Comparative Study of the Evolution of Natural Gas Spot Prices in Asia and North
America
Hari Malamakkavu Padinjare Var, Energy Studies Institute, 65167083,[email protected]
Loi Tian Sheng, Allan, Energy Studies Institute, 65162349, [email protected]
Overview
The oil-indexed gas, which is the dominant pricing mechanism prevailing in Asia faces many criticisms as
the oil prices do not reflect the region’s gas market fundamentals. It is in regional interest to diversify away from oilindexed gas pricing as such a move would entail the creation of East Asia’s own regional gas trading hubs. This can
in turn generate more transparent prices reflecting the region’s own market fundamentals. Although gas prices
derived from the European and North American hubs have been used in East Asia, they do not reflect Asia’s market
fundamentals. In an effort to create a reliable and relevant spot prices in Asia and as part of hub building measures,
Singapore based SGX LNG Index Group price (Sling) has become a regional spot benchmark in East and South East
Asia since 2014. While there have been analyses of price series and interdependence with oil prices in other markets,
the impact of Sling in Asian prices and evolution of its independence from oil prices are yet to be studied. In this
paper, we use statistical techniques to study the evolution of Sling prices in relation to oil price. We find that Sling
prices are more dependent on oil when compared to Henry Hub (HH) prices, but exhibits a statistically significant
move towards independence after 2016, July. This coincides with the beginning of assessment of North Asia Sling,
and the announcement of move by Japanese government to abolish destination clauses that allow for freer trading of
the fuel.
Methods
We use Fully Modified Least Squares method (FMOLS) (Peter, 1995) to study the optimal estimates of
cointegrating regressions of gas and oil prices. In our analysis, FMOLS (Baltagi, 2013; Peter, 1995) estimator is
applied to determine rolling window regression coefficient estimates for the inter-dependent relationship between
Sling and Brent, as well as Henry Hub and WTI prices. Our data for analysis is weekly prices between October 2014
to November 2016 for both North American and Asian gas markets. We then estimate for structural breaks using the
Bai-Perron Structural Break Test which tests for multiple structural changes occurring at unknown dates in a linear
regression model (Bai & Perron, 1998). Based on the results, we analyse the importance of break dates in
understanding structural changes that might explain the reasons behind the results of our FMOLS estimates.
Subsequently, we apply Markov-Switching Regression (Hamilton, 1989) to determine unobserved price state
(regime) changes that could provide additional insights on both gas markets as they evolved over the last two years.
Results
Using the FMOLS, we estimated 86 rolling regression coefficients across both Asian and North American
LNG Spot Markets after isolating the presence of existing trends and the reliance on past prices. Sling coefficients
exhibit higher standard deviations than Henry Hub, while having normally distributed kurtosis value of 3 as
compared to 9 for Henry Hub prices. For the North American Markets, except for the starting 12-week window
between 01/10/2014 to 08/07/2015, estimation of coefficients for all the other windows are largely insignificant and
close to zero. Also, we found that Sling prices largely follow oil prices from October 2014 to early-July 2016, with
coefficients ranging largely between 0.1 to 0.3. However, as the estimates start to include observations after the 1 st
week of July 2016, coefficients started to turn insignificant and are close to zero.
Subsequent structural break analysis detected 4 major shifts between Sling gas and oil price in the period of
assessment. In particular, there is a break in July 2016, which coincides with the period after which their coefficents
became insignificant and marginal as per our esimates of FMOLS. In contrast, the test did not detect any structural
breaks for Henry Hub prices, suggesting that the drivers of HH in America has been relatively stable and
independent from movements in WTI prices.
Our Markov Switching model detected 2 distinct states that characterise the oil and gas relationship for
Sling across the full period of analysis. This is identified mainly by the gap between Brent and Sling prices (Figure 1
and 2). In general, when brent prices started to increase, Sling prices did not follow suit. This suggests that Asian
spot prices have already started to exhibit some form of independence since 2015. However, when gas prices fall,
Sling prices start to fall as well over long periods. In contrast, gas prices in North America exhibit shorter price
regimes, which does not show any discernable trend with respect to WTI prices (Figures 3 and 4).
Filtered Regime Probabilities
18
1.0
16
14
0.8
12
0.6
10
8
0.4
6
0.2
4
2
0.0
III
IV
I
II
2014
III
IV
I
II
2015
III
IV
III
2016
BRENT_AVEMMBTU
IV
I
II
2014
III
IV
SLING
Figure 2
18
1.0
II
III
IV
III
IV
2016
P(S(t)= 1)
Figure 1
I
2015
P(S(t)= 2)
Filtered Regime Probabilities
16
0.8
14
12
0.6
10
8
0.4
6
4
0.2
2
0
0.0
IV
I
II
2014
III
Conclusions
I
II
2015
HENRYHUB
Figure 3
IV
III
2016
IV
IV
2014
WTI_MMBTU
I
II
III
P(S(t)= 1)
Figure 4
IV
I
2015
II
2016
P(S(t)= 2)
Increased spot trade in Asia together with market liberalization efforts in the region has increased the global
relevance of an Asian spot price. The Singapore based Sling prices were established at this critical juncture of a
market shift. Our preliminary studies of the price relationship finds a movement away from oil indexed relationship
of the Sling price. We also find that relative supply abundance since 2015, lack of demand shocks and determined by
large consumers to buy more gas from spot market based trade may have contributed to this structural breaks.
References
Bai, J., & Perron, P. (1998). Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica,
66(1), 47-78. doi:10.2307/2998540
Baltagi, B. H. (2013). Econometric analysis of panel data, Fifth Ed. Chichester, West Sussex: John Wiley & Sons,
Inc.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business
Cycle. Econometrica, 57(2), 357-384. doi:10.2307/1912559
Peter, C. B. P. (1995). Fully Modified Least Squares and Vector Autoregression. Econometrica, 63(5), 1023-1078.
doi:10.2307/2171721