How to Estimate Prices for OTC Exotic Options Without a Dealer Quote

# Requesting for price?


# Requesting for price?












Just for education purpose. Assuming I have some trading ideas that involves the use of OTC derivatives but I may not be able to put them into practice due to regulatory issues and huge minimum capital requirements.

Can I get the pricing(i.e the max return and max risk payoff) of a custom made derivative(might include an Asian, touch/no touch options, lookback et cetera) from a bank or an issuer even though trading those products are only available to H.N.W.I and professionals traders?

Thank you in advance for any help provided.

## Answer by user42108 (score 6, accepted)

https://quant.stackexchange.com/a/68073

As @noob2 noted, nobody is going to quote you a price unless you're a customer. And when I say "customer", I mean "customer of the desk", not just of the bank. Would require an ISDA covering the specific product area + some commitment to minimum 'spend' with that business line.

## Answer by AKdemy (score 2)

https://quant.stackexchange.com/a/68076

If you need this for educational purposes only, you can basically get all you need from reading papers. For example, Asian options are not always cheaper than their plain vanilla counterparts.

Risk or return details may be something for retail investors due to regulation like priips but no desk will usually provide this when you request a quote (RFQ).

If you would like to do it all by yourself, none of your products are particularly exotic. If you want to get an idea of pricing for educational purposes, you can start by looking at websites like investing.com where you can get implied vol quotes like shown in this question.

Once you have IVOL, you can get reliable prices for many products fairly easily with existing online tools. E.g. this question discusses touch option pricing in quantlib. Even more complex double no touch options have static replications as Uwe Wystup points out in his mathfinance newsletter.

The CME group offers listed Average price options. That is another terminology for Asian options. On top of that, it is relatively simply to price Asian options.

```
Turnbull, S. M., and L. M. Wakeman (1991): “A Quick Algorithm for
Pricing European Average Options,” Journal of Financial and Quantitative
Analysis, 26, 377–389
```

is one such solution. Another one is Krekel 2003. There is also

```
M. Curran, Valuing Asian and Portfolio Options by Conditioning on the Geometric Mean Price,
Management Science 40 (1994), 1705.
```

Matlab has an implementation for the Turnball Wakeman method. Quantlib is discussed here for example. I suppose other platforms / languages will have similar pricers as well.

You can also get a fairly reliable result with simple Monte Carlo simulations. For example, you can model commodity options using the following dynamic: $$\frac{dS(t)}{S(t)} =\bigg(r \ - \ y - \frac{\sigma^2}{2}\bigg)dt + \sigma d\hat{W_{t}}$$ Integrating this equation between $t=0$ and the end $t=1$ provides the generic equation used in many Monte Carlo simulations: $$ S(t_{1}) = S(0) * exp \bigg\{ \bigg(r \ - \ y - \frac{\sigma^2}{2} \bigg) \ * \ t_{1} \bigg\} $$ Where y denotes convience yield which is unobservable. However the formula for a forward price can be used to retrieve y. This simulation generates the fixings in between the start of the fixing period and the end of the fixing period. It is simple to compute the average fixing: $$ a_{1} = \frac{S(t_{1})+ \ ... \ + S(t_{n})}{n} $$ where S is the fixing at each day of the fixing period (for each iteration) and n is the number of fixing periods. Methods for Lookback options are discussed here. For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers. A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).

$$\frac{dS(t)}{S(t)} =\bigg(r \ - \ y - \frac{\sigma^2}{2}\bigg)dt + \sigma d\hat{W_{t}}$$ Integrating this equation between $t=0$ and the end $t=1$ provides the generic equation used in many Monte Carlo simulations: $$ S(t_{1}) = S(0) * exp \bigg\{ \bigg(r \ - \ y - \frac{\sigma^2}{2} \bigg) \ * \ t_{1} \bigg\} $$ Where y denotes convience yield which is unobservable. However the formula for a forward price can be used to retrieve y. This simulation generates the fixings in between the start of the fixing period and the end of the fixing period. It is simple to compute the average fixing: $$ a_{1} = \frac{S(t_{1})+ \ ... \ + S(t_{n})}{n} $$ where S is the fixing at each day of the fixing period (for each iteration) and n is the number of fixing periods. Methods for Lookback options are discussed here. For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers. A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).

Integrating this equation between $t=0$ and the end $t=1$ provides the generic equation used in many Monte Carlo simulations: $$ S(t_{1}) = S(0) * exp \bigg\{ \bigg(r \ - \ y - \frac{\sigma^2}{2} \bigg) \ * \ t_{1} \bigg\} $$ Where y denotes convience yield which is unobservable. However the formula for a forward price can be used to retrieve y. This simulation generates the fixings in between the start of the fixing period and the end of the fixing period. It is simple to compute the average fixing: $$ a_{1} = \frac{S(t_{1})+ \ ... \ + S(t_{n})}{n} $$ where S is the fixing at each day of the fixing period (for each iteration) and n is the number of fixing periods. Methods for Lookback options are discussed here. For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers. A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).

$$ S(t_{1}) = S(0) * exp \bigg\{ \bigg(r \ - \ y - \frac{\sigma^2}{2} \bigg) \ * \ t_{1} \bigg\} $$

Where y denotes convience yield which is unobservable. However the formula for a forward price can be used to retrieve y.

This simulation generates the fixings in between the start of the fixing period and the end of the fixing period. It is simple to compute the average fixing:

$$ a_{1} = \frac{S(t_{1})+ \ ... \ + S(t_{n})}{n} $$ where S is the fixing at each day of the fixing period (for each iteration) and n is the number of fixing periods. Methods for Lookback options are discussed here. For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers. A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).

where S is the fixing at each day of the fixing period (for each iteration) and n is the number of fixing periods. Methods for Lookback options are discussed here. For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers. A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).

Methods for Lookback options are discussed here.

For education, many universities have Bloomberg. You will get quite reliable values for all of the products you mentioned by using the available pricers.

A related question may be how you get trade ideas for these options without knowing what they are worth (or how they are priced).