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Name: Domenico Picone

Type: User

Bio: Interest Rate and Credit Derivatives, CLOs and RMBS, Residential and Equity Release Mortgages

Location: London

Domenico Picone's Projects

credit-risk-anaytics-the-r-companion

The rep contains the R files from Credit Risk Analytics: the R Companion, from H Scheule, D Rosch an B Baesens. Unfortunately, when I bought the book, I could not find the R files accompanying the book. So I decided to create the R files from the snippets of codes in the book. Hope you find the files useful. I also think this is an excellent book for those with an experience in both credt risk models and programming in R. If instead you want to learn R or credit risk, this is not the best book for you.

estimating_credit_rating_transition_matrices_hazard_vs_cohort

Estimating Credit Rating Transition Matrices in Python using Hazard and Cohort Methods

estimating_default_and_asset_correlation_with_csharp_exceldna

In this repository we estimate asset correlation using C# and ALGLIB. The functions are exported to excel via ExcelDNA As a background see our previous repository "Estimating_Default_and_Asset_Correlation_with_Python".

estimating_default_and_asset_correlation_with_python

Estimating default and asset correlation with method of moments and maximum likelihood method

fitting_and_simulating_archimedean_copulas

Fitting and Simulating Archimedean Gumbel, Clayton and Frank Copulas

fitting_t_student_copula

In this notebook, we implement algos to fit a bivariate 𝑡-student copula.

heath_jarrow_morton_model

A Multi Factor HJM MonteCarlo Model with Principal Component Factors

ho_lee_model_calibration_with_csharp_exceldna_and_python_xlwings

Calibrating Ho and Lee short term interest rate model in C#, ExcelDNA, Alglib, Python and xlwings. In this repository, we use both C# and Python to calibrate Ho Lee model's "theta" on market data. The calibration algo is explained in Veronesi, Fixed Income Securities, Chapter 19, at pages 653-659. The non linear solvers are from https://www.alglib.net/ when working with C#, and scipy.optimize when working with Python. We previously covered the implementation of the Ho Lee model together with three additional interest rate models, Vasicek, CIR and Hull and White, in our Repository "Vasicek_CIR_HoLee_Hull_White_Models_Python". With both C# and Python, we use an excel spreadsheet to enter market data and see calibration results. The communication between C# and excel occurs via "ExcelDNA" whereas when working in Python we used "xlwings". For more on ExcelDNA and C# see also my previous Repository "MortgageLoanCsharpExcelDNA". We also found a similar work in https://mikejuniperhill.blogspot.com/2014/10 by Mikael Katajamäki with a different calibration algo from Veronesi. Mikael's work is in our view an excellent example on how to combine C# and ExcelDNA to solve a financial problem. For the xlwings we refer to https://www.xlwings.org/.

integrals_in_python

Enjoying Integrations with Python

libormarketmodel

Single and Multi Factor Libor Market Model with Monte Carlo simulations to price a swaption receiver and a zcb option

mbs-prepayment-in-python

Two years ago, I literally stumbled on this superb note on how to use python functions to model the repayment of a mortgage loan, https://medium.com/swlh/simple-mortgage-calculator-with-python-and-excel-b98dede36720#8e39. At last, I found the time to add a simple (hope useful) module to include some prepayment tools. I also included two excel files to ease the problem of understanding prepayments

mortgageloancsharpexceldna

Building Mortgage Loan Cash flows with C# Excel DNA Add-ins. In this C# project we create a simple mortgage loan library to deal with the repayment of a loan. The library deals with two types of amortisation: Principal and Interest, and Interest Only. The loan interest rate can be fixed for life, or fixed for the first years, and then it resets to a variable rate plus a spread. We use Excel DNA to expose our C# library to excel. Excel DNA provides an easy way to create C# functions which can then be used in Excel. Our simple mortgage library can be used either via a VBA macro or as a standard excel function. Additionally, we also added an extra useful feature: the loan object can be stored in memory, and consequently run to produce cash flows. This is indeed very useful. Imagine you are dealing with a more complex task: pricing several interest rate option structures with the same interest rate simulation model. The only differentiation in each option is the payoff. You do not want to rerun the interest rate simulation each time you price a single option. This is very inefficient. Is it possible to call constructor once, store the simulation object somewhere and then use it to compute option price based upon its payoff? To the best of my knowledge, this solution was originally presented by Alex Chirokov in https://www.codeproject.com/Articles/1097174/Interpolation-in-Excel-using-Excel-DNA. We reuse here his original implementation and allow the user to create a loan, store it, and then run the relative cashflows. We have also attached the ExcelDNA manual by Govert van Drimmelen, https://excel-dna.net/, "Excel-DNA - Step-by-step C# add-in.doc" Before changing the project, check it is working. Open the excel file "ExcelMortgages.xlsm" and the xll "MBSExcelDNA_ForDistribution.xll". You need ExcelDnaPack.exe only when releasing the project. You may never need to use it. Again read the doc file for more info. In case you need more help with ExcelDNA, Mikael Katajamäki runs a very useful blog for everyone interested in using Excel DNA. https://mikejuniperhill.blogspot.com/2014/03/using-c-excel-addin-with-exceldna.html.

nelsonsiegelsvensson

The Nelson-Siegel and its extension Nelson-Siegel-Svensson are two of the most popular yield curve models They are very useful when we do not have nicely spaced bond quotes data to build the discounting factors. Sometimes up until certain maturies there are two many bonds compared with the number of maturities, and not sufficient bonds thereafter. Very often, there are multiple bonds maturiting in the same months. However there are several issues when trying to calibrate the model on real market data. The first python notebook contains an introduction. We are working on a second notebook which will deal with a more robust caibration

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