Modelling energy spot prices by volatility modulated Levy-driven Volterra processes
By Ole E. Barndorff-Nielsen, Fred Espen Benth, Almut E. D. Veraart. 2013
123 citations recorded by the source corpus.
This paper introduces the class of volatility modulated Levy-driven Volterra (VMLV) processes and their important subclass of Levy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot prices can be modelled directly in stationarity. Second, stochastic volatility is regarded as a key factor for modelling energy spot prices. Third, the model allows for the possibility of jumps and extreme spikes and, lastly, it features great flexibility in terms of modelling the autocorrelation structure and the Samuelson effect. We provide a detailed analysis of the probabilistic properties of VMLV processes and show how they can capture many stylised facts of energy markets. Further, we derive forward prices based on our new spot price models and discuss option pricing. An empirical example based on electricity spot prices from the European Energy Exchange confirms the practical relevance of our new modelling framework.
Subfields: Electrical and Electronic Engineering, Finance, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/90c7e3d9797b9f0bcc1f3851ea3ea81821de568d
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In Finance
- Eduardo Abi Jaber
- Christa Cuchiero
- Martin Larsson
- Sergio Pulido
- Mikkel Bennedsen
- Asger Lunde
- Mikko S. Pakkanen
- P. R. Hansen
In Molecular Biology
Citations in the corpus
- Decoupling the Short- and Long-Term Behavior of Stochastic Volatility (2016, background): “It is shown in Barndorff-Nielsen et al. (2013) that when g is given by the so-called gamma kernel (Example 4.2 below), we can choose the normal-inverse Gaussian (NIG) distribution as the marginal distribution of X.”
- A weak solution theory for stochastic Volterra equations of convolution type (2019, background): “This type of equation appears in multiple applications, for example turbulence (Barndorff-Nielsen and Schmiegel, 2008), energy markets (Barndorff-Nielsen et al., 2013), and rough volatility modeling in finance (El Euch and Rosenbaum, 2019; Gatheral et al.”