the efficiency of forecasting is increased substantially using the same-frequency highly correlated time series obtained by scale-by-scale wavelet transform. Moreover, the increasing or decreasing trend of prospected price shift is foreseen fairly well.

scholarly journals On the co-movement of crude, gold prices and stock index in the Indian market

2020 ◽  
Vol 07 (03) ◽  
pp. 2050036
Author(s):  
Abhibasu Sen ◽  
Karabi Dutta Choudhury
Keyword(s):  
Wavelet Analysis ◽  
Stock Exchange ◽  
Stock Index ◽  
Wavelet Coherence ◽  
Discrete Wavelet ◽  
Indian Market ◽  
Stock Market Index ◽  
Time Frequency ◽  
Market Index ◽  
Indian Stock Market

The nonlinear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week’s closing exchange rate. Though a good correlation was obtained during some period, but as a whole no such cointegration relation can be found out. Using the Discrete Wavelet Analysis, the data was decomposed and the presence of Granger Causal relations was tested. Unfortunately, no significant relationships are being found. We then studied the Wavelet Coherence of the two pairs, namely NSE-Nifty & Gold and NSE-Nifty & Crude. For different frequencies, the coherence between the pairs have been studied. At lower frequencies, some relatively good coherence have been found. In this paper, we report for the first time the co-movements between Crude Oil, Gold and Indian Stock Market Index using Wavelet Analysis (both Discrete and Continuous), a technique which is most sophisticated and recent in market analysis. Thus, for long-term traders they can include gold and/or crude in their portfolio along with NSE-Nifty index in order to decrease the risk (volatility) of the portfolio for the Indian Market. But for short-term traders, it will not be effective, not to include all the three in their portfolio.

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