The Energy Portfolio Management

Published Date: 02 Nov 2017

MBA (Energy Trading)

Saumya Sharma

Assistant Professor

College of Management & Economic Studies

University of Petroleum & Energy Studies

Dehradun â€“ 248 006Guided by:

Submitted by:

Siddharth saxena

SAP ID: 500014665

Roll No.: R590211028

MBA (Energy Trading)

2011-2013

upes_logo1

College of Management and Economic Studies

University of Petroleum and Energy Studies,

Dehradun, Uttarakhand, India

MARCH, 2013

Certificate of Originality

This is to hereby state with the intention of this report is very original in every sense of the terms and conditions and it carries a sense of honour and belief and that no shortcuts have been taken and I remained both meticulous and caring during the prevalence of this research work. I have put in my point best to keep this work as informative and precise as possible.

It may be also stated here that during the preparation of this report some help has been taken from a scope of professionally shared information & knowledge, a comprehensive description of which has been mention in the references chapter of this report.

Dated:

Signature:

Siddharth saxena

R-590211028

MBA â€“ Energy Trading (2011-13)

University of Petroleum & Energy Studies

Dehradun

Acknowledgement

I acknowledge with thanks the help, guidance and the support that I have received during research work.

I express deep sense of gratitude to Assistant Prof. Saumya Sharma, for her enormous help and valuable guidance and suggestion during the research work.

I must also thank Mr. Shailendra Pokhriyal, Head of Department, Energy Trading, COMES, UPES for his cooperation and suggestion which enabled me carry out this research work in a better manner.

Siddharth saxena

MBA (Energy Trading),

University of Petroleum & Energy Studies,

Dehradun,

Uttarakhand

Table of Contents

STATEMENT OF THE PROPOSAL

PROBLEM STATEMENT

"ENERGY PORTFOLIO MANAGEMENT" â€“ is the research work that aims of creating a dynamic portfolio management process with analyzing all the risks that are related with the energy portfolio and how they impact the valuation of the same. It is aimed to provide best immunity to the portfolio from the known risks.

BACKGROUND

The term "portfolio management" has a long history in the finance and investment. Under that name and others, the same risk management concepts and

techniques have long been applied to procurement of commodities, including energy utility procurement of fuels and purchased power In recent years, the term has begun to be used in the energy industry to describe actual or suggested approaches to default service resource management and trading in business that have restructured energy industry. However, application of portfolio management concepts need not be confined to energy deficient markets.

Energy portfolio management helps utilities like cost-efficiently procure energy by tracking changes in market behavior and consumption, changes in suppliers in deregulated markets, market price volatility, and supply risk. Study also addresses a high-value; cost-effective solution that can reduce risk and provide solid return on investment. Energy portfolio management is a comprehensive study for management of sales and procurement portfolios in the energy industry or energy-intensive companies

.OBJECTIVES

The following are the key objectives of the topic:

Price volatility which is tolerable for trader, taking into account

the means at their disposal for managing that risk?

How portfolio depends on the level and instability of prices?

Timeframe for which the proposed strategy apply?

What stability level of prices is expected to result during that time?

How sensitive is the expected level

What flexibility is available and what point of timeframe

SCOPE OF RESEARCH

This dissertation would include various risks which can occur while managing a energy profile. The time duration for collection and analysis of data would be factor that should be most relevant and help provide much needed immunity to the portfolio. This work would be done for energy sector and specially designed model for current valuation of assert i.e. portfolio.

RESEARCH METHODOLOGY

RESEARCH DESIGN

The research is descriptive as well as imperial as it will be conducted on the basis earlier research that has been conducted in this area of risk. Insights form market professionals would be obtain to know the "Ground Realities" of the risks involved and existing valuation models.

