3. Energy Trading

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(
Power Energy: Financial and Risk Management
Introduction
)
1. Introduction to Energy Trading




Exchange and OTC trading
Traded Contracts
Settlement
Fundamentals
2. Energy Contracts Time Series Specifics
3. Energy Trading: Financial and Risk
Management
(
Introduction to Energy Trading
Exchange and OTC Trading
)
What does energy trading mean?
Subject A is selling energy (the energy company – the producer or the
trading company)
+
Subject B is buying energy (the energy user or the trading company)
+
they agree on the amount (MWh), price (EUR/MWh), delivery
period, and delivery region
=
An energy trade (e.g. P PXE CZ BL M10-11)
(
Introduction to Energy Trading
Traded Contracts
)
Traded contracts:
Spot contracts day ahead:
Traded today with the delivery on the
following day (or the following few days
when the trade takes place before a holiday o
or weekend).
Spot hours contracts:
Traded during the current day, with delivery
on the same day, even during the next hour.
Futures/Forward contracts:
Traded today with delivery during a specified
period in the future.
Options:
The buyer of the option gains the right, but
not the obligation, while the seller incurs the
corresponding obligation to fulfill the
transaction.
(
Introduction to Energy Trading
Exchange and OTC Trading
)
Trading via exchange:

Standardized contracts: Futures (standardized forward), Options,
Options on futures etc.

Obligation to deposit margin and settle everyday mark-to-market
P/L which bears the cost of funding.

Almost no counterparty/credit risk as the central
counterparty/clearing house acts as the settlement guarantor.

The number of power exchanges increased dramatically during the
last decade
(
Introduction to Energy Trading
Exchange and OTC Trading
)
(
Introduction to Energy Trading
Exchange and OTC Trading
)
OTC trading:

Contracts are not necessarily standardized (forwards, options etc.

There is no obligation to deposit a margin and settle everyday markto-market P/L.

Counterparty risk exists in the case of OTC trading.

An OTC trade can be direct between two counterparties, or via
broker (Wallich Energy etc.).
(
Traded Contracts – On PXE
)
Introduction to Energy Trading
(
Settlement
)
Introduction to Energy Trading
The settlement can be:
Physical:
delivery of energy,
payment of agreed
amount
Financial:
payment of the
difference between
agreed variables
The settlement of a
futures contract is more
complicated:
* Source:
Botterud, A., Bhattacharyya, A., Illic, M. (2002).: Futures and Spot Prices - An Analysis of the Scandinavian Electricity Market.
34th North American Power Symposium, 2002.
(
Fundamentals
)
Introduction to Energy Trading
Supply
Demand
Short term
Weather,
readiness of the
generators, etc.
Weather, time of
day, day of week,
season, etc.
Long term
Prices of the
primary energy
sources,
technological
advances,
primary energy
source mix, etc.
Economic cycle,
climate condition,
technological
advances etc.
(
Fundamentals
)
Introduction to Energy Trading
Load in MWh during 23.8.2011
10,000,000
9,000,000
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Load
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
* Source:
www.ceps.cz
(
Fundamentals
)
Introduction to Energy Trading
Load: Demand for Electricity
75000
70000
65000
60000
55000
50000
45000
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
40000
* Source:
www.eru.cz
(
Fundamentals
)
Introduction to Energy Trading
Prices of Monthly Futures Contracts
(26.8.2011)
F PXE CZ BL
M11-11
66
F PXE CZ BL
M01-11
64
F PXE CZ BL
M11-11
62
F PXE CZ BL
M02-12
F PXE CZ BL
M12-11
60
58
56
54
F PXE CZ BL
M09-11
52
50
48
1.8.07
* Source:
www.pxe.cz
1.9.07
1.10.07
1.11.07
1.12.07
1.1.08
(
Energy Trading: Financial and Risk Management
Introduction
)
1. Introduction to Energy Trading
2. Energy Contracts Time Series Specifics
3. Energy Trading: Financial and Risk
Management
(
Introduction to Energy Trading
Power Contracts Time Series Specifics
)
Electricity is not an effectively storable asset. This fact has a strong
influence
on the time series characteristic of spot and term power contracts:
Spot contracts:
 Power time series are more volatile than common financial time series
 Extreme jumps are very common (jump diffusion)
 Price tends to revert to the fundamental equilibrium level (mean reverting)
 Time series are usually seasonal
 Return distribution does not have the characteristics of normal distribution
Forward/Futures contracts
 Limited storability and therefore, limited or no usage of the cost-of-carry
model
 Low liquidity and specific liquidity characteristics – liquidity increase with
decreasing time to contract maturity/delivery period.
And many other specifics!
(
Introduction to Energy Trading
Power Contracts Time Series Specifics
)
EEX Spot Base Load Day-Ahead
350
300
250
200
150
100
50
0
1.1.2006
-50
-100
* Source:
www.eex.com
1.1.2007
1.1.2008
1.1.2009
1.1.2010
1.1.2011
(
Power Contracts Time Series Specifics
)
Introduction to Energy Trading
High volatility and jumps
Electricity spot day-ahead time series are very volatile with extreme jumps.
Reason for this include non-storability, unexpected outages and
effect of renewable sources. These facts have to be taken into consideration
when we try to use stochastic model in risk management etc.
The so-called Jump Diffusion Mean Reverting Model is very common:
dxt   dt   dtW t   Jdq;
Where µ substitutes x  xt , dq represents the Poissono process where dq = 1
with the probability λ and dq = 0 with the probability 1-λ.

