Evaluating electricity price volatility risk in competitive environment based on ARMAX-GARCHSK-EVT model

2014 ◽  
Vol 50 (3/4) ◽  
pp. 174 ◽  
Author(s):  
Jin Wang ◽  
Ruiqing Wang
2013 ◽  
Author(s):  
Michael Frommel ◽  
Xing Han ◽  
Stepan Kratochvil

2014 ◽  
Vol 44 ◽  
pp. 492-502 ◽  
Author(s):  
Michael Frömmel ◽  
Xing Han ◽  
Stepan Kratochvil

Author(s):  
R.C. Leme ◽  
J.B. Turrioni ◽  
P.P. Balestrassi ◽  
A.C. Zambroni de Souza ◽  
P.S. Santos

Ledger ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Sungil Kim

All existing secured loans, including crypto-secured loans, are provided under the condition that the collateral entrusted by the borrower is kept safe during the loan term. In other words, they use a one-way collateral function. Thus, a frequent drawback of these loans is that the collateral value increases if and only if the collateral price increases. To resolve this problem, this paper proposes a new crypto-secured lending system incorporating a new two-way collateral function. It would allow a borrower to invest proportions of their own collateral by predicting the market in both directions to make profits irrespective of whether the price of the collateral increases or decreases. This benefits the borrower since profit can be made even if the price of the collateral drops, by betting on the price decrease. This new lending system could include a new hedged portion, unlike traditional secured lending systems. As a result, larger loans can be made under this arrangement; further, this portion provides the advantage of reducing the underlying collateral price volatility risk.


2021 ◽  
Author(s):  
◽  
Caroline Moy

<p>This thesis considers the conventional SARIMA model and the EVT-GARCH model for forecasting electricity prices. However, we find that these models do not adequately capture the important characteristics of the electricity price data. A new model is developed, the EVT-SARIMA model, for forecasting electricity prices which is found to be the best at modelling the nature of the electricity prices. A time series of half-hourly electricity price data from the Hayward node in New Zealand is transformed into a daily average price series and using this resulting series, appropriate models are fitted for estimating and forecasting.  The new EVT-SARIMA model is used to simulate 1000 time series of daily electricity prices, over a 90 day period, to consider strategies for managing the risk associated with price volatility. The effects of different financial instruments on the cumulative distribution functions of predicted revenue obtained using our model are considered. Results suggest that different contracts have different effects on the predicted revenue. However, all contracts have the effect of reducing variability in the predicted revenue values and thus, should be used by a risk manager to reduce the range of probable revenue values. The quantity traded and which contracts to use is dependent on the objectives of the risk manager.</p>


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