SOURCE OF DATA

This research will be based on Secondary data collected from

1. Journals

2. Industry Professionals

3. Past Data models

STATISTICAL/MATHEMATICAL TOOLS

Lattice model

Binomial option pricing model

Local volatility

Monte Carlo method for option pricing

Valuation of option

Fourier analysis

Taylor analysis

Bernoulli process

Markov chain

Stochastic process

Geometric Brownian motion

Stochastic volatility

PRICING THEORY

Dynamics-Free Pricing

Pricing Under Bernoulli Dynamics

Black-Scholes Dynamics

American Options and â€˜Exoticsâ€™

Models with Uncertain Volatility

Discontinuous Processes

Interest-Rate Dynamics

INTRODUCTION

In energy portfolios across the hedge funds it is generally observed to have much concentration on the crude oil or other petroleum products. To mitigate this exposes and divide across the energy spectrum we have taken crude oil , natural gas, coal and propane as commodities ,this will help us understand the overall measures and implications across the energy sector.

Energy portfolio is affected by number of attributes which goes around the whole spectrum that is petroleum products and other energy products .this explains the exposes and relative instability in the system .this intern affects the portfolio returns and its growth.

UNITS

Dollars per Barrel for crude oil.

Dollars per Thousand Cubic Feet for natural gas.

US Dollars per Metric Ton for coal.

Dollars per Gallon for propane.

TIME FRAME

The time frame taken is a span of five years taking average of each month prices.

Europe Brent Spot Price FOB

(Dollars per Barrel)

YEAR

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

2008

92.18

94.99

103.64

109.07

122.8

132.32

132.72

113.24

97.23

71.58

52.45

39.95

2009

43.44

43.32

46.54

50.18

57.3

68.61

64.44

72.51

67.65

72.77

76.66

74.46

2010

76.17

73.75

78.83

84.82

75.95

74.76

75.58

77.04

77.84

82.67

85.28

91.45

2011

96.52

103.72

114.64

123.26

114.99

113.83

116.97

110.22

112.83

109.55

110.77

107.87

2012

110.69

119.33

125.45

119.75

110.34

95.16

102.62

113.36

112.86

111.71

109.06

109.49

Price of U.S. Natural Gas LNG Imports (Dollars per Thousand Cubic Feet)

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2008

8.42

9.37

9.41

9.85

11.33

12.34

12.99

10.25

9.06

9.16

8.09

8.86

2009

7.63

6.82

6.34

4.2

3.88

3.89

4.21

3.94

3.35

3.95

4.25

4.63

2010

0.78

5.74

4.87

4.25

4.24

4.2

4.8

4.93

4.6

4.78

5.09

5.41

2011

5.56

4.99

5.35

5.42

4.63

6.66

5.5

6.99

4.48

7.41

4.2

5.57

2012

4.25

4.3

2.95

2.96

4.37

3.1

5.15

4.02

3.17

3.18

7.21

5.22

COAL, SOUTH AFRICAN EXPORT PRICE

US Dollars per Metric Ton

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2008

Â

115

111

108.75

120.7

142.38

167.75

156.9

147.75

109.7

89.38

77.25

2009

76.4

69.06

58.56

62.88

58.03

60.2

61.1

64.25

61.13

64.35

66.44

73.8

2010

86.94

83.36

82.96

88.7

90.94

92.81

90.61

87.9

85.82

90.99

103.2

115.24

2011

122.62

117.74

121

124.03

120.46

119.01

116.27

118.27

115.62

110.88

105.47

104.19

2012

106.26

105.3

103.43

101.33

93.77

85.31

87.33

89.11

85.82

82.8

85.74

88.84

Mont Belvieu, TX Propane Spot Price FOB (Dollars per Gallon)

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2008

0.506

1.425

1.475

1.59

1.7

1.813

1.862

1.651

1.53

1.045

0.738

0.61

2009

0.727

0.659

0.653

0.638

0.701

0.846

0.752

0.906

0.946

1.008

1.076

1.19

2010

1.312

1.284

1.136

1.137

1.082

1.037

1.01

1.072

1.132

1.234

1.254

1.296

2011

1.348

1.379

1.397

1.454

1.521

1.52

1.528

1.56

1.472

1.458

1.395

2012

1.294

1.22

1.261

1.196

0.954

0.788

0.874

0.901

0.91

0.962

0.89

0.797

COEFFICIENT OF VARIATION (CV)

This measure is the ratio of the distribution's standard deviation to its mean. It is one way to measure risk relative to return, or in such cases, variation in price relative to mean price, measured over a define period. Tolerance bands can be established around coefficient of variation (William Steinhurst, 2006)

To calculate cofficient of variation we will require percentage change per month.corrosponding tabel is given in annexture A.

