Articlegreen2004

THE NORMAL INVERSE GAUSSIAN DISTRIBUTION AND SPOT PRICE MODELLING IN ENERGY MARKETS

96 citations0/0 files storedInternational Journal of Theoretical and Applied Finance

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stochastic processes and financial applications Financial Markets and Investment Strategies Market Dynamics and Volatility

Abstract

We model spot prices in energy markets with exponential non-Gaussian Ornstein–Uhlenbeck processes. We generalize the classical geometric Brownian motion and Schwartz' mean-reversion model by introducing Lévy processes as the driving noise rather than Brownian motion. Instead of modelling the spot price dynamics as the solution of a stochastic differential equation with jumps, it is advantageous from a statistical point of view to model the price process directly. Imposing the normal inverse Gaussian distribution as the statistical model for the Lévy increments, we obtain a superior fit compared to the Gaussian model when applied to spot price data from the oil and gas markets. We also discuss the problem of pricing forwards and options and outline how to find the market price of risk in an incomplete market.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 96 citations exist upstream.

References

1999Option valuation using the fast Fourier transform 2001Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics 1997Processes of normal inverse Gaussian type 2000Energy derivatives : pricing and risk management 1997Energy Risk: Valuing and Managing Energy Derivatives 2002Energy and Power Risk Management

Cited by

2009A two-factor model for the electricity forward market 2012TIME‐CHANGED ORNSTEIN–UHLENBECK PROCESSES AND THEIR APPLICATIONS IN COMMODITY DERIVATIVE MODELS 2010THE STOCHASTIC VOLATILITY MODEL OF BARNDORFF-NIELSEN AND SHEPHARD IN COMMODITY MARKETS 2007Valuing Volatility and Variance Swaps for a Non‐Gaussian Ornstein–Uhlenbeck Stochastic Volatility Model 2005A Note on Merton's Portfolio Selection Problem for the Schwartz Mean-Reversion Model 2018Stochastic Modeling of Wind Derivatives in Energy Markets 2014Tail risk in energy portfolios 2010Dynamic copula models for the spark spread

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.