Using extreme value theory to measure value-at-risk for daily electricity spot prices

By K. F. Chan, P. Gray. 2006

175 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/05840e453ab5ba4035edda04f4eb2a71f87129c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. M. Nieto
2. Esther Ruiz
3. Pilar Abad
4. Carmen López
5. Sonia Benito
6. Marco Rocco

In Electrical and Electronic Engineering

1. Marco Rocco

In Finance

1. M. Nieto
2. Esther Ruiz
3. Pilar Abad
4. Carmen López
5. Sonia Benito
6. Marco Rocco

Citations in the corpus

• A comprehensive review of Value at Risk methodologies (2014, background): “Chan and Gray (2006) proposed a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of the returns as well as leverage effects via an EGARCH specification.”
• Frontiers in VaR forecasting and backtesting (2016, background): “Also, the recent deregulation has also heightened the importance of risk management in electricity markets; see, for example, Chan and Gray (2006).”
• Extreme Value Theory for Finance: A Survey (2012, methodology): “Chan and Gray (2006) use an AR-EGARCH model with t innovations to take into account weekly seasonality in conditional volatility and leverage effects, while Bali (2007) uses a GJR-GARCH model with skewed t innovations.”