CALCULATION

Europe Brent Spot Price FOB (Dollars per Barrel)

Coefficient of variation

STANDARD DEVIATION IN SPOT

7.878703

Coal, South African export price, US Dollars per Metric Ton

Coefficient of variation

Â

STANDARD DEVIATION IN SPOT

24.62738

STANDARD DEVIATION IN % CHANGE

0.08187

MEAN(AVERAGE)

97.16661

MEAN(AVERAGE)

-0.0011

COFFICIENT OF VARIATION

0.253455

COFFFICIENT OF VARIATION

-77.1

Mont Belvieu, TX Propane Spot Price FOB (Dollars per Gallon)

Coefficient of variation

Â

STANDARD DEVIATION IN SPOT

0.329396

STANDARD DEVIATION IN % CHANGE

0.257909

MEAN(AVERAGE)

1.160667

MEAN(AVERAGE)

0.86

COFFICIENT OF VARIATION

0.283799

COFFFICIENT OF VARIATION

0.301347

INFERENCE

The coefficient of variation value of spot price of natural gas is 0.4 which is more than the other energy forms across the same time spectrum which is around 0.25, thus we can deduce that the risk related to return is substantially high then other energy forms(for given time period)

BETA

Beta is a measure of the systematic risk of a single instrument or an entire Portfolio and describes the sensitivity of an instrument or portfolio to broad market movements. A portfolio with a large beta will tend to benefit or suffer from broad market moves more strongly than the market overall, while one with a small beta will swing less violently than the broad market. (William Steinhurst, 2006)

Here to get colse corrospondence of the market fluctuation the benchmark index taken is "Commodity Fuel (energy) Index" which is defined as

Commodity Fuel (energy) Index, 2005 = 100, includes Crude oil (petroleum), Natural Gas, and Coal Price Indices

(imf)

The calculation of beta is done by the regression which is given by the division of the covariance of the two arrey in the system by the variance of the benchmark index here it is taken as Commodity Fuel (energy) Index. All the four products percentage change in prices is taken against corresponding change in fuel price index.

0.989531

INFERENCE

Beta corresponds to the systematic risk and correlation of each instrument with respect to the index. Here the value of Europe Brent Spot Price FOB that is 1.2 which relates to the index. Contrarily the U.S. Natural Gas LNG Imports gives negative value deducing that on every move in the index there will be opposite move of slightly less intensity on the natural gas prices.

RELEABILITY TEST

To test the reliability of the beta value special R â€“ SQUARE test is done which corresponds to the accuracy and efficient calculation mechanism.

R - SQUARE

R â€“ SQUARE(Europe Brent Spot Price FOB)

96.41%

R - SQUARE(U.S. Natural Gas LNG Imports)

0.46%

R - SQUARE(Coal, South African export price)

36.71%

R - SQUARE(Mont Belvieu, TX Propane Spot Price)

61.77%

This clearly shows Europe Brent Spot Price FOB mostly corresponds to the index in subject and the U.S. Natural Gas LNG does not correspond to the index at all.

EXTREME VALUE MEASURES

This measures the portfolio riskiness. In general, this type of measure is the difference in cost between a portfolio's expected cost and some estimate of its worst-case cost. This measure portfolio riskiness by the difference between its expected cost and average of the worst 10% of its cost's probability distribution. (NorthWestern Energy 2005 Electric Default Supply Resource Procurement Plan, 2005)

In this study of portfolio management the extreme value measure corrosponds to mean valure , standerd deviation , skewness , kitrous, r square and engle 1.

To calculate all the four commodities are evaluated for corrosponding given parameters.

Here the for R square and Engel 1 calculation commodities are evaluated with corresponding Commodity Fuel (energy) Index. For the calculation of R square the correlation coefficient for given data is deduced. Further the product of this coefficient with the sample size gives the value of Engle 1.

