Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

Berger, Theo

doi:10.1002/for.2384

Cited by 34 publications

(21 citation statements)

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“…These findings can be important for investors when building their expectations about future oil prices over several horizons based on the current level of uncertainty. This is also in line with findings of previous studies that already showed the relevance of wavelet decomposition for forecasting returns on financial markets (Berger, 2016;Faria and Verona, 2018;Risse, 2019). *** Insert Figure 8 about here ***…”

Section: Disaggregated Perspectivesupporting

confidence: 90%

“…on momentum trading with respect to different frequencies which enables us to examine investors' sentiment at various horizons. This decomposition mimics the heterogeneity of agents with regard to different consumption requirements, risk tolerance levels, assimilation of information, institutional constraints and heterogeneous beliefs (Chakrabarty et al, 2015) and is also able to improve return forecasts in financial markets (Berger, 2016;Faria and Verona, 2018;Risse, 2019). For instance, negative news may be seen as a selling signal for short-term investors, while long-term investors may interpret the same news as buying opportunity.…”

Section: Introductionmentioning

confidence: 97%

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Crude oil futures trading and uncertainty

Czudaj

2019

Energy Economics

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This paper examines the effect of different dimensions of uncertainty on expectations of WTI crude oil futures momentum traders at a daily level. We consider two concepts of uncertainty and two momentum trading indicators based on technical analysis. In addition, we also use wavelet techniques to decompose crude oil futures prices into different frequencies accounting for investors' sentiment at various horizons. To allow for different effects on the propagation mechanism of uncertainty shocks, we apply a time-varying Bayesian VAR approach. Our findings indicate that both measures of uncertainty affect momentum trading on the crude oil futures market in several periods, especially during the great recession between 2007 and 2009. For the decomposed futures prices our results also show that the reaction to uncertainty differs substantially across frequencies. High frequencies exhibit a very short-lived reaction to uncertainty while low frequencies show a persistent reaction to uncertainty shocks.

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Section: Disaggregated Perspectivesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 97%

Crude oil futures trading and uncertainty

Czudaj

2019

Energy Economics

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“…4 Speci…cally, we employ a particular version of a wavelet transformation of a time series called the Maximal Overlap Discrete Wavelet Transform (MODWT). 5 As an example, by using the speci…c form of a Haar wavelet …lter, any time series X t can be decomposed into a scale component S J;t and J detail components D j;t :…”

Section: Methodology and Datamentioning

confidence: 99%

“…If J is small, then the scale component includes ‡uctuations of shorter duration, which one may not normally associate with a trend. 8 An alternative interpretation is that S J;t is the 5 The MODWT version of the wavelet …lter has become the standard in the empirical …nance and forecasting literature, e.g. Berger (2016) or Faria and Verona (2018).…”

Section: Methodology and Datamentioning

confidence: 99%

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

Matthes¹,

Lubik²,

Verona³

2019

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We study the behavior of key macroeconomic variables in the time and frequency domain. For this purpose, we decompose U.S. time series into various frequency components. This allows us to identify a set of stylized facts: GDP growth is largely a high-frequency phenomenon whereby in ‡ation and nominal interest rates are characterized largely by low-frequency components. In contrast, unemployment is a mediumterm phenomenon. We use these decompositions jointly in a structural VAR where we identify monetary policy shocks using a sign restriction approach. We …nd that monetary policy shocks a¤ect these key variables in a broadly similar manner across all frequency bands. Finally, we assess the ability of standard DSGE models to replicate these …ndings. While the models generally capture low-frequency movements via stochastic trends and business-cycle ‡uctuations through various frictions, they fail at capturing the medium-term cycle.

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“…to measure the co-movements among financial time series, and nowadays there is a vast and growing literature on this topic (see for instance the following recent studies: Bartram & Wang, 2015;Berger, 2016;Durante et al, 2014Ling et al, 2015;Min & Czado, 2014;Shahzad et al, 2016;Wang & Xie, 2016;Weng & Gong, 2016). Copulas have also been used in studies related to applied economics, such as tourism (Pérez-Rodríguez et al, 2015;Tang et al, 1959;Zhu et al, 2016) and agriculture.…”

Section: Introductionmentioning

confidence: 99%

A copula-based clustering algorithm to analyse EU country diets

Lascio

Disegna

2017

Knowledge-Based Systems

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The aim of the paper is to suggest a novel clustering technique to explore the changes of the food diet in 40 European countries in accordance with common European policies and guidelines on healthy diets and lifestyles. The proposed clustering algorithm is based on copulas and it is called CoClust. The CoClust algorithm is able to find clusters according to the multivariate dependence structure of the data generating process. The database analysed contains information on the proportions of calories from 16 food aggregates in 40 European countries observed over 40 years by the Food and Agriculture Organization of the United Nations (FAO). The findings suggest that European country diets are changing, individually or as a group, but not in a unique direction. Central and Eastern European countries are becoming unhealthier, while the tendency followed by the majority of the remaining countries is to integrate the common European guidelines on healthy, balanced, and diversified diets in their national policies.

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Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

Cited by 34 publications

References 50 publications

Crude oil futures trading and uncertainty

Crude oil futures trading and uncertainty

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

A copula-based clustering algorithm to analyse EU country diets

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