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2014 | Buch

Functional Representation, Approximation, Bases and Wavelets Kapitel lesen Erstes Kapitel lesen

Wavelet Applications in Economics and Finance

herausgegeben von: Marco Gallegati, Willi Semmler

Verlag: Springer International Publishing

Buchreihe : Dynamic Modeling and Econometrics in Economics and Finance

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book deals with the application of wavelet and spectral methods for the analysis of nonlinear and dynamic processes in economics and finance. It reflects some of the latest developments in the area of wavelet methods applied to economics and finance. The topics include business cycle analysis, asset prices, financial econometrics, and forecasting. An introductory paper by James Ramsey, providing a personal retrospective of a decade's research on wavelet analysis, offers an excellent overview over the field.

Inhaltsverzeichnis

Frontmatter
Functional Representation, Approximation, Bases and Wavelets
Abstract
After stressing the importance of analyzing the various basis spaces, the exposition evaluates the alternative bases available to wavelet researchers. The next step is to demonstrate the impact of choice of basis for the representation or projection of the regressand. The similarity of formulating a basis is explored across a variety of alternative representations. This development is followed by a very brief overview of some articles using wavelet tools. The comparative advantage of wavelets relative to the alternatives considered is stressed.
James B. Ramsey

Macroeconomics

Frontmatter
Does Productivity Affect Unemployment? A Time-Frequency Analysis for the US
Abstract
The effect of increased productivity on unemployment has long been disputed both theoretically and empirically. Although economists mostly agree on the long run positive effects of labor productivity, there is still much disagreement over the issue as to whether productivity growth is good or bad for employment in the short run. Does productivity growth increase or reduce unemployment? This paper try to answer this question by using the property of wavelet analysis to decompose economic time series into their time scale components, each associated to a specific frequency range. We decompose the relevant US time series data in different time scale components and consider co-movements of productivity and unemployment over different time horizons. In a nutshell, we conclude that, according to US post-war data, productivity creates unemployment in the short and medium terms, but employment in the long run.
Marco Gallegati, Mauro Gallegati, James B. Ramsey, Willi Semmler
The Great Moderation Under the Microscope: Decomposition of Macroeconomic Cycles in US and UK Aggregate Demand
Abstract
In this paper the relationship between the growth of real GDP components is explored in the frequency domain using both static and dynamic wavelet analysis. This analysis is carried out separately for both the US and the UK using quarterly data, and the results are found to be substantially different in the two countries. One of the key findings in this research is that the “great moderation” shows up only at certain frequencies, and not in all components of real GDP. We use these results to explain why the incidence of the great moderation has been so patchy across GDP components, countries and time periods. This also explains why it has been so hard to detect periods of moderation (or otherwise) reliably in the aggregate data. We argue it cannot be done without breaking the GDP components down into their frequency components across time and these results show why: the predictions of traditional real business cycle theory often appear not to be upheld in the data.
Patrick M. Crowley, Andrew Hughes Hallett
Nonlinear Dynamics and Wavelets for Business Cycle Analysis
Abstract
We provide a signal modality analysis to characterize and detect nonlinearity schemes in the US Industrial Production Index time series. The analysis is achieved by using the recently proposed “delay vector variance” (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using Fourier and wavelet-based surrogates. A complex Morlet wavelet is employed to detect and characterize the US business cycle. A comprehensive analysis of the feasibility of this approach is provided. Our results coincide with the business cycles peaks and troughs dates published by the National Bureau of Economic Research (NBER).
Peter Martey Addo, Monica Billio, Dominique Guégan