CALCULATION

mean

1.160667

standard deviation

0.329396

skewness

0.01812

kitrous

-0.82808

r square

0.586967

sample size

engle 1

34.63103

INFERENCE

Combined table

Brent

ng

coal

propane

mean

92.153

5.77

97.166

1.16

standard deviation

24.43

2.48

24.6

0.32

skewness

0.42

1.04

0.54

0.018

kitrous

-0.79

0.79

0.33

-0.828

r square

0.97

0.081

0.62

0.586

sample size

engle 1

57.59

4.79

36.97

34.63

INTERPRETATION

(taking all commodities of equal proportions)

The average of all four mean is 49.06, this corresponds to the minimum value of the portfolio that can be possible with all four commodities doing worse corresponding to the fuel index.

The average of skewness is around 0.5 which means that the mostly the return achieved by the portfolio is underperforming by fifty percent.

Kitrous is significantly low and positive for natural gas and coal but just opposite for crude and propane. Thus indicating that the index is related to the portfolio.

Engle 1 value of natural gas is significantly low indicating low hetroskedasicity. Same is significantly high for all other commodities.

VALUE AT RISK (VAR)

A traditional approach for quantifying risk of investment portfolios. VaR measures the downside risk of a portfolio. It is always calculated in the context of a risk level and a planning horizon. VaR of a proposed resource portfolio over a one year planning horizon at the 99% risk level. That VaR would tell us the amount of extra cost that would have a 1% chance of occurring over the next year. (William Steinhurst, 2006)

In this study all the commodities are evaluated for VaR analysis

CALCULATION

Europe Brent Spot Price FOB (Dollars per Barrel)

PARAMETER SPOT

PARAMETER(% CHANGE)

PORTFOLIO VALUE

100

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

21.07103

AVERAGE RETURN % CHANGE

0.069167

STANDARD DEVIATION IN SPOT

24.43969

STANDARD DEVIATION IN SPOT

0.093408

CONFIDENCE LEVEL

0.95

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-19.1287

MINIMUM RETURN WITH 95 % PROB

-0.08448

VALUE OF PORTFOLIO

-1812.87

VALUE OF PORTFOLIO

91.5524

VALUE AT RISK

1912.87

VALUE AT RISK

8.447596

Price of U.S. Natural Gas LNG Imports (Dollar/ Thousand Cubic Feet)

PARAMETER (SPOT)

PARAMETER(% CHANGE)

PORTFOLIO VALUE

100

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

1.948306

AVERAGE RETURN % CHANGE

0.328863

STANDARD DEVIATION IN SPOT

2.483838

STANDARD DEVIATION IN SPOT

0.877858

CONFIDENCE LEVEL

0.95

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-2.13724

MINIMUM RETURN WITH 95 % PROB

-1.11508

VALUE OF PORTFOLIO

-113.724

VALUE OF PORTFOLIO

-11.5084

VALUE AT RISK

213.7245

VALUE AT RISK

111.5084

Coal, South African export price, US Dollars per Metric Ton

PARAMETER (SPOT)

PARAMETER(% CHANGE)

PORTFOLIO VALUE

100

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

19.88988

AVERAGE RETURN % CHANGE

0.06009

STANDARD DEVIATION IN SPOT

24.62738

STANDARD DEVIATION IN %CHANGE

0.08187

CONFIDENCE LEVEL

0.95

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-20.6186

MINIMUM RETURN WITH 95 % PROB

-0.0746

VALUE OF PORTFOLIO

-1961.86

VALUE OF PORTFOLIO

92.5426

VALUE AT RISK

2061.856

VALUE AT RISK

7.45736

Mont Belvieu, TX Propane Spot Price FOB (Dollars per Gallon)

PARAMETER (SPOT)

Â

PARAMETER(% CHANGE)