(
Power Contracts Time Series Specifics
)
Introduction to Energy Trading
Cost of carry model
Example:
Imagine, that you know, that you will need a fuel delivery on 31.12.2011. Your
supplier offers you a price for that delivery of 2 EUR per liter. Would you accept
the offer?
What would you consider?
The spot price for the delivery tomorrow is 1,5 EUR.
What else do you need to know?
(
Power Contracts Time Series Specifics
)
Introduction to Energy Trading
Example of the relationship between spot and forward contract of storable
assets:
25.5
25
24.5
EURCZK
24
EURCZK12M
23.5
* Source:
Reuters
7.1.2011
6.1.2011
5.1.2011
4.1.2011
3.1.2011
2.1.2011
1.1.2011
12.1.2010
11.1.2010
10.1.2010
9.1.2010
8.1.2010
23
(
Introduction to Energy Trading
Power Contracts Time Series Specifics
)
Example of the relationship between spot and forward contract of non-storable
asset - electricity:
80
70
60
50
40
EEX spot day ahead
30
EEX futures year ahead
20
10
* Source:
Reuters
7.1.2011
6.1.2011
5.1.2011
4.1.2011
2.1.2011
3.1.2011
1.1.2011
12.1.2010
11.1.2010
10.1.2010
9.1.2010
8.1.2010
0
(
Stochastické modely v energetice
Power Contracts Time Series Specifics
)
Valuation of forward/futures contracts
Valuation of storable commodities forward
contracts
Cost of carry model
Valuation of nonstorable commodities forward
contracts
Equilibrium pricing
Ft,T  St  1 c
Ft ,T  EST    t ,T
Where c represents costs of the commodity carry,
T is the time of forward contract maturity
(delivery start), St is the spot contract price and
Ft,T is the forward / futures contract price, which
is traded today with maturity/delivery starting at
time T.
Actual Price of the forward contract at time t
for delivery period starting at time T (Ft,T) is
given by the expected spot price of the
electricity for the period T (E(ST)) and its risk
premium (πt,T).
T t

(
Power Trading: Financial and Risk Management
Introduction
)
1. Introduction to Energy Trading
2. Energy Trading Specifics
3. Energy Trading: Financial and Risk
Management
(
Introduction to Energy Trading
Financial and Risk management
)
Characteristic features of the issues related to energy trading
and connected with financial and risk management reveal plenty of
questions which have to be solved by:




energy companies’ management
consultants
academics
etc.
The following slides present some examples of such issues.
(
Introduction to Energy Trading
Financial and Risk management
)
Storability and supply + transmission management
Electricity cannot be economically stored. However, the load
(demand for power) cannot be predicted with 100 % certainty. A generator can
not be turned off or turned on in a second. Furthermore, we have to
calculate with the start up and shut down costs.

How we should manage the supply?

How we should manage the transmission and the balance in the
transmission system?
The market with so-called purchased services exists.
(
Introduction to Energy Trading
Financial and Risk management
)
Storability and term contracts valuation
As it was presented above, there is no clear connection between the electricity
spot price and the forward price. The cost of carry model cannot be applied, as
the storability of electricity is not very common. The question of term
contracts valuation arises:

What should the real value of the forward/futures contracts be?

Is equilibrium pricing the best model to explain the value of
forward/futures contracts?
(
Introduction to Energy Trading
Financial and Risk management
)
Forecasting – different deterministic factors than those, known from
finance
The analysis of fundamental factors which determine the value of shares,
bonds, FX rates, interest rates, etc. have been the center of attention for the last
several decades. However, power is traded and the fundamental factors of its
value have to be examined as well. Forecasting of load (demand) value and its
determinants is still a very important issue. Should we use

Standard and well known tools such regression and correlation
analysis etc?

