The relationship between stock returns and inflation: new evidence from wavelet analysis

Kim, Sangbae; In, Francis Haeuck

doi:10.1016/j.jempfin.2004.04.008

Cited by 152 publications

(96 citation statements)

References 16 publications

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“…where ℎ , = ℎ , / 2 /2 are the MODWT wavelet filters, and , = , / 2 /2 are the 12 The description of DWT and MODWT is heavily drawn from Durai & Bhaduri (2009) and Kim & In (2005) 13 As highlighted by Durai and Bhaduri (2009), for more details on MRA, readers are encouraged to see Mallat (1989) and Percival and Walden (2000).…”

Section: Cross Wavelet Phase Anglementioning

confidence: 99%

“…A biased estimator of the wavelet covariance can be constructed by simply including the MODWT wavelet coefficients effected by the boundary and normalizing. Given that covariance does not take into account the variation of the univariate time series, it is natural also to introduce the concept of wavelet correlation (Kim & In (2005)):…”

Section: Cross Wavelet Phase Anglementioning

confidence: 99%

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Are there profit (returns) in Shariah-compliant exchange traded funds? The multiscale propensity

Farouk

Masih

2016

Research in International Business and Finance

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This paper investigates the co-movement of nine Islamic Exchange Traded Fund (ETF) returns using wavelet coherence methods. The results tend to indicate consistent comovement between most of the ETF returns especially in the long run. The study also uncovers evidence of wide variation of co-movement across the time-scales during the global financial crisis and the Euro debt crisis. Strong co-movement can be observed during the global financial crisis, both for the medium term investors and long term investors. The paper also studies the relationship between different ETF returns using wavelet multi-resolution analysis. The cross-correlation analysis also shows certain significant and positive correlations between the ETF returns, especially during the period of global financial crisis. The findings from these two recent dynamic time-scale decomposition methodologies have important policy implications for risk management and investors' investment policy. Keywords IntroductionExchange Traded Funds (ETF) emanated from the innovation in the capital market.ETFs are tradable securities which derive their value from a pre-defined basket of securities which are constituents of an index. These types of ETFs derive their value (and volatility) from market movements of the underlying stocks, which comprise the portfolio, and these funds are similar to index funds managed by institutional portfolio managers.Index-linked products, such as ETFs, have been increasingly successful because they provide investors with benefits of diversification through one investment product There are several advantages of using ETF data in the study. First, these securities are liquid and give investors instant exposure to the underlying index. It is not necessary to buy a "basket" of securities to mimic the index, and ETFs are not subject to the nonsynchronous trading problems associated with stock index price data. The ETFs under study here are not vulnerable to potential biases since they are traded securities, not indices that are calculated from underlying individual stock price data (Krause & Tse (2013)).The rest of the paper is organized as follows: in Section 2, we review the literature 2 See Abdou Diaw et al (2010), which give a good analysis on the performance of Islamic ETF in Malaysia by looking at the performance of MyETF-DJIM Malaysia Titan 25 (MyETF).3 on ETF performance and the wavelet applications. In Section 3, we provide a brief introduction on the ETF considered and their unique aspect. The wavelet technique is

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Section: Cross Wavelet Phase Anglementioning

confidence: 99%

Section: Cross Wavelet Phase Anglementioning

confidence: 99%

Are there profit (returns) in Shariah-compliant exchange traded funds? The multiscale propensity

Farouk

Masih

2016

Research in International Business and Finance

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“…To define wavelet analysis and multi-resolution analysis, in the one dimensional case, firstly a refinable (scale) function φ : R → C is defined as the solution to the two scale recursion difference equation in (6).…”

Section: Multivariate Wavelet Denoising Algorithmmentioning

confidence: 99%

“…Firstly, wavelet analysis has been used to identify time varying behaviours of market behaviours, key economic variables and their correlation evolutions. A typical example would be the studies on multi-scale price movements and lead lag relationships in various financial markets [3,[6][7][8][9][10]. Empirical studies in these markets reveal the multi-scale structure of prices and evolution of their correlation.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Lai

Xiang

2012

Energies

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In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporates the mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

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“…One approach is to decompose the time series of two markets on a scale-by-scale basis into components with different frequencies using wavelets. The lead-lag relationship is studied by comparing the components of one selected level of the wavelet transformation for two markets, see e.g., [1][2][3][4][5]. More on wavelet methods in finance can be found in the book of Gençay, Selçuk and Whitcher [6].…”

Section: Introductionmentioning

confidence: 99%

Lead–Lag Relationship Using a Stop-and-Reverse-MinMax Process

Maier-Paape

Platen

2016

Risks

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Abstract:The intermarket analysis, in particular the lead-lag relationship, plays an important role within financial markets. Therefore, a mathematical approach to be able to find interrelations between the price development of two different financial instruments is developed in this paper. Computing the differences of the relative positions of relevant local extrema of two charts, i.e., the local phase shifts of these price developments, gives us an empirical distribution on the unit circle. With the aid of directional statistics, such angular distributions are studied for many pairs of markets. It is shown that there are several very strongly correlated financial instruments in the field of foreign exchange, commodities and indexes. In some cases, one of the two markets is significantly ahead with respect to the relevant local extrema, i.e., there is a phase shift unequal to zero between them.

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The relationship between stock returns and inflation: new evidence from wavelet analysis

Cited by 152 publications

References 16 publications

Are there profit (returns) in Shariah-compliant exchange traded funds? The multiscale propensity

Are there profit (returns) in Shariah-compliant exchange traded funds? The multiscale propensity

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Lead–Lag Relationship Using a Stop-and-Reverse-MinMax Process

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