That's not a model, but rather just a specific case of applying Black-Scholes for the ATM case.
Do you price the options yourself or do you use black-scholes?
- Thread starter thenmmm
- Start date
Q
http://en.wikipedia.org/wiki/Black–Scholes
BlackâScholes in practice
The BlackâScholes model disagrees with reality in a number of ways, some significant. It is widely employed as a useful approximation, but proper application requires understanding its limitations â blindly following the model exposes the user to unexpected risk.
Among the most significant limitations are:
* the underestimation of extreme moves, yielding tail risk, which can be hedged with out-of-the-money options;
* the assumption of instant, cost-less trading, yielding liquidity risk, which is difficult to hedge;
* the assumption of a stationary process, yielding volatility risk, which can be hedged with volatility hedging;
* the assumption of continuous time and continuous trading, yielding gap risk, which can be hedged with Gamma hedging.
In short, while in the BlackâScholes model one can perfectly hedge options by simply Delta hedging, in practice there are many other sources of risk.
UQ
http://en.wikipedia.org/wiki/Black–Scholes
BlackâScholes in practice
The BlackâScholes model disagrees with reality in a number of ways, some significant. It is widely employed as a useful approximation, but proper application requires understanding its limitations â blindly following the model exposes the user to unexpected risk.
Among the most significant limitations are:
* the underestimation of extreme moves, yielding tail risk, which can be hedged with out-of-the-money options;
* the assumption of instant, cost-less trading, yielding liquidity risk, which is difficult to hedge;
* the assumption of a stationary process, yielding volatility risk, which can be hedged with volatility hedging;
* the assumption of continuous time and continuous trading, yielding gap risk, which can be hedged with Gamma hedging.
In short, while in the BlackâScholes model one can perfectly hedge options by simply Delta hedging, in practice there are many other sources of risk.
UQ
"SuperDerivatives - The global derivatives benchmark pricing systems
The global derivatives benchmark. Multi-asset pricing systems, sales, operational risk and revaluation solutions for the buy- and sell-side."
http://www.superderivatives.com/
"NEW YORK -- SuperDerivatives(R), the benchmark for option pricing, risk management and independent revaluations, has unveiled its state-of-the-art commodity option pricing platform, SD-CM(TM). The new product complements SuperDerivatives' range of option pricing platforms, which already cover the foreign exchange, fixed income and equity asset classes. The systems are suitable for buy and sell side institutions. "
http://www.allbusiness.com/banking-finance/financial-markets-investing-securities/5105022-1.html
The global derivatives benchmark. Multi-asset pricing systems, sales, operational risk and revaluation solutions for the buy- and sell-side."
http://www.superderivatives.com/
"NEW YORK -- SuperDerivatives(R), the benchmark for option pricing, risk management and independent revaluations, has unveiled its state-of-the-art commodity option pricing platform, SD-CM(TM). The new product complements SuperDerivatives' range of option pricing platforms, which already cover the foreign exchange, fixed income and equity asset classes. The systems are suitable for buy and sell side institutions. "
http://www.allbusiness.com/banking-finance/financial-markets-investing-securities/5105022-1.html
Yeah, SuperD's can price a whole variety of options, but some of their pricing is suspect. At any rate, I am not sure I understand what SuperD's have to do with the issue?
"
http://www.nesug.org/proceedings/nesug98/post/p020.pdf
REVIEW AND RESULTS:
How good is this new technique â Option
Pricing with a Time Series? Letâs apply it to the
AMR data series. First we follow the 4 steps
just discussed to produce a forecast (recall that
these steps are very applicable to many
compound growth problems).
1) Plot the data â this gives an indication
about linearity, variance, and appropriate
âweightingsâ.
2) Screen for outliers â all points in this series
were deemed to have predictive value (no
outliers).
3) Transform the series using a log, and then a
first difference â creating a new (stationary)
series that measures daily % change in
stock price.
4) Check for autocorrelation â as expected,
this series has âmoving averageâ behavior,
and no âautoregressiveâ behavior. We can
now use an exponential smoothing model.
The actual parameters chosen for this
problem (refer to section III for details)
were NSTART=100, WEIGHT=0.005, and
TREND=constant.
The model produces a forecast for October 15,
1998 (the date the 6 month option expires) of
$167.77, with a standard deviation of $12.48.
Applying steps two and three from the binomial
model described in section II, we can calculate
an option value of $18.15! This is a bit higher
than the âmarketâ price of $16.00. Why? For
this forecast, I used the most recent three years
of data. Since the stock has increased quite a bit
in the past three years, this is reflected in the
forecast.
"
http://www.nesug.org/proceedings/nesug98/post/p020.pdf
REVIEW AND RESULTS:
How good is this new technique â Option
Pricing with a Time Series? Letâs apply it to the
AMR data series. First we follow the 4 steps
just discussed to produce a forecast (recall that
these steps are very applicable to many
compound growth problems).
1) Plot the data â this gives an indication
about linearity, variance, and appropriate
âweightingsâ.
2) Screen for outliers â all points in this series
were deemed to have predictive value (no
outliers).
3) Transform the series using a log, and then a
first difference â creating a new (stationary)
series that measures daily % change in
stock price.
4) Check for autocorrelation â as expected,
this series has âmoving averageâ behavior,
and no âautoregressiveâ behavior. We can
now use an exponential smoothing model.
The actual parameters chosen for this
problem (refer to section III for details)
were NSTART=100, WEIGHT=0.005, and
TREND=constant.
The model produces a forecast for October 15,
1998 (the date the 6 month option expires) of
$167.77, with a standard deviation of $12.48.
Applying steps two and three from the binomial
model described in section II, we can calculate
an option value of $18.15! This is a bit higher
than the âmarketâ price of $16.00. Why? For
this forecast, I used the most recent three years
of data. Since the stock has increased quite a bit
in the past three years, this is reflected in the
forecast.
"
Isn't this just using historical volatility for option pricing and, as such, is neither particularly original nor particularly interesting?
Don't over-think it! Use B-S (or other) to model what will happen to your position if certain things happen (e.g. time passes, stock price changes, interest rate changes, dividends, volatility changes due to whatever factors, etc.). Making profits comes not from better modeling the price option from given inputs, but from correctly estimating those inputs, and the potential risk and profit the result. ... And no, the market isn't random (e.g. coin toss, etc.). And no, options trading isn't a zero-sum game because of the trading costs. ...