Such tools, as fuzzy logic, neural networks or other?
(
Introduction to Energy Trading
Financial and Risk management
)
Costs allocation
The energy for final customers is usually bought from the electricity wholesale
market. Some portion could be bought as a base load month contract and
some as a day-ahead spot contract. Costs should be somehow reallocated
between the final customers.
The problem is that customers can turn on or turn off an electric device at
any time and it is hard to identify who consumed electricity bought at a lower or
higher price.
This is an interesting management and risk management issue.
(
Introduction to Energy Trading
Financial and Risk management
)
Risk management issues
Electricity time series characteristics, and trading business itself, reveal several
risk management issues which have to be solved within each company:

Calculation of Value at Risk figures (model development and
calibration)

Monitoring of risk management exposures (sensitivity analysis,
stress testing, position limits monitoring etc.).

Counterparty/credit risk management, modeling of the future
potential exposure etc.
(
Introduction to Power Trading
Financial and Risk management
)
Long term investments
The investment into an energy generator device is usually very long term.
However future electricity prices are very uncertain, and might change
dramatically. In the long term period, the political changes might be very
influential and unpredictable.
These facts open issues of

particular parameters forecasting

project financing

business model set up
(
Introduction to Energy Trading
Financial and Risk management
)
Transmission
Electricity is usually produced in a few large generators and has to be
transfered to final consumers through a transmission system.
How effective can the transmission be; how significant are the losses?
Imagine a European integrated electricity market; is that possible?
What is market coupling?
(
Introduction to Energy Trading
Financial and Risk management
)
Thanks for your attention
(
Introduction to Energy Trading
References
)
ARLT, J., M., ARLTOVÁ, M.: Ekonomické časové řady. Grada Publishing, Praha, 2007.
BARAN, J.: Analýza a porovnání různých modelů pro Value at Risk na nelineárním portfoliu. Katedra
pravděpodobnosti a matematické statistiky, 2009.
CARTEA, A., MARCELO, G.F.: Pricing in Elektricity Markets: a mean reverting jump diffusion model with
seasonality. University of London, 2005
CULOT, M., GOFFIN, V., LAWFORD,S. a kol: An Affine Jump Diffusion Model for Electricity. Electrabel SA,
2006.
CRAINE, R., LOCHSTOER L., SYRTVEIT, K.: Estimation of a Stochastic-Volatility Jump-Diffusion Model.
University of California at Berkley, 2000.
ČULÍK, M., VALECKÝ, J.: Non-linear Modelling of Electricity Price: Self Exciting Threshold Auto-Regressive
Approach. Mezinárodní konference Finanční řízení podniků a finančních institucí, Ostrava, 2009.
DIXIT, A.K., PINDYCK R.S.: Investment Under Uncertainty, Princeton University Press, Princeton, NJ, 1994
EMBRECHTS, P., MCNEAIL, A., STRAUMANN, D.: Correlation: Pitfalls and Alternatives. ETH Zentrum,
Zurich, 1999.
GARCIA FRANCO, J.C.: Maximum likelihood estimation of mean reverting process.
HORNÍK, T., DRAHOVZAL, O.: Nová rizika v energetice – velkoobchodní trh s elektřinou. Ekonomika a
Management, Praha, 2008.
(
Introduction to Energy Trading
References
)
LYZANETS, N., SENCHYNA, M.: Comparing different Value-at-Risk models for hedge funds. University of
Lausanne, 2005.
LYZANETS, N., SENCHYNA, M.: Comparing different Value-at-Risk models for hedge funds. University of
Lausanne,
2005.
MEYER-BRANDIS, T.,TANKOV, P.: Multi-factor jump-diffusion models of elektricity prices. Europlace
Institute of
Finance, 2006.
PAPEŽ, M.: Verifikace VaR modelu – back testing
PAPEŽ, M.: Stochastické modelování úrokových sazeb
RUEDIGER, K., SCHINDLMAYR G., REIK, H. B.: A Two-Factor Model for the Electricity Forward Market.
Universtat
Karlsruhe, Karlsruhe, 2005
ECC margining. Leipzig: ECC, AG, 2010.
Trading Rules. Praha: Power Exchange Central Europe, a.s., 2010
www.pxe.cz
http://en.wikipedia.org/wiki/Credit_score
http://en.wikipedia.org/wiki/Credit_rating
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