Monday, August 01, 2016

13. Paper (2016): PSC Sliding Scale as a Fiscal Model for Marginal Fields in Indonesia

The Production sharing contract (PSC) system has been applied in the oil and gas upstream business in Indonesia since 1966. The contract between the oil and gas companies as contractors and the government as the owner of the country’s oil and gas fields relies on a risk sharing principle. Nevertheless, the companies are actually exposed to further uncertainties, particularly related to risks in exploration activities.

Furthermore, economic analysis needs to be performed once hydrocarbons have been discovered. At this stage, oil and gas companies are faced with investment decisions that have to allow for both the upside and downside of the related risks. In fact, it is now more challenging to find economic levels of hydrocarbon reserves in place. More flexible fiscal terms are therefore required to balance the risk between investors and the government.

This paper demonstrates a model of dynamic fiscal terms that incorporates a risk-balancing principle. The PSC is used as a basic model combined with a sliding scale that is derived from a r-factor (ratio-factor) of company revenue and investment costs. As a result, it concludes that the PSC sliding scale model could be implemented in a way that balances the risks and rewards between government and investors and leads to the development of marginal fields in addition to attracting more oil and gas investment in Indonesia.

Keywords: Fiscal System, Production Sharing Contract (PSC), Sliding Scale, R-Factor, Marginal Fields.

*Find out more on Academia.

Wednesday, July 01, 2015

Melihat Hubungan Future dan Spot Oil Price

Apakah oil price berdasarkan kontrak future mempengaruhi oil price hari ini? Secara intuisi ada pengaruh, karena keduanya menggunakan underlying yang sama, crude oil. Secara statistik, hubungan tersebut bisa diuji dengan data dan hipotesis statitistik.

Seperti yang dibahas pada perilaku harga minyak dunia, bahwa terdapat dua jenis pasar di pasar minyak dunia, pertama pasar fisik (wet oil market) dan kedua pasar kertas (paper oil market). Pasar fisik adalah transaksi sesungguhnya ketika penjual menjual dan mengirimkan minyak kepada pembeli. Sedangkan pasar kertas adalah menjadikan minyak sebagai instrument investasi (spekulasi), baik dalam bentuk future contract yang diperjualbelikan, dibundel dengan komoditi yang lain, bahkan dijadikan underlying asset untuk berbagai macam produk derivatives.  

Untuk uji empiris interaksi harga di kedua pasar tersebut, digunakan metode granger causaility, sebagai berikut.

1. Data oil price (Sumber:  EIA) (1)
Monthly average price dari Januari 1986 - Januari 2015, karena harga minyak mempunyai kecenderungan mempunyai tren bulanan daripada harian, salah satunya karena musim dingin atau panas. 
Spot prices: Crude Oil WTI
Future prices: NYMEX Futures Prices, Crude Oil (Light-Sweet, Cushing, Oklahoma): contract-3 adalah harga untuk delivery 3 bulan kemudian. Contohnya harga Januari 2000, delivery April 2000.

2. Pre-treatment data
Grafik harga minyak spot dan future, semua pengolahan data menggunakan software Eviews.
Terlihat keduanya punya pola naik dan bergerak bersama. Hal ini akan dipertimbangkan dalam stationary test. 
 
3. Stationary tes
Stationary test perlu dilakukan untuk data time series untuk menghindari kesimpulan yang janggal, dimana dua variable terlihat mempunyai hubungan tapi keduanya sebenarnya tidak berhubungan (2).
Hipotesis awal (H0): terdapat unit root atau data tidak stationary
Hipotesis alternatif (H1): tidak terdapat unit root atau data stationary --> yang diharapkan.  

Test dilakukan pertama dengan data level (asli) dengan memilih mode intercept/constant dan trend, karena dari grafik diatas terlihat seperti tren.  Namun, hasil uji menggunakan Augmented Dickey Fuler (ADF) test untuk data level baik spot prices dan future tidak menolak H0, sehingga uji stationary dilanjutkan menggunakan data first difference (growth/return). Grafik growth kedua harga seperti di bawah, dimana tidak terlihat seperti tren naik seperti yang ditunjukan data level diatas.
Hasil run stationary test data first difference (FD) kedua harga sebagai berikut, dimana pada level FD kedua data menunjukan nilai t-statistics negatif melebihi critical values (CV) pada 95% confidence level (5% significance level/SL, rule of thumb), maka untuk model dan analisis menggunakan data FD.


