# Introduction

The counter party credit risks are for the survival of the probabilities and the hazard rates which are reviewed. The effectiveness is based on the advancement with the electives that includes the viable combinations for the risks budgeting elective that are recommended as the primary choice. The counterparty credit risks are for the credit spread pricing and the interest rates where the trading strategy is for the utilization of the cointegration of the pairs and the basket trading. The deep learning and the machine learning with the two modules with the deep learning and machine learning types of ML modules of material.

# Mathematical Description of Models

The module of materials includes:

Counterparty credit risks includes the primary choice for the CR and IR. The CDS includes the survival probability and then reviewing the hazard rates. It is about the numerical methods for the quant pricing of finance. The Monte-Carlo, Binomial Trees and the Finites Differences is for the simple LMM with the review that includes the pricing techniques and the computation (Johansson, 2019).

The risks budgeting is the primary choice with the Modern Portfolio Theory which tend to tie in VaR and Risk Decomposition which comes through derivatives with the optimization of the portfolio. The risks budgeting portfolio is important for the handling of the contribution type.

The data analytics includes the data structure with the numpu with numerical analysis with the time series analysis with the outcome that includes the python language

Machine learning with python is about the quant finance with the capabilities that are for the OLS logistics that involves the tensor with the example of the deep learning classifier with the outcome that involves the modelling of the back testing and the least square Monte Carlo in LMM framework.

Python Applications: This involves the techniques with the examples that includes the computation efficiency. It is for the linear equations and the root finding measures that involves the forms with the bisection, newton random numbers with the Jupyter Notebook with the in Credit Spread and Interest rate.

Advanced volatility Modelling is for the different stochastic volatility with the analytical solution that involves the approach related to the in-depth integration of the classic pricing of the PDE.

# Pros and Cons of Model

The model includes the pros which are related to the use of R and MATLAB with the sessions that are for demonstrating the python notebooks. There are pandas with the matplotlib with the forms that are for the considerable challenges to MATLAB. There are projects which are set through using the excel spreadsheet with not only the robust but also not giving the understanding the numerical methods. The CQF methods are for the excel spreadsheets with the code that are for the numerical methods with producing the small model that includes the ready functions that are for the technique.

The cons are related to the delegates who are working on the checks of sensibility and the validation. The code needs to be tested and documented with functions that are described and the comments which needs to be used. The portfolio is based on the forms with the framework that are for the Black Litterman which tend to shift and then there are return distribution on the directional views which includes the forms that are related to the data of portfolio. The choice and data includes the assets and maximum optimal bets with the factor tilts and the budgeting of the risks (Egger et al., 2019).

Improvements

The improvement is through the robust covariance and the optimization with the views that involves the benchmark which includes the computing on the covariance and the implementation is based on the computing optimization. The back testing is optional which involves the production and the rolling beta and the allocation is based on the deep learning for particular time series data. The analysis is also for the volatility estimators with the credit spreads and the indices which are for the new indicators. The features are related to the exponential feature scaling that comes with the stock and then handling the heteroskedastic volatility.

The focus is on the strategy design and backrest that involves the hand-on regression computation with the matrix form. The optimization is based on comparative testing and rolling ratios that comes with the analytical use of the ready code libraries. This comes with the cumulative approach and the step instructions for the trading design and re-code regression that is for the specification tests as well (Garcia et al., 2018).

# Conclusion

The focus is on the recognition of the credit and the risks adjustments with the derivatives for the business. Which comes with the max, median and the quartiles and the percentile. The data requirements are based on the market cap data which is for the calibration on the market volatilities.

# References

de Graaf, C., Kandhai, D. and Sloot, P., 2016. Efficient estimation of sensitivities for counterparty credit risk with the finite difference Monte Carlo method. Journal of Computational Finance, Forthcoming.

Egger, D.J., Gutiérrez, R.G., Mestre, J.C. and Woerner, S., 2019. Credit risk analysis using quantum computers. arXiv preprint arXiv:1907.03044.

García-Ruiz, R.S., López-Herrera, F. and Cruz-Aké, S., 2018. CDS pricing using a Copula-Monte Carlo Approach. Revista de investigación en ciencias contables y administrativas3(1).

Johansson, S., 2019. Efficient Monte Carlo Simulation for Counterparty Credit Risk Modeling.