Â

PORTFOLIO VALUE

100

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

0.277567

AVERAGE RETURN % CHANGE

0.104765

STANDARD DEVIATION IN SPOT

0.329396

STANDARD DEVIATION IN SPOT

0.257909

CONFIDENCE LEVEL

0.95

CONFIDENCE LEVEL

0.95

Â

CALCULATION

Â

CALCULATION

Â

MINIMUM RETURN WITH 95 % PROB

-0.26424

MINIMUM RETURN WITH 95 % PROB

-0.31946

VALUE OF PORTFOLIO

73.57591

VALUE OF PORTFOLIO

68.05417

VALUE AT RISK

26.42409

VALUE AT RISK

31.94583

INFERENCE

The value at risk calculated for all four commodities in separate analysis at the spot price as well as on the percentage change of the spot prices. The confidence level taken is 95% as the time spectrum is of five years.

The VaR value for the percentage change taken is more relevant if correlated with the fuel index thus in percentage change calculation natural gas is the most venerable commodity and others are also quite venerable if invested independently.

VaR analysis shows the riskiness in investing in any one of these commodities independently and forming a portfolio will be a wise option.

REVENUE AT RISK (RAR)

Because of the cost uncertainty of that resource, they have Revenue at Risk (RaR).RaR is equal to the maximum amount of extra resource cost that the manufacturer can afford to pay without severe damage to its finances. (William Steinhurst, 2006)

For our study RaR is a function of the direct impact of the market fluctuations. To measure this if the change in the percentage impact is either greater than ten percent or less than ten percent is noted and analysis is done on the only those values. Thus giving the actual risk related to the revenue and its implication.

CALCULATION

Europe Brent Spot Price FOB (Dollars per Barrel)

MONTH

Spot Price

% CHANGE

MAY,08

122.8

12.59%

AUG,08

113.24

14.68%

SEP,08

97.23

-14.14%

OCT,08

71.58

-26.38%

NOV,08

52.45

-26.73%

DEC,08

39.95

-23.83%

MAY,09

57.3

43.43%

JUN,09

68.61

19.74%

AUG,09

72.51

12.52%

MAY,10

75.95

10.46%

MAR,11

114.64

10.53%

JUN,12

95.16

13.76%

AUG,12

113.36

10.47%

Calculation

PARAMETER SPOT

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

23.25337

STANDARD DEVIATION IN SPOT

26.92094

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-21.0276

VALUE OF PORTFOLIO

-2002.76

REVENUE AT RISK

2102.764

Price of U.S. Natural Gas LNG Imports (Dollar/ Thousand Cubic Feet)

