ALBITS- Energy Pricing & Risk Management Software Distinguishing features Pricing model
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ALBITS- Energy Pricing & Risk Management Software • Distinguishing features • Pricing model • Statistical Significance • Derivative pricing • Forward Curve • Risk gauging parameters of VaR • Options Greek • Hedge Ratio Optimization
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ALBITS- Energy Pricing & Risk Management Software Distinguishing features Pricing model Statistical Significance Derivative pricing Forward Curve Risk gauging parameters of VaR Options Greek Hedge Ratio Optimization Energy Book Manager. Key differentiators. A L B I T S. - PowerPoint PPT Presentation
Transcript of ALBITS- Energy Pricing & Risk Management Software Distinguishing features Pricing model
ALBITS- Energy Pricing & Risk Management Software
• Distinguishing features
• Pricing model
• Statistical Significance
• Derivative pricing
• Forward Curve
• Risk gauging parameters of VaR
• Options Greek
• Hedge Ratio Optimization
• Energy Book Manager
ALB
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Key differentiators ETRM Process Capture: Pre deal Analysis; Deal capture; Scheduling
Operations; Invoicing; Settlement; Risk Computations; Reports.
Energy Trading: The Pricing model identifies market imperfections and the trading opportunities. In case of deep out of money options & swing options, such opportunities become striking.
Energy Risk Metrics: The system computes risk metrics based on sampling distribution of underlying energy assets. i.e. WTI crude does not follow Normal distribution, hence parametric VaR is inappropriate risk measure.
Data Visuals: Histogram, Scatter plot, Function plots, Orthonormal plots.
Data Analysis: Distribution sampling, Distribution fitting; Normality test; Discretization; Correlation matrices; Multicollineraity test; Factor analysis; Discriminate analysis.
Data Modeling & Tests: Stochastic Models, Regression; ANOVA; CCR; Fourier transforms. Parametric & non parametric test.
Wide choice of technology: Programming languages, Database management systems, Web Integrations, BI Tools.
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Stochastic Pricing for WTI
Pricing Model in .NET
Stochastic Pricing for WTI ALB
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Pricing Model in
MATLAB
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Index Cushing, OK WTI Spot Price FOB
Model Predicted Price
Mean 56.216 56.233Variance 739.018 740.379
Observations 2909 2909Hypothesized Mean Difference 0
df 5816 t Stat -0.023438
P(T<=t) one-tail 0.490651 t Critical one-tail 1.645116 P(T<=t) two-tail 0.981302 t Critical two-tail 1.960372
At 5% level of significance, p- value is >α(.05)Conclusion: Ho is true
t-Test: Two-Sample Assuming Unequal VariancesHo: Model predicted price= Cushing Spot Price
Statistical Significance
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Distribution Fitting
1 2 3 4 5 6 7 8 9 100
100
200
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Observed and theoretical frequencies
Observations DistributionClass
Fre
qu
en
cy
Chi-square (Observed value) 405.182Chi-square (Critical value) 14.067
DF 7p-value < 0.0001alpha 0.05
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Forward Curve for WTI Crude oil
Nov 0
2,
2011
Nov 0
8,
2011
Nov 1
4,
2011
Nov 2
0,
2011
Nov 2
6,
2011
Dec 0
2,
2011
Dec 0
8,
2011
Dec 1
4,
2011
Dec 2
0,
2011
Dec 2
6,
2011
Jan
01,
2012
Jan
07,
2012
Jan
13,
2012
Jan
19,
2012
Jan
25,
2012
Jan
31,
2012
Feb
06,
2012
Feb
12,
2012
Feb
18,
2012
Feb
24,
2012
Mar
01,
2012
Mar
07,
2012
Mar
13,
2012
Mar
19,
2012
Mar
25,
2012
Mar
31,
2012
Ap
r 06,
2012
Ap
r 12,
2012
Ap
r 18,
2012
0
10
20
30
40
50
60
70
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100
WTI Price($/bbl)
WTI Price($/bbl)
Model generated Forward curve based on simulated price path for WTI till April 2012.
Derivative Pricing
Current Price S 90.000
Time to expiration t (year) 0.7500
Strike price K 80
Risk Free Rate r 0.8000 %
Volatility σ 30.000 %
Call Option price:$14.97
Plain Vanilla OptionsALB
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Current RBOB Price S1 119.86
Current WTI price S2 86.65
Strike Price K 30.00
Time to expiration t (year) 0.500
Volatility RBOB σr 0.0263
Volatility WTI σw 0.0265
Risk Free Rate r 0.524%
Correlation ρ 0.00524
Option price: $ 4.8078
Gasoline-WTI Crude Crack Call option
Derivative PricingALB
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VaR- Value at Risk• Monte Carlo simulations
• Variance-Covariance model
• Conditional VaR/Tail VaR
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Options Greek
Greek measures the sensitivity of the value of a portfolio to a small change in a given underlying parameter
*Calculated for Plain Vanilla example
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Hedge Ratio Optimization
0 0.2 0.4 0.6 0.8 1 1.20.00000000
0.00050000
0.00100000
0.00150000
0.00200000
0.00250000
Variance in Hedged Portfolio(Million $)
Variance in Hedged Portfolio
Optimal Hedge Ratio
dzSS
dtSSt
SS
d ½ 222
2
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Energy Book ManagerManages Versatile Energy portfolio
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Albedo Energy Consulting Undertake Projects for: ETRM system designing; Software development; Coding; Testing, Implementation & System integration.
• ETRM Business process capture
• Energy price Modeling
• Forward curve simulations
• Energy Risk models
• Energy Portfolio optimization models
• Energy complex derivatives Valuations & Structuring
For discussion contact: [email protected]
Programming
• Dot Net• Java• C++
Database
• Oracle• SQL Server• Sybase
BI Tools
• Business Objects• Crystal reports
