Department of Economics and Business Economics

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

Research output: Working paper/Preprint Working paperResearch

Standard

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange. / Lunde, Asger; Olesen, Kasper Vinther.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2013.

Research output: Working paper/Preprint Working paperResearch

Harvard

APA

Lunde, A., & Olesen, K. V. (2013). Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2013-19

CBE

MLA

Lunde, Asger and Kasper Vinther Olesen Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2013-19). 2013., 33 p.

Vancouver

Author

Lunde, Asger ; Olesen, Kasper Vinther. / Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange. Aarhus : Institut for Økonomi, Aarhus Universitet, 2013. (CREATES Research Papers; No. 2013-19).

Bibtex

@techreport{3c26c374fcaa4c4fa3384a917e9e7537,
title = "Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange",
abstract = "We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit can be obtained in-sample by utilizing high-frequency data compared to standard models that use only daily observations. Additionally, we show that the intraday sampling frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized volatility measures. For the conditional variance, a small gain is found, but for densities the opposite is the case. We conclude that realized measures of volatility developed in recent years must be used with caution in this market, and importantly that the use of high-frequency financial data in this market leaves much room for future research.",
keywords = "Financial Volatility, Realized GARCH, High Frequency Data, Electricity, Power, Forecasting, Realized Variance, Realized Kernel, Model Confidence Set, Volatility, Realized GARCH, High-Frequency Data, Electricity, Power, Forecasting, Realized Variance, Realized Kernel, Model Confidence Set, Predictive Likelihood",
author = "Asger Lunde and Olesen, {Kasper Vinther}",
year = "2013",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2013-19",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

AU - Lunde, Asger

AU - Olesen, Kasper Vinther

PY - 2013

Y1 - 2013

N2 - We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit can be obtained in-sample by utilizing high-frequency data compared to standard models that use only daily observations. Additionally, we show that the intraday sampling frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized volatility measures. For the conditional variance, a small gain is found, but for densities the opposite is the case. We conclude that realized measures of volatility developed in recent years must be used with caution in this market, and importantly that the use of high-frequency financial data in this market leaves much room for future research.

AB - We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit can be obtained in-sample by utilizing high-frequency data compared to standard models that use only daily observations. Additionally, we show that the intraday sampling frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized volatility measures. For the conditional variance, a small gain is found, but for densities the opposite is the case. We conclude that realized measures of volatility developed in recent years must be used with caution in this market, and importantly that the use of high-frequency financial data in this market leaves much room for future research.

KW - Financial Volatility, Realized GARCH, High Frequency Data, Electricity, Power, Forecasting, Realized Variance, Realized Kernel, Model Confidence Set

KW - Volatility, Realized GARCH, High-Frequency Data, Electricity, Power, Forecasting, Realized Variance, Realized Kernel, Model Confidence Set, Predictive Likelihood

M3 - Working paper

T3 - CREATES Research Papers

BT - Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -