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Credit Risk · Quantitative Finance · Markets M.Sc. Finance student at the Universität Liechtenstein, after a B.Sc. in Economics and Finance at Bocconi. I work on credit risk and quantitative finance, and I like turning stochastic calculus into tools that price and measure risk.
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I focus on credit risk and quantitative finance: probability of default, loss given default and exposure at default modelling, IFRS 9 expected credit loss, derivative valuation and market risk. I spent six months at Deloitte in the Financial Services audit practice, working on credit risk and IFRS 9 across Banking and Capital Markets engagements, from PD, LGD and EAD estimates to ECL staging and model validation.
What I enjoy most is building things that turn theory into numbers you can act on, from stochastic pricing models to full loss distributions. The visuals below trace that path in three steps: the mathematics of random processes, the measurement of risk, and the pricing of credit.
It starts with randomness. These are the processes that everything downstream is priced on, from Brownian motion to the jump models that capture defaults.
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Feed those paths into a book and you get a loss distribution. The work is then measuring its tail with VaR and Expected Shortfall, and watching how risk moves through time.
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Apply the same machinery to issuers and you are pricing credit, across the curve, the cross section, and decomposed into its drivers.
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Credit Risk Analytics Dashboard · github.com/andrealandini/credit-risk-modeling
A quantitative credit risk dashboard in Flask with nine interchangeable models for PD, LGD and EAD (Expected Loss = PD × LGD × EAD), including Logistic Regression, Merton Structural, Beta Regression and Markov Transition. It runs a Monte Carlo portfolio engine (3,000 paths) on the Vasicek single factor model with stochastic Beta LGD, and an ECB style stress testing module with baseline, recession, stagflation and recovery scenarios.
Languages
Libraries
Tools and Data
My work sits at the meeting point of finance, statistics and code. The themes I keep coming back to:
- Credit risk modelling: PD, LGD, EAD, ECL under IFRS 9, and credit portfolio models such as Vasicek.
- Market risk: VaR, Expected Shortfall, GARCH volatility and dependency modelling with copulas.
- Derivatives and pricing: Black-Scholes, Heston, and Monte Carlo methods for valuation and simulation.
- Regulation and resilience: Basel III and IV capital frameworks and stress testing.
- Building tools: small quantitative web apps that I deploy and run on a VPS.
Away from the screen I work in three languages: Italian (native), English (fluent) and German (B1).
The ticker uses real market data (Alpaca, free tier, refreshed daily). All analytics are driven by synthetic data and are illustrative only.