4. VAR (Vector Auto-Regressive) Model
VAR model digunakan untuk menguji causality test menggunakan Granger causality. VAR model dibangun dengan mengkombinasikan antara future dan spot. Karena kita tidak tahu secara jelas bagaimiana hubungan antara future dan spot (siapa mempengaruhi siapa), maka disusun dalam urutan di VAR model: Rfuture Rspot. Urutan ini sangat berpengaruh dalam variance decomposition dan impulse response analysis (2). Tapi kita tidak akan melakukannya karena model VAR belum tentu model yang terbaik menjelaskan hubungan antara future dan spot ini (terdapat model time series selain VAR yang harus dibandingkan).

VAR model dibangun pertama dengan asumsi lag-3, karena 3 bulan future price. Hasilnya sebagai berikut.
Kemudian, dilakukan analisis berapa lag yang optimal untuk VAR model ini, hasilnya seperti di bawah.

Dari lag analysis tersebut, ada dua kandidat lag yaitu lag 1 dan 7. Kita memilih lag-1 karena lebih masuk akal bahwa harga future 3 bulan pada bulan lalu (lag-1) mungkin akan berpengaruh terhadap spot price bulan ini. Sehingga model VAR dengan lag-1 sebagai berikut.

5. Granger causality
Granger causality test akan menguji apakah variable 1 granger cause variable 2 (perhatikan bahwa istilah yang digunakan adalah granger cause, bukan mutlak cause). Pengujian dengan hipotesis dan test statitistik.
H0 : No causality
H1 : Causality
Dari tabel diatas, terlihat bahwa kita tidak dapat menolak H0 karena prob (p-value) 0.365 lebih besar dari 0.05 (5%) SL, sehingga future prices 3 bulan tidak granger cause spot prices. Sebaliknya, spot prices juga tidak granger cause future prices 3 bulan karena prob (p-value) 0.99 lebih besar dari 5% SL.

6. Test price spread
Untuk melihat lebih jauh hubungan harga future dan spot ini, dilakukan analisis perbedaan harga (spread) future dan spot, apakah perbedaan harganya signifikan lebih dari nol karena harga future secara normal perlu lebih tinggi untuk men-cover biaya penyimpanan, interest rate dan resiko. Grafik di bawah perbedaan harga future dan spot.

Uji statistik sebagai berikut.
H0 : rata-rata spread (mean) = 0
H1 :  mean > 0
Secara statistik, kita tidak bisa menolak H0 karena CV 5% SL untuk 1-sided test adalah 1.65, sedangkan t-value table diatas 0.9927, atau probability 0.3216 > 0.05. Sehingga tidak ada spread secara statistik antara harga future dan spot pada 5% SL.

7. Conclusion
Dari analisis diatas, secara statitistik future prices 3 bulan tidak granger cause spot prices begitu pula sebaliknya. Hal ini juga sesuai dengan studi dimana tidak ada bukti empiris bahwa pasar future (pasar kertas) mempengaruhi pasar spot (pasar fisik) setelah 2003 karena pasar future juag dimotori oleh motif spekulatif (3). 

Meskipun dalam prakteknya, sangat mungkin keduanya sangat diperhatikan oleh para traders. Pasar fisik akan melihat pergerakan harga di pasar kertas yang melihat ekspektasi masa depan, sedangkan pasar kertas akan melihat riil transaksi hari ini untuk membangun ekspektasi tersebut. Meskipun terdapat kenaikan signifikan aktivitas pasar kertas dalam satu dekade terakhir (4), aktivitas tersebut bagaimanapun secara jangka panjang tetap bersandarkan pada interaksi supply-demand crude oil.


Referensi:
(1) EIA. 2015, Available: http://www.eia.gov/
(2) Brooks, C. 2008, “Introductory Econometrics for Finance second edition”, Cambridge University Press, New York
(3) Kilian, L. 2014, "Oil Price Shocks: Causes and Consequences", vol. CEPR Discussion Paper No. DP9823, no. Centre for Economic Policy Research (CEPR).
(4) EIA 2014, 10 July 2014-last update, What drives crude oil prices? An analysis of 7 factors that influence oil markets, with chart data updated monthly and quarterly

Tuesday, June 23, 2015

[Poster] Production Sharing Contract (PSC) Sliding Scale



Note: Poster had been presented at the International Student Energy Conference (ISES) 2015, 10 - 13 June 2015, Bali, Indonesia.

