The Zero Lower Bound and Market Spillovers: Evidence from the G7 and Norway

This paper investigates mean and volatility spillovers between the crude oil market and three financial markets, namely the debt, stock, and foreign exchange markets, while providing international evidence from each of the seven major advanced economies (G7), and the small open oil-exporting economy of Norway. Using monthly data for the period from May 1987 to March 2016, and a four-variable VARMA-GARCH model with a BEKK variance specification, we find significant spillovers and interactions among the markets, but also absence of a hierarchy of influence from one specific market to the others. We further incorporate a structural break to examine the possible effects of the prolonged episode of zero lower bound in the aftermath of the global financial crisis, and provide evidence of strengthened linkages from all the eight international economies

Electricity Prices, Large-Scale Renewable Integration, and Policy Implications

This paper investigates the effects of intermittent solar and wind power generation on electricity price formation in Germany. We use daily data from 2010 to 2015, a period with profound modifications in the German electricity market, the most notable being the rapid integration of photovoltaic and wind power sources, as well as the phasing out of nuclear energy. In the context of a GARCH-in-Mean model, we show that both solar and wind power Granger cause electricity prices, that solar power generation reduces the volatility of electricity prices by scaling down the use of peak-load power plants, and that wind power generation increases the volatility of electricity prices by challenging electricity market exibility

Fat Tails due to Variable Renewables and Insufficient Flexibility

The large-scale integration of renewable energy sources requires flexibility from power markets in the sense that the latter should quickly counterbalance the renewable supply variation driven by weather conditions. Most power markets cannot (yet) provide this flexibility effectively as they suffer from inelastic demand and insufficient flexible storage capacity. Research accordingly shows that the volume of renewable energy in the supply system affects the mean and volatility of power prices. We extend this view and show that the level of wind and solar energy supply affects the tails of the electricity price distributions as well, and that it does so asymmetrically. The higher the supply from wind and solar energy sources, the fatter the left tail of the price distribution and the thinner the right tail. This implies that one cannot rely on symmetric price distributions for risk management and for valuation of (flexible) power assets. The evidence in this paper suggests that we have to rethink the methods of subsidizing variable renewable supply such that they take also into consideration the flexibility needs of power markets.nonPeerReviewe

Προσομοίωση για τον υπολογισμό των πιθανοτήτων επιβίωσης επιχειρήσεων και τραπεζών σε παρατεταμένη διάρκεια της δανειακής κρίσης

In the current paper, we study the stability and the survival probabilities of enterprises and banks within a prolonged duration of the debt-crisis, with Monte Carlo simulation. We utilize historical data from banks and enterprises within the debt-crisis to define crisis-variability and crisis-average values of input parameters of the simulation. We introduce the concept of equities maximum draw-down as dynamic survival indicator. Finally we estimate the survival probabilities of enterprises and banks within a prolonged duration of the debt crisi

Causality in quantiles and dynamic relations in energy markets: (De)tails matter

In this article we investigate the dynamic relations between crude oil price returns and a set of energy price returns, namely diesel, gasoline, heating, and the natural gas. This is performed by means of Granger non-causality tests for US closing spot prices over the period from January 1997 to December 2017. In previous studies this has been done by testing for the added predictive value of including lagged returns of one energy price in predicting the conditional expectation of another. In this paper we instead focus on different ranges of the full conditional distribution, and thus market states, within the framework of a dynamic quantile regression model, and identify the quantile ranges from which causality arises. The results constitute a richer set of findings than what is possible by just considering a single moment of the conditional distribution, which can be useful for implementing better substitution investment strategies and effective policy interventions. We find several interesting uni-directional dynamic relations between the employed energy price returns, especially in the tail quantiles, but also bi-directional causal relations between energy price returns for which the classical Granger non-causality test suggests otherwise. Our results are robust to alternative measures of the price of oil and different data frequencies.peerReviewe

Causality in Quantiles and Dynamic Relations in Energy Markets

In this paper we investigate the dynamic relations between crude oil price returns and a set of energy price returns, namely diesel, gasoline, heating, and the natural gas. This is performed by means of Granger non-causality tests for US spot closing prices over the period from January 1997 to December 2017. In previous studies this has been done by testing for the added predictive value of including lagged values of one energy price return in predicting the conditional expectation of another. In this paper, we instead focus on different ranges of the full conditional distribution within the framework of a dynamic quantile regression model, and identify the quantile ranges from which causality arises. The results constitute a richer set of findings than what is possible by just considering a single moment of the conditional distribution, which can be useful for implementing better substitution investment strategies and effective policy interventions. We find several interesting one-directional dynamic relations between the employed energy prices, especially in the tail quantiles, but also a bi-directional causal relation between energy prices for which the classical Granger non-causality test suggests otherwise. Our results are robust to alternative measures of the price of oil and different data frequencies.nonPeerReviewe