An Intuitive Guide to Wavelets for Economists

Crowley, Patrick M.

doi:10.2139/ssrn.787564

Cited by 64 publications

(51 citation statements)

References 39 publications

(36 reference statements)

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“…Moreover, because of the translation and scale properties, nonstationarity in data is not a problem when using wavelets and prefi ltering is not needed (Pinho in Madaleno, 2009). Wavelet analysis is suitable for detecting seasonal and cyclical patterns, structural breaks, trend analysis, fractal structures and multiresolution analysis (Crowley, 2005).…”

Section: Introductionmentioning

confidence: 99%

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Dajčman¹

2013

Prague Economic Papers

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Abstract:This paper investigates multiscale interdependence between the stock markets of Germany, Austria, France, and the United Kingdom. Wavelet energy additive decomposition was analyzed to investigate which scales capture the most energy (volatility), whereas a wavelet cross-correlation estimator was used to analyze comovement and lead/lag relationship between stock markets' return dynamics on a scale-by-scale basis. The main fi ndings of the paper are as follows. First, major fi nancial market crises had a signifi cant impact on return volatility of investigated stock markets. Among them, the global fi nancial crisis of 2007-2008 had the greatest and the most durable impact. Second, the lowest scale (associated with stock markets' return dynamics over a 2-4 days horizon) and the second lowest scale (associated with stock markets' return dynamics over 4-8 days horizon) MODWT (maximal overlap discrete wavelet transform) decompositions of stock markets' returns captured the greatest share (together about 70-80%) of indices' returns volatility. Third, comovement between stock market returns is a scale-dependent phenomenon. Fourth, a strong comovement between stock market returns of Germany, France, and the United Kingdom exists at all scales, while the Austrian stock market is less correlated with the three biggest stock markets in Europe. Fifth, the dynamics of stock market returns seems to be well time-synchronized at daily (raw returns) and the lowest scale (scale ) return decomposition as most of the return innovations are transmitted between stock markets intraday. Sixth, at the highest investigated scale (associated with stock markets' return dynamics over a 64-128 days horizon), signifi cant leads and lags between dynamics of stock markets' returns were detected. The time-synchronization of the stock markets' return dynamics for investments of 64 to 128 days horizon is less perfect than for investments of shorter investment horizons.

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Section: Introductionmentioning

confidence: 99%

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Dajčman¹

2013

Prague Economic Papers

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“…In the result section we will use the VECM model from section There is increasing interest in adopting the wavelet technique to explore and forecast various dynamic features of economic and financial time series by, for example, Ramsey (1999), Schleicher (2002) and Crowley (2005), Vidakovic (1999), Percival and Walden (2000) and Gençay et al (2001). However, we find few applications in studies of agricultural commodities.…”

Section: The Wavelet Methodsmentioning

confidence: 99%

Future World Market Prices of Milk and Feed Looking into the Crystal Ball

Hansen

2015

SSRN Journal

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Both the world milk price and the world feed price have become more volatile during the last 7-8 years. The ability of dairy farmers to adapt quickly to these changing circumstances will be a key driver for future success, considering that feed is the major cost component in milk production and that the milk market is highly volatile. This development has increased the need for research on price dynamics and price forecasting. The first aim of this paper is to apply the wavelet multi-resolution analysis (MRA) to investigate the cyclical dynamics embedded in and between the world milk and feed prices. Second, the aim is to explore both the long and short interactions and the impulse response functions (IRF) between the two price series in the system of a vector error correction model (VECM). Third, the aim is to produce reliable forecasts for both the milk and the feed price applying a SARIMA model, a VECM model and wavelet MRA.We collected the world milk price and the world feed price series from 2002 to 2015 from the International Farm Comparison Network (IFCN). The analysis revealed that the two price series contain business cycles of approximately 32 months. Further, the two series share a long-run relationship, they are co-integrated, with the feed price as the leading variable. The results also revealed that a combination of different forecasting models can provide reasonably good forecasts of both prices for a period of one year ahead.

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“…Schleicher et al [37] were among the first to present a full practical introduction to wavelets for economists, in which some basic ideas and related economic applications of the wavelet were illustrated. Crowley [38] improved the work in [37] by adding more examples of applications and more detailed explanations about different wavelets and wavelet transforms. Furthermore, they provided various software sources and a practical guide for researchers to follow their empirical experiment.…”

Section: Applications Of Wavelet In Financial Analysismentioning

confidence: 99%

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

Liu

Jin

2016

Entropy

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Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP) was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT) method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI) 300 futures in the emerging China Financial Futures Exchange (CFFEX) market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.

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An Intuitive Guide to Wavelets for Economists

Cited by 64 publications

References 39 publications

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Future World Market Prices of Milk and Feed Looking into the Crystal Ball

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

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