Thursday, March 05, 2015

12. Journal Paper (2015); Understanding Risk and Return in Bidder’s EPC Price Using CAPM Concept

Abstract
Projects are currently characterized by their complexity, size and intensified multiparty involvement. It is therefore difficult to meet between company’s and contractor’s expectation in terms of project objectives. Using different type of contract, contractor may have different view that is reflected from their price.

In EPC contract that uses a concept of lump sum contract, price submitted by contractor is a promise of each contractor to deliver the project. Contractor will then face risk in executing the project, while in the same time they will have its potential return.


This paper describes behaviour of contractors in submitting price as part of bidding process using CAPM concept. It mathematically proves that risk and return will be going to the same way, e.g. high risk may have high return. It concludes that the price heavily rely on contractor’s experience and risk profile.

Keywords: Engineering Procurement Construction (EPC), Contract Price, Capital Asset Pricing Model (CAPM), Risk and Return.

***

Friday, February 06, 2015

Renewable Energy

There are some key reasons why we should deeply think about Renewable Energy (RE);
- Greenhouse gas (GHG) emission lead to climate change that is the mother of all externalities
- Carbon dioxide would be worth 1.5 percent of world income, if all emissions were priced at price of US$ 15/tC02; consumption from fossil fuels system accounts the majority of global GHG
- Demand for energy and associated services, to meet social and economic development and improve human welfare and health, is increasing
- Many countries are committed to develop RE in the future, regardless how well its commitment has been realised.

Worldwide RE's role in energy mix application is shown in table below (IPCC, 2011).

We may debate that RE is expensive thing, at least compared to non-RE or conventional energy. It may be true if we have a look below graph (IPCC, 2011). However, the range of RE cost varies since it still depends on each project. In other words, RE technology heavily rely on utilising technology to conform with its environment. Some countries can't apply the cheaper technology since it has no RE sources. In fact, there are some RE that are cheaper than conventional ones.

Nevertheless, this some reasons may enlighten us, why RE can't actually be stopped by oil price movement (Bloomberg, 2015).

1) The Sun Doesn't Compete With Oil
Oil is for cars; renewables are for electricity. The two don’t really compete. Oil is just too expensive to power the grid, even with prices well below $50 a barrel. Instead, solar competes with coal, natural gas, hydro, and nuclear power. Solar, the newest to the mix, makes up less than 1 percent of the electricity market today but will be the world’s biggest single source by 2050, according to the International Energy Agency

2. Electricity Prices Are Still Going Up
The real threat to renewables isn’t cheap oil; it’s cheap electricity. Cheap oil don’t make cheap electricity as well, since fuel isn’t the only component of the electricity bill. Consumers also pay to get the electricity from power plant to home

3. Solar Prices Are Still Going Down
As time passes, the efficiency of solar power increases and prices fall. This is soon or later going to happen. When many countries enforces more RE in their energy mix policy, there will be new economic of scale for RE technologies.
But, there are some issues for further implementing RE:
- Under most conditions, increasing the share of RE in the energy mix will require policies to stimulate changes in the energy system.
- Many RE resources include natural unpredictability and variability over time scale, which can constrain the ease of integration and result in additional system costs.
- There might be a few technical limits to the integration of RE technologies across the very broad range of present energy supply systems

To conclude,
- RE sources have a large potential to displace emissions of GHG from the combustion of fossil fuels and thereby to mitigate climate change.
- Many RE technologies are becoming market competitive although some innovative RE technologies are not yet mature.
- Some policies have been shown to be effective and efficient in rapidly increasing RE deployment. However, there is no one-size-fits-all policy.
- Some policies need to take into account the different stages of RE deployment.

References:
- Intergovernmental Panel on Climate Change, 2011, Renewable Energy Sources and Climate Change Mitigation, Cambridge University Press
-http://www.bloomberg.com/news/articles/2015-01-30/seven-reasons-cheap-oil-can-t-stop-renewables-now