spot-derivatives-analysis.md

Spot Derivatives Analysis Results Report

Last Update September 14, 2021

Matteo Bottacini, [email protected]

Project description

In this report are show the results obtained for the Spot Cryptocurrency Derivatives Market.

The codes described are in ../deliverables/run-spot-analysis.py which is the only script you need to run to perform the analysis. The images are in ../reports/images.

Note: all the images in this GitHub repo are .png instead of .pdf. The reason is to make it easier for GitHub to render the results.

Table of contents

1. Create the Environment: directories and sub-directories
2. Data Pre-processing
3. Implied Volatility Smile
4. Implied Volatility Surface and ATM Term-Structure
5. Greeks: Surface and ATM term-structure
6. Implied Volatility-Delta Surface

Create the Environment: directories and sub-directories

The first step is to create the environment to store the results using the self-function create_env()

# import modules
from SpotAnalysis.src.utils import *

# create environment
create_env(local_folder='deliverables')

Data Pre-processing:

The second step is to preprocess the data for both BTC and ETH using the self-function `data_preprocessing() and store the results.

# import modules
from SpotAnalysis.src.utils import *

# preprocessing data
btc_df = data_preprocessing(coin='BTC')
eth_df = data_preprocessing(coin='ETH')

Implied Volatility Smile

The third step is to inspect the first stylized fact of Options: implied volatility smile. We want to look at the smiles of options with the closest expiry to 90 days. We do this operation for both BTC and ETH.

Note: if you want to look at different expires change the variable 90 in time_to_maturity=90 to the maturity of choice.

# import modules
from SpotAnalysis.src.utils import *

# iVol Smile plots - 3 months to maturity
iv_smile(coin_df=btc_df, coin='BTC', time_to_maturity=90, cwd='deliverables')
iv_smile(coin_df=eth_df, coin='ETH', time_to_maturity=90, cwd='deliverables')

Thus, we obtained these two smiles:

Implied Volatility Surface and ATM term-structure

The fourth step consists in analyzing the Implied Volatility Surface and the ATM implied volatility term-structure. We do this operation for both BTC and ETH. The function to perform is: implied_vol().

# import modules
from SpotAnalysis.src.utils import *

# iVol Surface and ATM term structure
implied_vol(coin_df=btc_df, coin='BTC', cwd='deliverables')
implied_vol(coin_df=eth_df, coin='ETH', cwd='deliverables')

The BTC surfaces are estimated with a (i) nearest interpolation, (ii) linear interpolation, (iii) cubic interpolation. Here are the results:

And the BTC ATM implied volatility term-structure estimated with (i) nearest interpolation, (ii) linear interpolation, (iii) cubic interpolation are:

For ETH the surfaces estimated with (i) nearest interpolation, (ii) linear interpolation, (iii) cubic interpolation are:

And the ETH ATM implied volatility term-structure estimated with (i) nearest interpolation, (ii) linear interpolation, (iii) cubic interpolation are:

Greeks: Surface and ATM term-structure

The fifth step is to analyze the surface of some greeks (Delta, Gamma, Rho, Theta) and their ATM term-structure for both BTC and ETH. The function is greeks():

# import modules
from SpotAnalysis.src.utils import *

# Greeks Surface and ATM term structure
greeks(coin_df=btc_df, coin='BTC', cwd='deliverables')
greeks(coin_df=eth_df, coin='ETH', cwd='deliverables')

The BTC greeks surfaces are:

The BTC Greeks ATM term-structure are:

While the ETH Greeks surfaces are:

And the ETH Greeks ATM term structure are:

Implied Volatility-Delta Surface

The sixth and last step is to evaluatue the Implied Volatility Delta-Surface for both BTC and ETH. The function that performs the task is:

# import modules
from SpotAnalysis.src.utils import *

# iVol Delta Surface
iv_delta_surface(coin_df=btc_df, coin='BTC', cwd='deliverables')
iv_delta_surface(coin_df=eth_df, coin='ETH', cwd='deliverables')

The BTC iVol-Delta surface is:

The ETH iVol-Delta surface is: