Adding Dummy Variables: A Simple Approach for Improved Volatility Forecasting in Electricity Market

[1]  Yaojie Zhang,et al.  Investors’ perspective on forecasting crude oil return volatility: Where do we stand today? , 2021, Journal of Management Science and Engineering.

[2]  Xu Gong,et al.  Effects of structural changes on the prediction of downside volatility in futures markets , 2021 .

[3]  Jian Chai,et al.  Structural analysis and forecast of gold price returns , 2021 .

[4]  M. Wahab,et al.  The role of oil futures intraday information on predicting US stock market volatility , 2020 .

[5]  Tae-Hwy Lee,et al.  Forecasting using supervised factor models , 2019, Journal of Management Science and Engineering.

[6]  Xinyu Zhang,et al.  Versatile HAR model for realized volatility: A least square model averaging perspective , 2019, Journal of Management Science and Engineering.

[7]  A. Ciarreta,et al.  Realized volatility and jump testing in the Japanese electricity spot market , 2018, Empirical Economics.

[8]  Yaojie Zhang,et al.  Forecasting oil futures price volatility: New evidence from realized range-based volatility , 2018, Energy Economics.

[9]  Qingling Duan,et al.  Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets , 2018, Energy Economics.

[10]  Genaro Sucarrat,et al.  Equation-by-equation estimation of multivariate periodic electricity price volatility , 2018, Energy Economics.

[11]  Yaojie Zhang,et al.  Forecasting the oil futures price volatility: Large jumps and small jumps , 2018 .

[12]  Zhihong Jian,et al.  The Effect of Market Quality on the Causality between Returns and Volatilities: Evidence from CSI 300 Index Futures , 2018 .

[13]  Xu Gong,et al.  Structural breaks and volatility forecasting in the copper futures market , 2018 .

[14]  A. Clements,et al.  Point process models for extreme returns: Harnessing implied volatility , 2018 .

[15]  Aitor Ciarreta,et al.  Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices , 2017 .

[16]  Alan Moreira,et al.  Volatility Managed Portfolios , 2016 .

[17]  A. Clements,et al.  Forecasting the variance of stock index returns using jumps and cojumps , 2017 .

[18]  A. Hurn,et al.  The Effect of Transmission Constraints on Electricity Prices , 2017 .

[19]  T. Andersen,et al.  Short-Term Market Risks Implied by Weekly Options , 2017 .

[20]  Xu Gong,et al.  Forecasting the volatility of crude oil futures using HAR-type models with structural breaks , 2016 .

[21]  A. Ciarreta,et al.  Modeling realized volatility on the Spanish intra-day electricity market , 2016 .

[22]  Jerzy A. Filar,et al.  Australian electricity market and price volatility , 2016, Ann. Oper. Res..

[23]  Kris Boudt,et al.  Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity , 2016 .

[24]  Derek W. Bunn,et al.  Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models , 2016 .

[25]  Duc Khuong Nguyen,et al.  Global financial crisis and spillover effects among the U.S. and BRICS stock markets , 2016 .

[26]  Wei Chen,et al.  Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models , 2016 .

[27]  Feng Ma,et al.  Forecasting the realized volatility in the Chinese stock market: further evidence , 2016 .

[28]  A. Opschoor,et al.  Forecasting Value-at-Risk Under Temporal and Portfolio Aggregation , 2015 .

[29]  Paresh Date,et al.  Electricity futures price models: Calibration and forecasting , 2015, Eur. J. Oper. Res..

[30]  Sjur Westgaard,et al.  A Comparison of Implied and Realized Volatility in the Nordic Power Forward Market , 2015 .

[31]  Yudong Wang,et al.  Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy? , 2014, Manag. Sci..

[32]  Benoît Sévi,et al.  Forecasting the volatility of crude oil futures using intraday data , 2014, Eur. J. Oper. Res..

[33]  Apostolos Serletis,et al.  Energy markets volatility modelling using GARCH , 2014 .

[34]  Kevin Sheppard,et al.  Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility , 2013, Review of Economics and Statistics.

[35]  Atilla Cifter Forecasting electricity price volatility with the Markov-switching GARCH model: Evidence from the Nordic electric power market , 2013 .

[36]  Jing Shi,et al.  Applying ARMA–GARCH approaches to forecasting short-term electricity prices , 2013 .

[37]  M. C. Recchioni,et al.  The Analysis of Real Data Using a Multiscale Stochastic Volatility Model , 2013 .

[38]  Duc Khuong Nguyen,et al.  Long memory and structural breaks in modeling the return and volatility dynamics of precious metals , 2012 .

[39]  S. Koopman,et al.  Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models , 2012, Review of Economics and Statistics.

[40]  P. Solibakke,et al.  Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data , 2011 .

[41]  Erik Haugom,et al.  Forecasting Spot Price Volatility Using the Short-Term Forward Curve , 2010 .

[42]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[43]  Carl J. Ullrich,et al.  Realized Volatility and Price Spikes in Electricity Markets: The Importance of Observation Frequency , 2009 .

[44]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[45]  Philip Gray,et al.  A New Approach to Characterizing and Forecasting Electricity Price Volatility , 2008 .

[46]  T. Bollerslev,et al.  A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures , 2008 .

[47]  Siem Jan Koopman,et al.  Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices , 2007 .

[48]  F. Diebold,et al.  Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility , 2005, The Review of Economics and Statistics.

[49]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[50]  P. Hansen A Test for Superior Predictive Ability , 2005 .

[51]  M. Dacorogna,et al.  Defining efficiency in heterogeneous markets , 2001 .

[52]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[53]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[54]  G. C. Tiao,et al.  Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance , 1994 .

[55]  Carolina García-Martos,et al.  Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities , 2013 .

[56]  David G. Loomis,et al.  Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs , 2012 .

[57]  Janine Leschke,et al.  Documents de Travail du Centre d ’ Economie de la Sorbonne Transitional Labour Markets , from theory to policy application . Can transitional labour markets contribute to a less traditional gender division of labour ? , 2008 .

[58]  S. Hammoudeh,et al.  The impact of the Asian crisis on the behavior of US and international petroleum prices , 2004 .

[59]  P. Perron,et al.  Computation and Analysis of Multiple Structural-Change Models , 1998 .