risk bucketing for p / l ken and greg ,
what we have been doing is absoutely fine under the assumption that the market
conditions move relatively small ( where taylor series has fast convergence ) .
however , we could run into troubles when the market has a big move .
in order to have a error proof bucketing , we can use the following
method ( finite - difference ) , let me
know what you guys think how to implement it to the transport book .
sensitivity to risk parameters , or p / l attribution by risk bucket :
today ' s premium = premium based on today ' s curves
last day ' s premium = premium based on last day ' s curves
change due to
deliverycurveshift = [ premium based on today ' s delivery price and last day ' s
receipt price , volatilities , interest rate , last ' s time to expiration etc ] -
last day ' s premium - today ' s change due to gammal
receiptcurveshift = [ premium based on today ' s receipt price and last day ' s
everything else ] - last day ' s premium - today ' s change due to gamma 2
vegal = [ premium based on today ' s delivery volatility and last day ' s
everything else ] - last day ' s premium
vega 2 = as above for gas volatility
rho = as above for interest rate
eta = as above for correlation
theta = { [ premium based on today ' s days to expiration and last day ' s
everything else ] - drift - last day ' s premium } / 365 . 25
[ this is a daily theta . the sprdopt function returns an annualised theta . ]
gammal = 0 . 5 last day ' s gammal ' * priceshiftl 2 ? ? gamma 2 = 0 . 5 last day ' s gamma 2 ' * priceshift 2 2
drift = [ ( exp ( last day ' s interest rate * ( today - last days ) / 365 . 25 ) ) - 1 ] *
last day ' s premium
priceshiftl = today ' s delivery price - last day ' s delivery price
priceshift 2 = today ' s receipt price - last day ' s receipt price
gammal ' = theoretical gammal , i . e . gamma from spread option
gamma 2 ' = theoretical gamma 2 , i . e . gamma from spread option calculation
liquidation = premium of option which expired the day before , i . e . intrinsic
value .
risk bucketing for p / l
ken and greg ,
what we have been doing is absoutely fine under the assumption that the market
conditions move relatively small ( where taylor series has fast convergence ) .
however , we could run into troubles when the market has a big move .
in order to have a error proof bucketing , we can use the following
method ( finite - difference ) , let me
know what you guys think how to implement it to the transport book .
sensitivity to risk parameters , or p / l attribution by risk bucket :
today ' s premium = premium based on today ' s curves
last day ' s premium = premium based on last day ' s curves
change due to
deliverycurveshift = [ premium based on today ' s delivery price and last day ' s
receipt price , volatilities , interest rate , last ' s time to expiration etc ] -
last day ' s premium - today ' s change due to gammal
receiptcurveshift = [ premium based on today ' s receipt price and last day ' s
everything else ] - last day ' s premium - today ' s change due to gamma 2
vegal = [ premium based on today ' s delivery volatility and last day ' s
everything else ] - last day ' s premium
vega 2 = as above for gas volatility
rho = as above for interest rate
eta = as above for correlation
theta = { [ premium based on today ' s days to expiration and last day ' s
everything else ] - drift - last day ' s premium } / 365 . 25
[ this is a daily theta . the sprdopt function returns an annualised theta . ]
gammal = 0 . 5 last day ' s gammal ' * priceshiftl 2 ? ? gamma 2 = 0 . 5 last day ' s gamma 2 ' * priceshift 2 2
drift = [ ( exp ( last day ' s interest rate * ( today - last days ) / 365 . 25 ) ) - 1 ] *
last day ' s premium
priceshiftl = today ' s delivery price - last day ' s delivery price
priceshift 2 = today ' s receipt price - last day ' s receipt price
gammal ' = theoretical gammal , i . e . gamma from spread option
gamma 2 ' = theoretical gamma 2 , i . e . gamma from spread option calculation
liquidation = premium of option which expired the day before , i . e . intrinsic
value .