MONTH

SPOT PRICE

% CHANGE

FEB,08

9.37

11.28%

MAY,08

11.33

15.03%

AUG,08

10.25

21.09%

SEP,08

9.06

-11.61%

NOV,08

8.09

11.68%

JAN,09

7.63

13.88%

FEB,09

6.82

-10.62%

APR,09

4.2

33.75%

SEP,09

3.35

-14.97%

OCT,09

3.95

17.91%

JAN,10

0.78

-80.25%

FEB,10

5.74

635.90%

MAR,10

4.87

-15.16%

APR,10

4.25

-12.73%

JUL,10

4.8

14.29%

FEB,11

4.99

-10.25%

MAY,11

4.63

14.58%

JUN,11

6.66

43.84%

JUL,11

5.5

-17.42%

AUG,11

6.99

27.09%

SEP,11

4.48

-35.91%

OCT,11

7.41

65.40%

NOV,11

4.2

-43.32%

DEC,11

5.57

32.62%

JAN,12

4.25

-23.70%

MAR,12

2.95

31.40%

MAY,12

4.37

47.64%

JUN,12

3.1

-29.06%

JUL,12

5.15

66.13%

AUG,12

4.02

-21.94%

SEP,12

3.17

-21.14%

AUG,12

4.02

26.81%

SEP,12

3.17

-21.14%

NOV,12

7.21

126.73%

DEC,12

5.22

-27.60%

PARAMETER SPOT

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

1.772

STANDARD DEVIATION IN SPOT

2.269332

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-1.96072

VALUE OF PORTFOLIO

-96.0719

REVENUE AT RISK

196.0719

Coal, South African export price, US Dollars per Metric Ton

MONTH

SPOT

% CHANGE

MAY,08

120.7

10.99%

JUN,08

142.38

17.96%

JUL,08

167.75

17.82%

OCT,08

109.7

-25.75%

NOV,08

89.38

-18.52%

DEC,08

77.25

-13.57%

MAR,09

58.56

-15.20%

DEC,09

73.8

11.08%

JAN,10

86.94

17.80%

NOV,10

103.2

13.42%

DEC,10

115.24

11.67%

Calculation

PARAMETER SPOT

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

24.61107

STANDARD DEVIATION IN SPOT

31.89444

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-27.8506

VALUE OF PORTFOLIO

-2685.06

REVENUE AT RISK

2785.062

Mont Belvieu, TX Propane Spot Price FOB (Dollars per Gallon)

MONTH

SPOT

% CHANGE

FEB,08

1.425

181.62%

AUG,08

1.651

-11.33%

OCT,08

1.045

-31.70%

NOV,08

0.738

-29.38%

DEC,08

0.61

-17.34%

JAN,09

0.727

19.18%

JUN,09

0.846

-20.68%

JUL,09

0.752

-11.11%

AUG,09

0.906

20.48%

DEC,09

1.19

10.59%

JAN,10

1.312

10.25%

MAR,10

1.136

11.53%

MAY,12

0.954

20.23%

JUN,12

0.788

-17.40%

JUL,12

0.874

10.91%

DEC,12

0.797

10.45%

Calculation

PARAMETER SPOT

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

0.231547

STANDARD DEVIATION IN SPOT

0.288443

CONFIDENCE LEVEL

0.95

CALCULATION

MINIMUM RETURN WITH 95 % PROB

-0.2429

VALUE OF PORTFOLIO

75.71005

REVENUE AT RISK

24.28995

INFERENCE

All the commodities are taken only for real fluctuations among all propane comes out to have minimum revenue at risk than come natural gas

This corresponds that propane and natural gas both requires less purification when compared to other two and both are ready to use thus have much less exposes to market fluctuations.

To add to this the market capitalizations for these two is also less to have such an impact.

LIQUIDATION VALUE AT RISK

The total potential loss that could occur if an asset has to be liquidated. For instance, a fund might try to determine what would happen if it were forced to retire an unproductive assert. (William Steinhurst, 2006)

For liquadation the confidance level taken has to be ten percent or less than ten percent this is done to make sure the least confidence on the assert and the liquidation value at risk.

CALCULATION

Europe Brent Spot Price FOB (Dollars per Barrel)

LIQUDATION AT RISK

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

21.07103

STANDARD DEVIATION IN SPOT

24.43969

CONFIDENCE LEVEL

0.10

CALCULATION

MINIMUM RETURN WITH 100 % PROB

52.39176

VALUE OF PORTFOLIO

5339.18

VALUE AT RISK

-5239.18

Price of U.S. Natural Gas LNG Imports (Dollar/ Thousand Cubic Feet)

LIQUDATION AT RISK

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

1.948306

STANDARD DEVIATION IN SPOT

2.483838

CONFIDENCE LEVEL

0.10

CALCULATION

MINIMUM RETURN WITH 100 % PROB

5.131472

VALUE OF PORTFOLIO

613.15

VALUE AT RISK

-513.15

Coal, South African export price, US Dollars per Metric Ton

LIQUDATION AT RISK

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

21.01588

STANDARD DEVIATION IN SPOT

27.45148

CONFIDENCE LEVEL

0.10

CALCULATION

MINIMUM RETURN WITH 100 % PROB

56.19638

VALUE OF PORTFOLIO

5719.64

VALUE AT RISK

-5619.64

Mont Belvieu, TX Propane Spot Price FOB (Dollars per Gallon)

LIQUDATION VALUE AT RISK AT RISK

PORTFOLIO VALUE

100

AVERAGE RETURN SPOT

0.277567

STANDARD DEVIATION IN SPOT

0.329396

CONFIDENCE LEVEL

0.10

CALCULATION

MINIMUM RETURN WITH 100 % PROB

0.699704

VALUE OF PORTFOLIO

169.97

VALUE AT RISK

-69.97

INFERENCE

All the commodities are taken only for real liquidations, among all propane comes out to have minimum liquidation value at risk than come natural gas.

