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bloomberg-fixed-income-data-processing's Introduction

Standardised Bloomberg Fixed Income Data Processing

This project was started as a academic project I did for my fixed income securitiy module at Singapore Management University. The original data was simplfied and less complete. For this project I used the excel files generated by export function in Bloomberg. I manaully merged them into one but feel free to use different xlsx files.

Data Input: (data 3.11.xlsx) The data date is 11th of March 2019. Given that the current day date is constantly updated every few minutes. I used the closing rates so they are consistent and more respresentative. For new inputs, you can simply change the name of the file, and same computation will be performed.

This notebook doesn't take processing date adjustments into account. (i.e. when you buy an IRS today, the effective date will actually be a few days later)

For OIS (Overnight Interest Rate Swaps): 6 columns with a range of data dates. 10 Years maximum. Tenor, CUSIP, Description, Yield(%), Source, Update(Data Date, mm/dd/yy)

1D
1W
2W
3W
1M
2M
3M
4M
5M
6M
9M
1Y
18M
2Y
3Y
4Y
5Y
10Y

For IRS: (Interest Rate Swaps)

6 columns with a range of data dates. 50 Years maximum.

The time steps for first 2 years are 3 months. We stick to this frequency and boostrap a discount factor curve of 4x50 = 200 data points, up to 50 years. Note the maximum window for IRS (50yr) and IR Swaps (30yr expiry X 30yr tenor = 60yr) are different, for our Swaption pricing we do up to 30X20.

Tenor, CUSIP, Description, Yield(%), Source, Update(Data Date, mm/dd/yy)
3M
6M
9M
12M
15M
18M
2Y
3Y
4Y
5Y
6Y
7Y
8Y
9Y
10Y
11Y
12Y
15Y
20Y
25Y
30Y
40Y
50Y

IR Swaptions(Volatility): (Interest Rate Swaptions)

These vol data are of Black vol (market standard and primary in Bloomberg). We use the swaption data to calibrate our SABR parameters.

Note the maximum window for IRS (50yr) and IR Swaps (30yr expiry X 30yr tenor = 60yr) are different, for our Swaption pricing we do up to 30X20.

Expiry x Tenor -200bps -100bps -50bps -25bps ATM 25bps 50bps 100bps 200bps
3Mo x 2Yr
3Mo x 5Yr
3Mo x 10Yr
3Mo x 20Yr
3Mo x 30Yr
1Yr x 2Yr
1Yr x 5Yr
1Yr x 10Yr
1Yr x 20Yr
1Yr x 30Yr
2Yr x 2Yr
2Yr x 5Yr
2Yr x 10Yr
2Yr x 20Yr
2Yr x 30Yr
5Yr x 2Yr
5Yr x 5Yr
5Yr x 10Yr
5Yr x 20Yr
5Yr x 30Yr
10Yr x 2Yr
10Yr x 5Yr
10Yr x 10Yr
10Yr x 20Yr
10Yr x 30Yr
20Yr x 2Yr
20Yr x 5Yr
20Yr x 10Yr
20Yr x 20Yr
20Yr x 30Yr
30Yr x 2Yr
30Yr x 5Yr
30Yr x 10Yr
30Yr x 20Yr
30Yr x 30Yr

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bloomberg-fixed-income-data-processing's Issues

Using curve_fit instead of least_squares

In v1.1 the calibration of SABR is done by minimising error term with least_sqaure. Yet with this approach, the fitting for deep OTM contracts are poor (especially +200 bps contracts). I am looking to use curve_fit instead. By the time I wrote this issue, I have been working on the curve_fit for 2 days but little progress is made. I am looking to close it within this week.

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