Multivariate EMD-based portfolio value at risk estimate for electricity markets

Hongqian Wang; Kaijian He; Kin Keung Lai

doi:10.1109/BIFE.2013.45

Multivariate EMD-based portfolio value at risk estimate for electricity markets

Hongqian Wang
, Kaijian He
, Kin Keung Lai

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

With the electricity market reform in recent years, the electricity price is seeing higher level of volatility and risk. Based on the Multivaraite EMD algorithm, this paper proposes a novel risk measurement approach for studying the risk trends and estimating Value at Risk (VaR) in the electricity market. The Multivariate EMD algorithm is introduced to analyze the multi-scale and the volatility behaviors of the correlation among different electricity markets. The heterogeneous data is decomposed in the proposed Multivariate EMD based VaR estimation algorithm. The decomposed time series will be calculate with the DCC-GARCH model. Empirical studies in the representative Australian electricity markets suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

Original language	English
Title of host publication	Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013
Publisher	IEEE
Pages	211-215
ISBN (Print)	9781479947775
DOIs	https://doi.org/10.1109/BIFE.2013.45
Publication status	Published - Nov 2013
Event	6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013 - Hangzhou, Zhejiang, China Duration: 14 Nov 2013 → 16 Nov 2013

Conference

Conference	6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013
Place	China
City	Hangzhou, Zhejiang
Period	14/11/13 → 16/11/13

Research Keywords

DCC-GARCH model
Empirical Mode Decomposition (EMD)
Heterogeneous Market Hypothesis (HMH)
multivariate time series model
Portfolio Value at Risk

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10.1109/BIFE.2013.45

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@inproceedings{b5f3fe2e7cf84e88a1198acaf54f7644,

title = "Multivariate EMD-based portfolio value at risk estimate for electricity markets",

abstract = "With the electricity market reform in recent years, the electricity price is seeing higher level of volatility and risk. Based on the Multivaraite EMD algorithm, this paper proposes a novel risk measurement approach for studying the risk trends and estimating Value at Risk (VaR) in the electricity market. The Multivariate EMD algorithm is introduced to analyze the multi-scale and the volatility behaviors of the correlation among different electricity markets. The heterogeneous data is decomposed in the proposed Multivariate EMD based VaR estimation algorithm. The decomposed time series will be calculate with the DCC-GARCH model. Empirical studies in the representative Australian electricity markets suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.",

keywords = "DCC-GARCH model, Empirical Mode Decomposition (EMD), Heterogeneous Market Hypothesis (HMH), multivariate time series model, Portfolio Value at Risk",

author = "Hongqian Wang and Kaijian He and Lai, \{Kin Keung\}",

year = "2013",

month = nov,

doi = "10.1109/BIFE.2013.45",

language = "English",

isbn = "9781479947775",

pages = "211--215",

booktitle = "Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013",

publisher = "IEEE",

address = "United States",

note = "6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013 ; Conference date: 14-11-2013 Through 16-11-2013",

}

Wang, H, He, K & Lai, KK 2013, Multivariate EMD-based portfolio value at risk estimate for electricity markets. in Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013., 6961123, IEEE, pp. 211-215, 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013, Hangzhou, Zhejiang, China, 14/11/13. https://doi.org/10.1109/BIFE.2013.45

Multivariate EMD-based portfolio value at risk estimate for electricity markets. / Wang, Hongqian; He, Kaijian; Lai, Kin Keung.
Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013. IEEE, 2013. p. 211-215 6961123.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Multivariate EMD-based portfolio value at risk estimate for electricity markets

AU - Wang, Hongqian

AU - He, Kaijian

AU - Lai, Kin Keung

PY - 2013/11

Y1 - 2013/11

N2 - With the electricity market reform in recent years, the electricity price is seeing higher level of volatility and risk. Based on the Multivaraite EMD algorithm, this paper proposes a novel risk measurement approach for studying the risk trends and estimating Value at Risk (VaR) in the electricity market. The Multivariate EMD algorithm is introduced to analyze the multi-scale and the volatility behaviors of the correlation among different electricity markets. The heterogeneous data is decomposed in the proposed Multivariate EMD based VaR estimation algorithm. The decomposed time series will be calculate with the DCC-GARCH model. Empirical studies in the representative Australian electricity markets suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

AB - With the electricity market reform in recent years, the electricity price is seeing higher level of volatility and risk. Based on the Multivaraite EMD algorithm, this paper proposes a novel risk measurement approach for studying the risk trends and estimating Value at Risk (VaR) in the electricity market. The Multivariate EMD algorithm is introduced to analyze the multi-scale and the volatility behaviors of the correlation among different electricity markets. The heterogeneous data is decomposed in the proposed Multivariate EMD based VaR estimation algorithm. The decomposed time series will be calculate with the DCC-GARCH model. Empirical studies in the representative Australian electricity markets suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

KW - DCC-GARCH model

KW - Empirical Mode Decomposition (EMD)

KW - Heterogeneous Market Hypothesis (HMH)

KW - multivariate time series model

KW - Portfolio Value at Risk

UR - https://www.scopus.com/pages/publications/84917692512

UR - https://www.scopus.com/pages/publications/84917692512#tab=impact

U2 - 10.1109/BIFE.2013.45

DO - 10.1109/BIFE.2013.45

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479947775

SP - 211

EP - 215

BT - Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013

PB - IEEE

T2 - 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013

Y2 - 14 November 2013 through 16 November 2013

ER -

Multivariate EMD-based portfolio value at risk estimate for electricity markets

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