Gabriel Ryan, FRM on LinkedIn: Vasicek Interest Rate Model Vasicek model. A term that has different… (2024)

Gabriel Ryan, FRM

Vice President at DBS Bank | Risk Analytics | Data

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Vasicek Interest Rate ModelVasicek model. A term that has different meaning to different group of quants.For credit risk - most likely the first impression is the Vasicek loan loss model, the conditional default probability model underlying the Basel IRB RWA formulation.For others, most likely it would be the Vasicek interest rate model - a stochastic model that describes the random process of interest rates. It is one of the earliest short rate model, that is mean reverting. The Vasicek model has a long run mean parameter b, and a speed of reversion parameter a and volatility parameter sigma. The model allows for negative interest rates.The Vasicek model calibration can be done using historical data, by the least squared estimation. One can get the regression setting from the discretized version. PS:The Vasicek interest rate model follows the Ornstein–Uhlenbeck process.

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Youssef AIT ALI

User Automation & Management Reporting @ Revantage Europe | Financial Engineering, Data Analysis

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Thanks for sharing about the Vasicek model! It's interesting how this model captures the randomness of interest rates through a mean-reverting process.Looking forward to diving deeper into these topics!

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Thijs van den Berg

Head of AI & Quantitative Research, Shell Asset Management Company

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There is this great view by Paul Wimott about how the Vasicek model came about: https://youtu.be/ZNqkyKNobLE?feature=shared&t=218

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Daniel Souza Barbosa

Pricing | Quantitative Risk | Analytics | BI | R | PYTHON | SAS | SQL

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Vasicek model is really good because of its simplicity and use of randomness described by wiener process.Despite this, the model considers constant volatility.Which can limit its usage for credit risk, for example.Hull-White two factors model ie. Consider time-varying volatility in short rates. Can be used to models market risk in different terms.I love this subject, thanks for sharing!

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Parth Bhanushali

CQF| Computational & Quantitative Finance | CFA L3

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Gabriel Ryan, FRM ssssshhh.. -ve interest is like lord voldomorld, you never mention his name💀💀

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  • Gabriel Ryan, FRM

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    Actuarial Science & Credit RiskSome thoughts on the two distinct professions and activities. Insurance actuarial risk and banking credit risk is kind of in a quantum superposition - similar yet different at the same time.Some key similarities -1. Uncertainty around time to event, either future claims or loan default2. Modeling time to event3. Both require loss reserves4. Both require capital buffersSome key differences -1. Insurance, receive regular cash flows from premium, which contributes to a pool of surplus and can be invested. While risk event requires insurance companies to meet claims obligations. No upfront cash overlay. 2. Bank loans, the bank disburse cash upfront and borrowers repay i.e receive regular cash flows. Upon risk event i.e default on loans, bank suffers a short fall on the loan balance owed.Thus the reserves between the insurance companies and banks are deployed differently.- Actuaries set reserves such that on average the insurer can meet its future claims obligation- Bank risk modelers set loan loss reserves to plug the shortfall in loan balances in defaultIn both cases, movements in reserves are directly attributable to P&L. Capital also is also in a quantum superposition - similar but different:- Similar in a sense, both insurance companies and banks hold capital to cover any unexpected losses - different in a sense, insurers may face unexpected large claims and need buffer to meet such obligations. Banks on the other hand, may face large unexpected defaults, losses eating into its capacity to lend further. Risk events also trigger different actions:- For the insurer, upon receiving a claims, insurers either proceed with settlement or dispute the claim, leading to some court battle. - For banks, upon default of loan, banks start the loan recovery / collection to reduce losses. Involve recovery agents, liquidators etc. Thus we can see, from an actuarial and credit risk point of view, much similarities and much differences. PS:I am no insurance expert. Happy for experts to chip in.

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  • Gabriel Ryan, FRM

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  • Gabriel Ryan, FRM

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    When will Mr. Salary grow up? Mr. Salary does grow older though. Reality!Image: author

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  • Gabriel Ryan, FRM

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    Credit Risk - Vasicek Model For Default Probability (PD)The Vasicek model is probably the most well known structural credit risk model, being the basis for credit risk RWA model under advanced internal ratings based IRB approach.The Vasicek model describes the condition default probability, conditional on the realisation of a systematic state variable.The back story is that a borrower's assets is seen as random, and the returns on the assets are described by a standard normal random variable, V, that is decomposed into a systematic variable Y and borrower specific idiosyncratic variable, with an asset correlation parameter.Then we can derive the conditional default probability, a well known formula. Then from here there are few areas this can be used:1. Firstly, under the Large hom*ogeneous Portfolio approximation assumption, we can derive the Basel IRB formula. Probably topic for next post.2. For IFRS9 - the conditional default probability model can be used to convert a through the cycle TTC PD to a point in time PIT PD. This can be achieved by constructing a time series of the systematic variable Y, or the so-called credit cycle. More for wholesale, large corporate exposures.3. Stress testing. We can stress the cycle Y and obtain a stressed PD4. Portfolio credit analytics (from asset managers perspective)Probably a few other use cases but broadly, these are major uses.PS:Often in the IRB sense, the systematic variable Y is latent, not observed. But in IFRS9, stress testing, the systematic variable is necessary.

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  • Gabriel Ryan, FRM

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  • Gabriel Ryan, FRM

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    Malaysia and Its Unique English (Manglish)Being a former British colony, some feel Malaysians (and our Singaporean neighbours) have butchered the English language. But it is what makes Malaysia (and Singapore) unique. A localised version of English amongst locals, while we try to conform to standard English when communicating more formally.The Manglish (and Singlish) amalgamates most of the local languages together (English, Chinese, Malay, sometimes Tamil). Some takes in Malaysia:- We don't say "I finally understand, I get it now". We say "Ooooo" a long prolonged "Oh".- We don't say "I beg your pardon, can you repeat please?". We say "Haarr?"- We don't say "Here you go, this is for you." We say "Nahh".Notice intonations and sounds matter alot in delivering a message 😂. Single word sentences are more than enough to say a thousand things. Some other takes:- We say "eat where?" instead of "where shall we have lunch / dinner?". Probably a direct translation from Malay i.e "makan mana?" Could be a direct translation from Cantonese too.- We say "don't play-play" to mean "that's dangerous, don't do it or it might get you in trouble". Probably a direct translation from Malay's "jangan main-main".A single sentence could contain multiple languages simultaneously, like:"Eh bro, eat where? Think fast fast. If not, just tapao. Later late for meeting, boss marah nanti". Translation: "We need to decide quickly where should we go for a meal. Otherwise, let's do take away meal. We have a meeting later. If we are late, the boss won't be happy ". This is what makes the country unique and brings the people together. For most part. I should like to think the majority of people are thinking straight and ignoring the political rhetorics from politicians.So many more examples, I am sure my fellow Malaysians can give more examples. PS:- Video from Epic Asian YouTube channel - Gostan likely came from the phrase "go astern", a old English sailing term to mean "go back" towards the stern, the back of the ship.

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