Volatility and Asset Prices

Frontmatter
Measuring the Impact Intradaily Events Have on the Persistent Nature of Volatility
Abstract
In this chapter we measure the effect a scheduled event, like the opening or closing of a regional foreign exchange market, or a unscheduled act, such as a market crash, a political upheaval, or a surprise news announcement, has on the foreign exchange rate’s level of volatility and its well documented long-memory behavior. Volatility in the foreign exchange rate is modeled as a non-stationary, long-memory, stochastic volatility process whose fractional differencing parameter is allowed to vary over time. This non-stationary model of volatility reveals that long-memory is not a spurious property associated with infrequent structural changes, but is a integral part of the volatility process. Over most of the sample period, volatility exhibits the strong persistence of a long-memory process. It is only after a market surprise or unanticipated economic news announcement that volatility briefly sheds its strong persistence.
Mark J. Jensen, Brandon Whitcher
Wavelet Analysis and the Forward Premium Anomaly
Abstract
Forward and corresponding spot rates on foreign exchange markets differ so that forward rates cannot be used as unbiased predictors for future spot rates. This phenomenon has entered the literature under the heading of the Forward Premium Anomaly. We argue that standard econometric analyses implicitly assume that the relationship is time scale independent. We use wavelet analysis to decompose the exchange rate changes, and the forward premia, using the maximal overlap discrete wavelet transform (MODWT). Then we estimate the relationship on a scale-by-scale basis, thereby allowing for market inefficiencies such as noise, technical, and feedback trading as well as fundamental and rational trading. The results show that the forward premia serve as unbiased predictors for exchange rate changes (unbiasedness hypothesis) for certain time scales only. Monthly and weekly data concerning Euro, US-dollar and British Pound for forward periods from 1 month to 5 years is analysed. We find that the unbiasedness hypothesis cannot be rejected if the data is reconstructed using medium-term and long term components. This is most prevalent in the forward transaction periods up to 1 year.
Michaela M. Kiermeier
Oil Shocks and the Euro as an Optimum Currency Area
Abstract
We use wavelet analysis to study the impact of the Euro adoption on the oil price macroeconomy relation in the Euroland. We uncover evidence that the oil-macroeconomy relation changed in the past decades. We show that after the Euro adoption some countries became more similar with respect to how their macroeconomies react to oil shocks. However, we also conclude that the adoption of the common currency did not contribute to a higher degree of synchronization between Portugal, Ireland and Belgium and the rest of the countries in the Euroland. On the contrary, in these countries the macroeconomic reaction to an oil shock became more asymmetric after adopting the Euro.
Luís Aguiar-Conraria, Teresa Maria Rodrigues, Maria Joana Soares
Wavelet-Based Correlation Analysis of the Key Traded Assets
Abstract
This chapter reveals the time-frequency dynamics of the dependence among key traded assets—gold, oil, and stocks, in the long run, over a period of 26 years. Using both intra-day and daily data and employing a variety of methodologies, including a novel time-frequency approach combining wavelet-based correlation analysis with high-frequency data, we provide interesting insights into the dynamic behavior of the studied assets. We account for structural breaks and reveal a radical change in correlations after 2007–2008 in terms of time-frequency behavior. Our results confirm different levels of dependence at various investment horizons indicating heterogeneity in stock market participants’ behavior, which has not been documented previously. While these key assets formerly had the potential to serve as items in a well-diversified portfolio, the events of 2007–2008 changed this situation dramatically.
Jozef Baruník, Evžen Kočenda, Lukas Vacha

Forecasting and Spectral Analysis

Frontmatter
Forecasting via Wavelet Denoising: The Random Signal Case
Abstract
In the paper we evaluate the usability of certain wavelet-based methods of signal estimation for forecasting economic time series. We concentrate on extracting stochastic signals embedded in white noise with the help of wavelet scaling based on the non-decimated version of the discrete wavelet transform. The methods used here can be thought of as a type of smoothing, with weights depending on the frequency content of the examined processes. Both our simulation study and empirical examination based on time series from the M3-JIF Competition database show that the suggested forecasting procedures may be useful in economic applications.
Joanna Bruzda
Short and Long Term Growth Effects of Financial Crises
Abstract
Growth theory predicts that poor countries will grow faster than rich countries. Yet, growth in developing countries has been consistently lower than growth in developed countries. The poor economic performance of developing countries coincides with both long-lasting and short-lived financial crises. In this paper, we analyze to what extent financial crises can explain low growth rates in developing countries. We distinguish between inflation, currency, banking, debt, and stock-market crises and separate the short- and long-run effects of them. Our results show that financial crises have reduced growth and that policy decisions have caused them to be worsened and/or extended.
Fredrik N. G. Andersson, Peter Karpestam
Measuring Risk Aversion Across Countries from the Consumption-CAPM: A Spectral Approach
Abstract
Using the Consumption-CAPM, Campbell (2003, Consumption-based asset pricing, Constantinides G, Harris M, Stulz R (eds), Handbook of the economics of finance, Amsterdam, North-Holland) reports cross-country evidence that imply implausibly large coefficients of relative risk aversion, thus confirming the “equity premium puzzle” in an international context. In this paper we adopt a spectral approach to re-estimate the values of risk aversion over the frequency domain. Our findings indicate that at lower frequencies risk aversion falls substantially across countries, thus yielding in many cases reasonable values of the implied coefficient of risk aversion.
Ekaterini Panopoulou, Sarantis Kalyvitis
Metadaten
Titel
Wavelet Applications in Economics and Finance
herausgegeben von
Marco Gallegati
Willi Semmler
Copyright-Jahr
2014
Electronic ISBN
978-3-319-07061-2
Print ISBN
978-3-319-07060-5
DOI
https://doi.org/10.1007/978-3-319-07061-2

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