Comparison of theoretical & market prices

Description
This is a Presentation explaining topics like objectives,theoretical models and price,Helmer and Longstaff models.

• Application of theoretical models to predict future prices for Indian Market Underlying Considered Equity Index 1. Nifty Equities 1. ICICI Bank 2. HUL Commodities 1. Gold 2. Crude Oil

For Nifty Model Used ?F0= S0*e^(rT) ?F0= S0*e^(r-q)T (for dividend yield q) Hemler and Longstaff Model ?Log(Ft/St)=a + b*r + c*(sigma)+error

Stocks Model used ?F0= S0*e^(rT) ?F0=(S0 –d)*e^(rT) (for absolute dividend ‘d’) Hemler and Longstaff Model ?Log(Ft/St)=a+ b* r+ c*(sigma)+error

Commodity ?F0= S0*e^(rT) ?F0= S0*e^(r+u-y)T (cost of carry model) -U- Storage costs -Y- convenience for storing

Equities ?3 month contract ?Time series data - Rolling contract using near month contracts ?Time period considered - 1st Nov 2007 to 30 October 2009

Results for time series from Nov-2007 to Oct 2009

Using formula S0*e^(rT) Security name Hindustan Unilever ICICI Bank MAPE Standard Deviation

Using dividend (S0 – d)*e^(rT) MAPE Standard deviation

0.585659

0.683131

0.584418 0.394727

0.681685 0.526219

0.3955753 0.527161

NIFTY

0.3276939 0.3592638 0.3274482 0.3594415

Results for a single contract of 3 months from March to June 2009 Using formula S0*e^(rT) Security name Hindustan Unilever ICICI Bank NIFTY MAPE Standard Deviation Using dividend (S0 – d)*e^(rT) MAPE Standard deviation

1.000141 0.61979 0.999366 0.618436 0.82589 0.512115 0.824014 0.510252 0.153422 0.147486 0.153402 0.147507

?1 year contract ending 04oct 2008. ?Period – 16 Oct 2007 to 04 Oct 2008

Results: MAPE 2.384% SD 1.355%

?For Gold storage cost is negligible ?Convenience yield is to be considered ?So formula becomes S0*e^(r-y)*T
– Where Y is the convenience yield

Y (in%) 1 2 3 4 5 6 Y (in%) 4.25 4.5 4.75

MAPE 1.912 1.453 1.074 0.895 0.912 1.123 MAPE 0.878 0.874 0.886

Std dev(%) 1.165 1.030 0.912 0.8 0.832 0.980 Std dev(%) 0.796 0.801 0.813

?3 month contract expiring on 15 Nov 2007 Results: MAPE 2.71% SD 1.744%

?For Crude Oil, storage cost (u) is a significant factor ?Convenience yield (y) is also significant
– y>u

?So formula becomes S0*e^(r+u-y)*T
– Where Y is the convenience yield

?By Trial and error, we find the price difference is minimum when value of ‘u-y’ is -20%

?The Helmer and Longstaff model is given as Log(Ft/St)=a+ b* r+ c*(?)+error Where:1. Ft and St are the dividend adjusted future price and Spot price of the underlying at time t 2. R is the the interest rate to expiry T 3. ? is the volatility of stock return measured by the 3 day rolling standard deviation

?The adjusted cost of carry model is nested in this model ?This model also accounts for the stock price volatility along with the interest rate ?Model tried out for the period from November 2007 to October 2009 using one month contract periods

For ICICI (Significance of model 0.693)
Model Summary

Model 1

R .044a

R Square .002

Adjusted R Square -.002

Std. Error of the Estimate .2576071

a. Predictors: (Constant), std_dev, t_bill Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta 1 (Constant) .048 .042 t_bill -.284 6.616 -.002 std_dev -.784 .829 -.044 a. Dependent Variable: log_ft_st

t 1.145 -.043 -.947

Sig. .253 .966 .344

Nifty (Significance of model 0.561)
Model Summary

Model 1

R .049a

R Square .002

Adjusted R Square -.002

Std. Error of the Estimate .004171

a. Predictors: (Constant), std_dev, t_bill Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta 1 (Constant) 9.742E-5 .000 t_bill 0.016 .106 .007 std_dev -0.014 .013 -.044 a. Dependent Variable: log_ft_st

t .237 .154 -1.070

Sig. .813 .877 .285

?The difference between the theoretical and settlement prices reduce overall when dividend is adjusted in the model ?Storage costs (U) & Convenience to carry (Y) has a significant effect on commodity pricing ?Helmer and Longstaff model is not proved to be valid to estimate the theoretical prices in the Indian Market as the model is not significant with the given data



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