This corresponds that propane and natural gas both requires less purification when compared to other two and both are ready to use thus have much less exposes to market liquidation.

To add to this the market capitalizations for these two is also less to have such an impact.

COSTS AT RISK

Cost-at-Risk (CaR) is a supplementary measure used in the management of the interest-rate risk on the domestic central-government debt. CaR quantifies the risk on the debt and gives important input to the weighing of interest-rate risk against costs. A distinction is made between absolute and relative CaR. Absolute CaR for a given year indicates the maximum costs with a probability of 95 per cent. Relative CaR is the difference between absolute CaR and the average interest costs. Relative CaR thus indicates the maximum increase in the costs for a given year, with a probability of 95 per cent. (Cost-at-Risk for the Domestic Debt, 2000)

In our study the cost at risk is taken with respect to the Average majority prime rate charged by banks (US banks) .All the fluctuations and impacts are calculated annually in order to get maximum exposer to the intrest rates.The cost of precuring and trading a commdity is impacted by the intrest rates largly.

CALCULATION

Europe Brent Spot Price FOB (Dollars per Barrel)

calculation

spot price

MAX 08

132.72

PROBABILITY

0.95

ABSOLUTE COST AT RISK

126.084

MEAN 08

96.8475

RELATIVE COST AT RISK

29.2365

2.413

47.14917

0.44015

0.225383

INFERENCE

For all the years individually natural gas and propane lave very less cost at risk when compared to other two commodities.

This again proves that the more commodity is exposed in terms of market capitalization more is the chance of running into risk ,realization in tough and immunity is weak.

All those commodities namely here Brent and coal have higher cost at risk with respect to interest rate index.

Volatility

Measure of variation of the value of portfolio within a given time spectrum. This also shows the impact of random market fluctuation on the valuation. Due to impact on one assert the corresponding change in other asset and relative change in valuation.

Volatility impact is calculated with respect to CBOE Volatility Index which is benchmark in volatility index. This will give relative exposure corresponds to each date.

To calculate the implied volatility standard deviation of the stress impact of given index as well as the spot prices are to be deduced. The correlation of the two values is deduced in order to get standard deviation.

CALCULATION

INFERENCE

Coal and Brent have higher volatility with respect to the volatility index in consideration.

The volatility in the market is impacting the commodities having more exposure to the market and are more venerable in nature.

Valuation

Based on black soles theory we can find the valuation of the portfolio .there are two main attributes of this theory these are preference free and risk neutral nature of portfolio .from this theory for our study we are adopting only risk neutral nature of portfolio. In this theory of valuation is based on the fact that assert price is lognormally proportional to the market as well as time spectrum

Taking binomial calculation for the current value of portfolio while taking the risk neutral nature of black soles model. For the calculation time frame taken is 5 years, value of portfolio ( assert) is taken 100 units, average riskless interest rate taken in the study is 3 %,market implication affecting the given portfolio is 10 % i.e. implied volatility

Thus if we will form binomial portfolio tree with 10% volatility, we will get

100

90.48 110.52

81.87 100 122.14

74.08 90.48 110.52 134.99

67.03 81.87 100 122.14 149.18 60.65 74.08 90.48 110.52 134.99 164.87

Our Service Portfolio

Want To Place An Order Quickly?

Then shoot us a message on Whatsapp, WeChat or Gmail. We are available 24/7 to assist you.

Do not panic, you are at the right place

Visit Our essay writting help page to get all the details and guidence on availing our assiatance service.

Get 20% Discount, Now
£19 £14/ Per Page
14 days delivery time

Our writting assistance service is undoubtedly one of the most affordable writting assistance services and we have highly qualified professionls to help you with your work. So what are you waiting for, click below to order now.

Get An Instant Quote

ORDER TODAY!

Our experts are ready to assist you, call us to get a free quote or order now to get succeed in your academics writing.

Get a Free Quote Order Now