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Monte Carlo Simulation
- Thread starter WAWTU31
- Start date
Monte Carlo simulation as a tool sounds more elaborate than what it really is. That is not to say that you cannot use it for elaborate analysis.
You need to determine what you need to figure out. For instance, what is the probability of a coin flipping head or tails. Create a process that acts like a coin: for instance a binary random variable in an excel sheet, that returns 0 or 1, with a 50% chance each. Generate the variable 10000 times and count how many times you get 1 and 0. You result will tend to 50%.
You can price an option using Monte Carlo simulation. This time, however, generate a Brownian motion price path with a given volatility. Run it many times and see what your option is worth on expiry and discount. The outcome should quickly converge to the Black and Scholes value.
You need to determine what you need to figure out. For instance, what is the probability of a coin flipping head or tails. Create a process that acts like a coin: for instance a binary random variable in an excel sheet, that returns 0 or 1, with a 50% chance each. Generate the variable 10000 times and count how many times you get 1 and 0. You result will tend to 50%.
You can price an option using Monte Carlo simulation. This time, however, generate a Brownian motion price path with a given volatility. Run it many times and see what your option is worth on expiry and discount. The outcome should quickly converge to the Black and Scholes value.
Amazon dot com sells a book called "Monte Carlo methods in finance" and another one called "Monte Carlo simulation in finance." Maybe they might be of some benefit.
In practical terms how you would apply the Monto Carlo simulation to the price path of index futures or an index such as the Dow? Cheers.Quote from sabotage:
Monte Carlo simulation as a tool sounds more elaborate than what it really is. That is not to say that you cannot use it for elaborate analysis.
You need to determine what you need to figure out. For instance, what is the probability of a coin flipping head or tails. Create a process that acts like a coin: for instance a binary random variable in an excel sheet, that returns 0 or 1, with a 50% chance each. Generate the variable 10000 times and count how many times you get 1 and 0. You result will tend to 50%.
You can price an option using Monte Carlo simulation. This time, however, generate a Brownian motion price path with a given volatility. Run it many times and see what your option is worth on expiry and discount. The outcome should quickly converge to the Black and Scholes value.
Pssst...don't tell anybody else, but there is an explicit formula for all the options you'll be able to trade as a retail trader....
Quote from sabotage:
Monte Carlo simulation as a tool sounds more elaborate than what it really is. That is not to say that you cannot use it for elaborate analysis.
You need to determine what you need to figure out. For instance, what is the probability of a coin flipping head or tails. Create a process that acts like a coin: for instance a binary random variable in an excel sheet, that returns 0 or 1, with a 50% chance each. Generate the variable 10000 times and count how many times you get 1 and 0. You result will tend to 50%.
You can price an option using Monte Carlo simulation. This time, however, generate a Brownian motion price path with a given volatility. Run it many times and see what your option is worth on expiry and discount. The outcome should quickly converge to the Black and Scholes value.
Thank you for your response...is there a website where I can download info into Excel or code writing for dummies?
I have also a question.
Lot of people talk of backtesting in conjunction with MC simulations. I understand the value of Monte Carlo in option pricing, I understand (somewhat) the value in backtesting, but this is what puzzles me:
When you have trading method that exploits non-randomness when simulating do you:
a) Generate price path based on some neutral distribution, e.g. lognormal
b) You include the non-randomness in the distribution
If a how can you have positive results?
If b isnât that a sell-fulfilling prophecy?
Many thanks.
Lot of people talk of backtesting in conjunction with MC simulations. I understand the value of Monte Carlo in option pricing, I understand (somewhat) the value in backtesting, but this is what puzzles me:
When you have trading method that exploits non-randomness when simulating do you:
a) Generate price path based on some neutral distribution, e.g. lognormal
b) You include the non-randomness in the distribution
If a how can you have positive results?
If b isnât that a sell-fulfilling prophecy?
Many thanks.