A regime switching long memory model for electricity prices

By Niels Haldrup, Morten Ørregaard Nielsen. 2006

267 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2876bf287e3aadaa0a19bd0402f427da5a06311e

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Citations in the corpus

A generalized ARFIMA process with Markov-switching fractional differencing parameter (2009, methodology): “However, the state variable considered in Haldrup and Nielsen [22] (p. 350) is observable as opposed to a standard Hamilton [23] Markov-switching model where the state variable is latent.”
Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “In the 8 Haldrup and Nielsen (2006) argue that a regime switching model is especially attractive in forecasting electricity prices.”
Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets (2016, background): “…and dynamics (e.g., Johnsen, 2001; Bessembinder and Lemmon, 2002, 2006; Longstaff and Wang, 2004; Knittel and Roberts, 2005; Park et al., 2006; Haldrup and Nielsen, 2006; Mount et al., 2006; Weron, 2006; Guthrie and Videbeck, 2007; Benth and Koekebakker, 2008; Karakatsani and Bunn, 2008;…”
A review of electricity product differentiation (2014, background): “Since spot electricity prices are highly volatile with occasional spikes [57–68], LSEs and customers may seek to manage electricity spot-price risk using such hedge instruments as, tolling agreements, forward contracts, and capacity options that are traded in financial markets [69–74].”
Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices (2007, methodology): “Periodic autoregressive models were first applied by Hannan (1955) and Jones and Brelsford (1967).”
Local Whittle Analysis of Stationary Fractional Cointegration and the Implied–Realized Volatility Relation (2007, background): “, Andersen, Bollerslev, Diebold, and Ebens 2001; Bandi and Perron 2004; Christensen and Nielsen 2006), spot prices for crude oil (Robinson and Yajima 2002), and electricity spot prices (Haldrup and Nielsen 2006).”
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach (2006, methodology): “Hamilton (1990) introduced an application of the Expectation–Maximization (EM) algorithm of Dempster et al.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “Hall and Mitchell (2007) propose a data-driven approach to the direct combination of density forecasts by taking a weighted linear combination of the competing density forecasts. The combination weights are chosen to minimize the ‘distance’, as measured by the Kullback-Leibler information criterion,”
The Nature of Power Spikes: A Regime-Switch Approach (2005, unknown): “For example, Kosater and Mosler (2006) and Kosater (2006) make day-ahead forecasts in the German power market, whereas Haldrup and Nielsen (2006) and Spagnolo and Uddin (2006) make forecasts in the Nord Pool market.”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, unknown): “Deseasonalization in conjunction with Markov regime-switching models has been performed with either of the approaches: Huisman and De Jong (2003) and Haldrup and Nielsen (2004) corrected the time series by regression on seasonal dummy variables, while Weron et al. (2004) utilized a sinusoidal…”
Locally stationary long memory estimation (2009, background): “These two approaches have been developed, respectively, in [25,18] (see also [17] where singularities in the spectral density at frequencies different from zero are considered in a piecewise stationary context).”
Long memory and regime switching: A simulation study on the Markov Regime-Switching ARFIMA model (2015, unknown): “Alternative to modelling the long memory and regime switching in separate stages, Haldrup and Nielsen (2006) propose a seasonal ARFIMA model with regime switching (RS-SARFIMA) framework to model both characteristics simultaneously for electricity price.”