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Dr Ekaterina Abramova currently works at the Department of Management Science and Operations, London Business School. Ekaterina does research in Machine Learning focusing on Reinforcement Learning, Econometrics, Probability Theory and Statistics. Ekaterina holds a PhD in Artificial Intelligence and Machine Learning from Imperial College London (2016), MSc in Financial Engineering from Birkbeck (2010) and has interest in Finance and trading. Ekaterina has worked at the New York Stock Exchange as a futures and options analyst, and at Marex Trading as a proprietary derivatives trader.
An important revenue stream for electric battery operators is often arbitraging the hourly price spreads in the day-ahead auction. The optimal approach to this is challenging if risk is a consideration as this requires the estimation of density functions. Since the hourly prices are not normal and not independent, creating spread densities from the difference of separately estimated price densities is generally intractable. Thus, forecasts of all intraday hourly spreads were directly specified as an upper triangular matrix containing densities. The model was a flexible four-parameter distribution used to produce dynamic parameter estimates conditional upon exogenous factors, most importantly wind, solar and the day-ahead demand forecasts. These forecasts supported the optimal daily scheduling of a storage facility, operating on single and multiple cycles per day. The optimization is innovative in its use of spread trades rather than hourly prices, which this paper argues, is more attractive in reducing risk. In contrast to the conventional approach of trading the daily peak and trough, multiple trades are found to be profitable and opportunistic depending upon the weather forecasts.
Ekaterina Abramova; Derek Bunn. Optimal Daily Trading of Battery Operations Using Arbitrage Spreads. Energies 2021, 14, 4931 .
AMA StyleEkaterina Abramova, Derek Bunn. Optimal Daily Trading of Battery Operations Using Arbitrage Spreads. Energies. 2021; 14 (16):4931.
Chicago/Turabian StyleEkaterina Abramova; Derek Bunn. 2021. "Optimal Daily Trading of Battery Operations Using Arbitrage Spreads." Energies 14, no. 16: 4931.
Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions.
Ekaterina Abramova; Derek Bunn. Forecasting the Intra-Day Spread Densities of Electricity Prices. Energies 2020, 13, 687 .
AMA StyleEkaterina Abramova, Derek Bunn. Forecasting the Intra-Day Spread Densities of Electricity Prices. Energies. 2020; 13 (3):687.
Chicago/Turabian StyleEkaterina Abramova; Derek Bunn. 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices." Energies 13, no. 3: 687.