Wavelets decomposition, volatility analysis and correlation for financial indexes

Since the benefits of portfolio international diversification results appeared in financial literature, the issue of financial co-movements among different markets has been a key question. Many recent studies have approached this topic targeting a correlation investigation among financial indicators. Unfortunately, the huge majority of them focus only in the time domain, ignoring the relevant frequency domain which could differentiate long and short-run investment contribution to the energy of a time series. This paper proposes a wavelet-based volatility and correlation analysis of key financial indexes for the Brazilian, American and European markets looking for some results concerning the correlation structure between them in time and frequency domain, obtaining distinct results for long and short-run investment performance and contribution.Acontecimentos recentes no ambiente financeiro internacional suscitaram algumas questões relacionadas à inter-relação e interdependência dos diversos mercados ao longo do globo e seus graus de integração. Nesse sentido, este artigo propõe uma análise de variância e correlação para índices financeiros como o Dow Jones Industrial, o Ibovespa e o Euro Stoxx 50 através da decomposição destas séries em ondaletas. Uma vez que a metodologia de ondaletas é capaz de separar as diferentes frequências de uma série temporal ao longo do tempo (frequência-temporal), o estudo de uma estrutura de correlações e variâncias através desta metodologia é capaz de evidenciar fenômenos particulares de cada frequência de dados que, de forma agregada, são perdidos

What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis

Bitcoin has emerged as a fascinating phenomenon of the financial markets. Without any central authority issuing the currency, it has been associated with controversy ever since its popularity and public interest reached high levels. Here, we contribute to the discussion by examining potential drivers of Bitcoin prices ranging from fundamental to speculative and technical sources as well as a potential influence of the Chinese market. The evolution of the relationships is examined in both time and frequency domains utilizing the continuous wavelets framework so that we comment on development of the interconnections in time but we can also distinguish between short-term and long-term connections.Comment: 19 pages, 5 figure

Gold, Oil, and Stocks

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods

Multi-scale Causality between Energy Consumption and GNP in Emerging Markets: Evidence from Turkey

Tests results for causality between energy consumption and economic growth do not have a consensus in the financial economics literature. Empirical evidence varies on the economies examined and methodology employed. This paper proposes a wavelet analysis as a semi- parametric model for detecting multi-scale causality between electricity consumption and growth in emerging economies. Using wavelet analysis we find that in the short run there is feedback relationship between GNP and energy consumption, while in the long run GNP leads to energy consumption. Wavelet correlation between GNP and energy consumption is maximum at 3rd time-scale(5-8 years) and this shows that GNP effects electricity consumption maximally around 5-8 years later in the long-run. We also find that the magnitude of the wavelet correlation changes based on time-scales for GNP and energy consumption and thus indicate that GNP and energy consumption are fundamentally different in the long run.Economic Growth; Energy Consumption; Employment; Wavelets; Causality

On the Co-movement of Crude, Gold Prices and Stock Index in Indian Market

This non-linear 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 \textit{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 \textit{Wavelet Coherence} of the two pairs viz. 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 Indian Market. But for short term traders, it will not be effective, not to include all the three in their portfolio

The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets

This paper proposes a powerful methodology wavelet networks to investigate the effects of international F/X markets on emerging markets currencies. We used EUR/USD parity as input indicator (international F/X markets) and three emerging markets currencies as Brazilian Real, Turkish Lira and Russian Ruble as output indicator (emerging markets currency). We test if the effects of international F/X markets change across different timescale. Using wavelet networks, we showed that the effects of international F/X markets increase with higher timescale. This evidence shows that the causality of international F/X markets on emerging markets should be tested based on 64-128 days effect. We also find that the effects of EUR/USD parity on Turkish Lira is higher on 17-32 days and 65-128 days scales and this evidence shows that Turkish lira is less stable compare to other emerging markets currencies as international F/X markets effects Turkish lira on shorten time scale.F/X Markets; Emerging markets; Wavelet networks; Wavelets; Neural networks