Projects GOAL OF THE PROJECTS • Prepare to your next professional life • Conduct a R&D project on current topics • Experience the challenges faced by quant teams DELIVERABLES • 15 pages maximum report • Tool implemenLng the model • 30 minutes presentaLon TIMELINE • November: make groups • Last lesson: round tables and targets definiLon • Beginning of March: delivery of the report and the tool • Mid March: oral presentaLon in our office in La Défense
Machine learning on a Kaggle database: Give me some credit DESCRIPTION OF THE PROJECT • Retail banking customers are more or less likely to default based on the value of their own credit risk drivers • The Kaggle database includes credit risk observaLons and values of the features • StaLsLcal modeling can be used to link both informaLons TARGETS • StaLsLcal analysis of the database. Prepare a training set and a test set • Select and calibrate some machine learning algorithms to accurately predict default • Compare XGBoost and neural networks REFERENCES • www.kaggle.com • Pacelli, Azzolini: An arLficial neural network approach for credit risk management • Yobas, Crook and Ross: Credit scoring using neural and evoluLonary techniques
Machine learning on a brasilian consumer credit database DESCRIPTION OF THE PROJECT • Retail banking customers are more or less likely to default based on the value of their own credit risk drivers • The database includes credit risk observaLons and values of the features • StaLsLcal modeling can be used to link both informaLons TARGETS • StaLsLcal analysis of the database. Prepare a training set and a test set • Select and calibrate some machine learning algorithms to accurately predict default • Use the H20 or WEKA systems REFERENCES • brasilian database • H2O website • WEKA website
CVA modeling for an interest rate swap DESCRIPTION OF THE PROJECT • The drivers of the CVA for an interest rate swap are the whole interest rate curve, its volaLlity and the credit spread of the counterparty. • As CVA is a complex and exoLc derivaLve, liale is known on its senLvity to some risk factors TARGETS • StaLsLcal analysis of the interest rate curve dynamics • Develop a Swap pricer and and CVA pricer • Analyze the sensiLvity of the CVA to IR level, slope, curvature and volaLlity REFERENCES • P. Priaulet, Produits de taux d’intérêt (Economica)
Numerical method to compute CVA DESCRIPTION OF THE PROJECT • CompuLng CVA and DVA is very computaLon intensive • Henry-Labordère has developped a numerical method to achieve such computaLons accurately: the parLcular method TARGETS • ImplementaLon of Henry-Labordère’s approach • Pricing of the CVA on an interest rate swap REFERENCES • Pierre Henry-Labordère, Counterparty risk valuaLon: a marked branching diffusion approach (haps://arxiv.org/pdf/1203.2369.pdf) • Pierre Henry-Labordère, Culng CVA’s complexity, Risk (2012)
CDO models and implied correlaBons
DESCRIPTION OF THE PROJECT • Applying Vasicek model on CDO market data, one finds that the implied correalLon is not constant through the distribuLon of losses of the underlying pormolio. TARGETS • Apply Vasicek model on iTraax data to see this phenomenon • Try different models (Double t, Clayton, ExponenLal, Student, StochasLc, etc.) to find one that fits well the market REFERENCES • Laurent J.L., A comparaLve analysis of CDO pricing models, 2008 hap://laurent.jeanpaul.free.fr/comparaLve%20analysis%20CDO%20pricing %20models.pdf
CVA Wrong Way Risk MulBplier DecomposiBon and Efficient CVA Curve
DESCRIPTION OF THE PROJECT • CVA esLmaLon is computaLonally challenging. • In their paper, Pang et al. define an algorithm based on the so-called Robust correlaLon and Efficient Curve Filng to compute CVA more efficiently. TARGETS • Apply Pang et al. algorithm • Discuss its limits REFERENCES • Pang et al., CVA Wrong Way Risk MulLplier DecomposiLon and Efficient CVA Curve (hap://www4.ncsu.edu/~tpang/MyPapers/Pang_Chen_Li_2015a.pdf)
For a given threshold l0 < LGD, compute [(L â l0)+]. ... consists in the securitization of a portfolio of mortgage loans which have the same ... Loan to value: 75 %;.
This exercise is based on the following S&P transition matrix: .... order to make the results reproducible, we set the seed of the random number generator before.
Swap: the reference is a reference pool (usually between 5 and 10 reference entities) ..... The CRM is a risk metrics that, as the IRC, captures the risks due to.
the common threshold and Ri a normal variable equal to: Ri = ÏF + 1 â Ïεi, where F and ... consists in the securitization of a portfolio of mortgage loans which have the same ... depend on the initial amount provided (1 - Loan To Value), the rat
Write the value of the debt of the firm for the debt-holders, of the shares of the firm for the shareholders, as an option on the value of the firm and with maturity the ...
counterparty risk management: for internal purpose and for regulatory capital .... An airline usually protects itself against a rise in fuel prices by entering into long.
1 Credit risk models to fulfill regulatory requirements and prevent the bank from failure .... IRB Advanced: modeling of PD, LGD and EAD. .... Asset Management.
Lecture 2 â Statistical tools for scoring and default modeling. François CRENIN .... The logistic regression model can be defined the following ways: ... therefore lead to different estimations depending on the algorithm/software chosen. .... Ther
With a barrier option approach and stochastic interest rate [Brys et al., 1997];. â· Taking into account ..... Journal Of Finance. Link. Modigliani and Miller (1958).
that PD = Q(Ri < s) = Φ. ︸︷︷︸. Normal cdf ... A copula C, is a function that is used to model dependencies: â(x1, ..., xd ) .... Gumbel compulas, Student copulas, grouped t-copulas, individual t-copulas, etc.;. â· the so-called ..... Delt
Class structure and assignments Credit risk and economics Credit risk outcomes Credit risk: The basics ... Credit Risk. Lecture 1 â Introduction, reduced-form models and CDS .... Source: Aspects of Global Asset Allocation, IMF. and personal cross-c
Let us consider the loan portfolio of a bank made of 200 000 loans for an average amount of 100 000 EUR. ... default occurs when the value of equity is smaller that a threshold. .... Exercise 3: The subprime mortgage crisis: a model risk crisis?
year conditional on no earlier default is 3 %. 1. Estimate the ... that buys protection on A during the period starting in 3 years and ending in 7 years. We assume ...
2 Banks are financial intermediaries which fill the gap between supply and demand on money market and the risk ..... Banking services. Proprietary Trading.
Risk-Neutral Valuation: Pricing and Hedging of Finance Derivatives. N.H, Bingham and R. ... to credit risk, our goal was to introduce the basic concepts and related no- tation, rather than to ...... 3.5.3 Incomplete Accounting Data. 113 ..... high le
For details of our global editorial offices, for customer services and for ..... spelling mistakes and bad grammar to provide some invaluable suggestions. .... A lack of proper assessment of credit exposure and default probability was a key ...... In
Credit derivatives. OTC derivatives. Corporate. Retail. Basket default swaps. Contingent ... one can easily deal with a large number of names,. â« Tackle with ...
announcements, should lead to a drop in the company's bond prices. .... associated with the names in the credit portfolio, that can be either historical or market.
Jul 1, 2003 - This correlation parameter is not estimated in principle by the banks but computed ..... 16 It is easy to prove additivity in the single factor setting. ..... 20 As the for the quantile based risk measure, the expected shortfall based r
