A wavelet analysis of oil price volatility dynamic

In this the paper we investigate the oil price volatility, by studying the causal relationships between different volatilities captured at different time scales. We first decompose the oil price volatility at various scales of resolution or frequency ranges by using wavelet analysis. We then explore the causalities between absolute returns of oil prices at different time scales. As traditional Granger causality test, designed to detect linear causality, is ineffective in uncovering certain nonlinear causal relationships, we use the nonlinear causality test introduced by Péguin-Feissolle and Teräsvirta (1999) and Péguin-Feissolle, Strikholm and Teräsvirta (2008). Our results confirm the fact that the vertical dependence is a strong stylised fact of oil returns volatility. But, the main finding consists on the presence of a feed- back effect from high frequency traders to low frequency traders. In contrast to Gençay et al. (2010), we prove that high frequency shocks could have an impact outside their boundaries and reach the long term traders.Causality, Wavelet decomposition, oil price volatility

Wind power feed-in impact on electricity prices in Germany 2009-2013

Until quite recently no electricity system had faced the challenges associated with high penetrations of renewable energy sources (RES). In this paper, we carry out an empirical analysis for Germany, as a country with high penetration of wind energy, to investigate the well-known merit-order effect. Our main empirical findings suggest that the increasing share of wind power in-feed induces a decrease of electricity spot price level but an increase of spot prices volatility. Furthermore, the relationship between wind power and spot electricity prices can be strongly impacted by European electricity grids interconnection which behaves like a safety valve lowering volatility and limiting the price decrease. Therefore, the impacts of wind generated electricity on electricity spot markets are less clearly pronounced in interconnected systems

An analysis of storage revenues from the time-shifting of electrical energy in Germany and Great Britain from 2010 to 2016

The purpose of this paper is to investigate the level of revenues available to storage operators through the bulk time-shifting of electrical energy in Germany and Great Britain over the 7 years from 2010 to 2016, and to analyse the impact of volatility and underlying mean price on the potential revenues that a storage operator could theoretically capture. The analysis is carried out using an algorithm adapted from previous work, coupled with new empirical hourly price data from the German and Great British day-ahead electrical markets, and using characteristics typical of a pumped-storage hydropower scheme (1000 MWh, 125 MW charge and discharge, and 75% round-trip efficiency). Our results suggest that volatility rather than average price is the dominant factor affecting storage revenues, with a 1% increase in volatility implying an increase in mean daily storage revenues of €300 in Germany and £550 in Great Britain for the simulated storage plant. In comparison, an increase in underlying mean prices of €1 per MWh leads to an increase in mean daily revenue of €100 in Germany, with a £1 per MWh increase in underlying mean prices leading to an increase in mean daily revenue of £380 in Great Britain. We also find that during the period 2010–2016, the times when the highest revenue is derived have moved from late morning to early evening, which we attribute to the increase in low short-run marginal cost solar PV electricity in both markets suppressing the day-ahead wholesale electrical prices. In addition, we find a large increase in storage operator revenues in Britain in the last quarter of 2016, due to a number of events that impacted the price of electricity, however these would have been difficult to predict with any degree of certainty. This paper therefore highlights the perennial problem of forecasting the time-shifting revenue for electrical energy, with its high degree of variation from one year to the next that would undoubtedly impact the financing of these capital-intensive projects that seek to capture these variable revenues

Correlations between oil and stock markets:A wavelet-based approach

International audienceno abstrac

Noise Traders or Fundamentalists: a Wavelet Approach

International audienc

Bull or Bear markets: a wavelet dynamic correlation

International audienceIn this paper, we contribute to the literature on the international stock market co-movements and contagion, especially during the recent subprime crisis, by researching the interconnections between international stock markets in time-frequency domain.Our innovative approach consists on carrying out a wavelet decomposition of return time series before investigating the correlation dynamics across stock markets during the recent financial crisis. It thus enables us to show how the contagion dynamics between international stock market returns are changing across time scales corresponding to investors with heterogeneous time horizons. Moreover, our results reveal that the contagion dynamics depends on the bull or bear periods of stock markets, on stock markets maturity, and on regional aspects. Therefore, all these finding should be considered from an international portfolio diversification perspectiv

Modeling Nonlinear Granger Causality between the Oil price and U.S Dollar: a Wavelet based Approach

International audienc

A Wavelet Analysis of Oil Price Volatility Dynamic

International audienc

Dynamic cyclical comovements between Oil price and US GDP: A wavelet perspective

International audienc

The WVaR : a time-frequency analysis of CAC40 VaR

Malgré la multiplicité des méthodes d'estimation de la VaR, elles souffrent d'une faiblesse fondamentale. En effet, elles ne font aucune distinction entre l'information captée à basse fréquence et celle captée à haute fréquence. Ce qui revient à  supposer de façon implicite que l'information contenue dans les données historiques a la même importance quel que soit l'horizon temporel de l'investisseur c'est-à-dire sa fréquence de trading (intra-journalière, journalière, hebdomadaire, mensuelle,..). Mais, accepter une telle hypothèse revient à supposer que les marchés financiers sont homogènes. Ce qui est contraire à la réalité empirique. En effet, les marchés financiers sont caractérisés par une grande hétérogénéité d'acteurs. L'objet de notre thèse est d'apporter une contribution à l'estimation de la VaR basée sur la décomposition de la volatilité dans le domaine des fréquences. Ce qui nous permet de mette en évidence l'influence de l'hétérogénéité des horizons temporels des acteurs des marchés financiers sur l'estimation de la Value at Risk. Pour cela,nous faisons appel à un outil statistique susceptible de nous procurer de l'information temporelle sur la volatilité et de l'information fréquentielle sur la fréquence de trading des différents acteurs des marchés financiers: l'approche temps-fréquence de la transformée en ondelettes.Although multiplicity of VaR estimate approaches,they suffer from a fundamental weakness.They don't make any distiction between informations captured in a high frequency and in a low frequency manner.It is an implicit assumption of homogeneity of fiancial markets in contrast to empirical facts. In our thesis, we try to construct a VaR model based on volatility decomposition in the frequency domain.It enables us to show how the time horizons heterogeneity of financial markets participants could influence value at risk estimates.We use a statistical tool able to give us temporal information about volatility and frequencial information about trading frequencies of market participants:the time frequency approach of wavelet transform