Introduction 1. Introduction to Energy Trading Exchange and OTC trading Traded Contracts Settlement Fundamentals 2. Energy Contracts Time Series.

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

Power Energy: Financial and Risk Management

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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

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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.


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


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OTC trading:

Contracts are not necessarily standardized (forwards, options etc.

There is no obligation to deposit a margin and settle everyday mark-to-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.).


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Traded Contracts – On PXE


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Settlement


( )The settlement can

be:Physical:delivery of energy,payment of agreedamount

Financial:payment of thedifference betweenagreed variables

The settlement of afutures contract is

morecomplicated:

* 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


( )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


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* Source: www.ceps.cz

Fundamentals


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* Source: www.eru.cz

Fundamentals


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* Source: www.pxe.cz

Introduction

1. Introduction to Energy Trading

2. Energy Contracts Time Series Specifics


Energy Trading: Financial and Risk Management

( )

Power Contracts Time Series Specifics

Electricity is not an effectively storable asset. This fact has a strong influenceon 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( )

* Source: www.eex.com



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

considerationwhen we try to use stochastic model in risk management etc.

The so-called Jump Diffusion Mean Reverting Model is very common:

Where µ substitutes , dq represents the Poissono process where dq = 1

with the probability λ and dq = 0 with the probability 1-λ.


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;JdqtWdtdttdx

x x t


Cost of carry model

Example:Imagine, that you know, that you will need a fuel delivery on

31.12.2011. Yoursupplier offers you a price for that delivery of 2 EUR per liter. Would

you acceptthe offer?

What would you consider?

The spot price for the delivery tomorrow is 1,5 EUR.

What else do you need to know?


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Example of the relationship between spot and forward contract of storable assets:

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* Source: Reuters



Example of the relationship between spot and forward contract of non-storable asset - electricity:

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* Source: Reuters



Valuation of forward/futures contracts

Stochastické modely v energetice

( )Valuation of storable commodities forward contractsCost of carry model

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.

Valuation of nonstorable commodities forward contractsEquilibrium pricing

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).

Ft ,T St 1c T t TtTTt SEF ,,

Introduction

1. Introduction to Energy Trading

2. Energy Trading Specifics


Power Trading: Financial and Risk Management

( )

Financial and Risk management

Characteristic features of the issues related to energy tradingand connected with financial and risk management reveal plenty ofquestions which have to be solved by:

energy companies’ management consultants academics etc.

The following slides present some examples of such issues.


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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 tocalculate 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.


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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 termcontracts 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?


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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?


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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.


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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.


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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 Power Trading

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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?


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Thanks for your attention


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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.


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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