It is almost spring…nearly time for Flame. Flame is one of Europe’s leading natural gas and LNG conferences. It is the largest annual meeting and attracts more than 650 people from the industry like gas producers and consumers, infrastructure providers, traders and regulators.
This year the Flame conference is again in Amsterdam, at the Okura Hotel, from 13-15 May 2019. KYOS is proud to be associate sponsor of this event.
This year the event is only three days, starting with the Traders’ day on Monday. As a result, the agenda is more compact, and filled with interesting topics and panels. KYOS is delighted to announce that we will give a presentation on Wednesday 15 May. Cyriel de Jong will present his views on the economics of storage. His presentation focuses on the question: what is the role of storage and above all: how can it remain economically viable?
Stream C: Storage
For more information about the event, please visit the Flame Conference website.
If you wish to arrange a quick meeting during Flame, please send us an e-mail: email@example.com. Alternatively, you can use the mail in the black section below, just select “plan meeting”. We look forward meeting you in Amsterdam! Also, if you haven’t booked your admission tickets for this event, let us know. Since we sponsor this event, we can provide you with a discount code. Don’t hesitate to ask!
The models KyStore and KySwing will provide the underlying data for the presentation of Cyriel de Jong. KyStore supports traders and portfolio managers in natural gas markets. This gas storage optimization software raises first of all revenues from gas storage trading operations. Furthermore, it provides accurate valuations and reduces risk with adequate hedge recommendations. The model uses advanced stochastics including Least Squares Monte Carlo techniques to capture the full optionality in gas storage facilities.
KySwing helps to generate most income from gas contracts by optimizing the contract flexibility. Lisewise, the risk on future income is reduced by forward hedging. The model applies advanced stochastics to find the optimal exercise.