18 November 2020
In the last month there have been two major price swings in key bunker ports around the world as shown below.
The first major swing occurred from Oct 16th to Nov 2nd with prices falling anywhere between $10-$30 mt .
To help further illustrate this, we have taken a closer look at Singapore during this period. Singapore VLSFO prices fell $30+ mt. If you stemmed 7-10 days in advance for delivery on Nov 1st or 2nd, the opportunity to catch the savings is lost. The price at the end of October was in the lows $310’s versus $320’s and $330’s if booked 7-10 days out, an opportunity loss $10-15mt.
The second swing occurred from Nov 2nd – Nov 11th with prices rising as much as $50mt .
If the stem was placed with 1-3 days’ notice, due to freight market uncertainty, the price impact is more than if placed 5, 7, or 10 days (normal) in advance of ETA. For example, if the delivery date were Nov 13, stemming 3 days prior the price would have been $356 versus stemming 7 days out ($331) or even 10 days out ($328), an opportunity loss $25mt.
Stem timing is crucial to good price performance:
The use of ClearLynx’s optimization & procurement tools can help create additional savings through improvement of stem timing. In some ports there can be $2-10 mt lost on most stems due to market volatility (daily and weekly), which seems to be here to stay. Our market data shows the variability of notices and the resulting impact in rising or falling markets as shown below:
A Look into the Future:
All current indicators point to prices falling in the near future.
Through our bunker procurement, pricing and analytics platform, we have unique visibility of actual and indicative pricing, product requirements, availability and completed transactions across the market. By aggregating this anonymized data, coupled with over a decade of historical data, we can provide insight into market activity, current trends and commentary on potential future outcomes.
ClearLynx Market Insights brings you the latest thoughts and findings from our team based on real data.