I'd have to make a complaint..... to my program at University of London. I've been working with assigned material from Benninga and Hull in their Financial Engineering program. Hull has some great theoretical material but I've been frustrated with the Excel spreadsheets with Benninga and came across your material as I was looking to move everything to Python. Felt like I was stuck in the late 90s with this material. All I can say is fantastic work, I've been impressed with every video and coding along the way. I think its fair to say that the academy is dying as they can't compete with talented people like yourself.
Hello, could you please advise why you have in your CRR code this row - S[0] = S0*d**N? I have seen more your videos regarding to binomial trees but just only for the CRR model you have it. It is necessary to write it for CRR model as I want to apply this model to value the implied volatility of an american put option? thank you in advance
hey, can you make a video on how to use the volatility surface, once it has been obtained and on its derivation in python both once from BS model and once from binomial tree method? I was also looking for videos with constructing the option implied risk neutral distribution - it will be great if you could delve into the the topic. Can you explain as well the breeden-litzenberger formula? merci
Hi Elizabeta, just to summarise what you would like to see: 1. Deriving the vol surface with BS model and binomial tree method, 2. Creating/Visualising the “market observed” risk neutral distributions by using the breeden-litzenberger formula, and 3. A real example of using the implied vol surface, perhaps using local volatility tree to price some kind of derivative. Hopefully I can get to these soon.
@@QuantPy Thank you! Maybe i have one additional question. How do we get u and d in reality? Are they really derived using exp of time-scaled historical volatility or are there other methods to determine u and d?
Cheers, I have only mentioned the four most common methods to assign values to the up and down factors for a binomial tree model. However, I’m sure there are plenty of other ways to define these values, at the end of the day, all you’re doing is definitely the time scaled drift and variance! I’m sure there are plenty of smart ways of assigning these values. These are the ones that are most well defined in literature.
I'd have to make a complaint.....
to my program at University of London. I've been working with assigned material from Benninga and Hull in their Financial Engineering program. Hull has some great theoretical material but I've been frustrated with the Excel spreadsheets with Benninga and came across your material as I was looking to move everything to Python. Felt like I was stuck in the late 90s with this material. All I can say is fantastic work, I've been impressed with every video and coding along the way. I think its fair to say that the academy is dying as they can't compete with talented people like yourself.
Hello, could you please advise why you have in your CRR code this row - S[0] = S0*d**N? I have seen more your videos regarding to binomial trees but just only for the CRR model you have it. It is necessary to write it for CRR model as I want to apply this model to value the implied volatility of an american put option? thank you in advance
hey, can you make a video on how to use the volatility surface, once it has been obtained and on its derivation in python both once from BS model and once from binomial tree method? I was also looking for videos with constructing the option implied risk neutral distribution - it will be great if you could delve into the the topic. Can you explain as well the breeden-litzenberger formula? merci
Hi Elizabeta, just to summarise what you would like to see:
1. Deriving the vol surface with BS model and binomial tree method,
2. Creating/Visualising the “market observed” risk neutral distributions by using the breeden-litzenberger formula, and
3. A real example of using the implied vol surface, perhaps using local volatility tree to price some kind of derivative.
Hopefully I can get to these soon.
@@QuantPy Thank you! Maybe i have one additional question. How do we get u and d in reality? Are they really derived using exp of time-scaled historical volatility or are there other methods to determine u and d?
Cheers, I have only mentioned the four most common methods to assign values to the up and down factors for a binomial tree model.
However, I’m sure there are plenty of other ways to define these values, at the end of the day, all you’re doing is definitely the time scaled drift and variance!
I’m sure there are plenty of smart ways of assigning these values. These are the ones that are most well defined in literature.
@@QuantPy Cheers, I didn't know that there are videos on your channel, with methods of assigning the up and down factors. Very well!
Haha I can’t tell if you’re joking or not, the video we are commenting on is exactly that :) good luck