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Study Paths for Risk Analysis of Bank Failures

Article Quant Q&A · Author: Esat Koç

Summary

The document outlines two broad routes into risk analysis: an empirical statistics route and a more theoretical modelling route. For the latter, it recommends building on calculus with measure theory and stochastic calculus, focusing on Brownian motion, martingales, semimartingales, Itô’s lemma, stochastic exponentials, and change of measure. It also suggests learning change of numeraire and names an introductory stochastic calculus text as a self-study resource.

These tools can lead into mathematical finance topics such as derivatives pricing, volatility modelling, and interest-rate modelling. The answer connects mortgage-backed securities to bank-failure risk, noting that quantitative analysis can involve value-at-risk, sensitivities, prepayment, and default risk. The guidance is a suggested route rather than a tailored syllabus: the appropriate depth depends on the thesis direction, and the author’s preference for theoretical modelling is explicit. The cited mortgage example also assumes access to market pricing data and does not itself provide an empirical thesis design.

Key ideas

  • Risk analysis can follow an empirical statistics path or a theoretical modelling path.
  • Stochastic calculus study can proceed through Brownian motion, martingales, Itô’s lemma, and change of measure.
  • Change of numeraire is presented as a useful extension for mathematical finance.
  • Derivatives, volatility, and interest-rate models are suggested applications of the mathematical background.
  • Mortgage risk analysis can combine pricing, value-at-risk, sensitivity, prepayment, and default considerations.

Tags

Full text
# what would be the most parsimonous sequence of study?


# what would be the most parsimonous sequence of study?












As a 3rd year undergraduate Economics student, I want to write my undergraduate thesis on Risk Analysis of bank failures. I want to prepare my theoretical background in summer time before the starting my senior year.So, what would be the minimum relevant sequence of study in my case to write an undergraduate thesis about risk theory.

My relevant background, non-measure theoretic probability & statistics ( read Newbold's Statistics for Business and Economics + Wackerly's Mathematical Statistics) , undergraduate level Econometrics ( read Gujarati's Basic Econometrics + Wooldridge's Introductory Econometrics + first few chapters of Davidson & Mackinnon's Econometric Theory and Methods) + Undergraduate level Linear Algebra + Advanced Calculus course series which constitutes topics such as intro to Point set topology and real analysis + Riemann Integration Theory and Differential Calculus. I have not taken any Risk Theory or Actuarial Mathematics courses.

Please give some field & topics recommendation so that i smoothly go into the target topics. Additionally, give some self-study textbook recommendations of your field recommendation.

## Answer by oliversm (score 1)

https://quant.stackexchange.com/a/27564

There are two main roots to risk analysis (as I see it):

- Statistics

- Modelling

where the first is more real-world and requires data analytics, whereas the second is more academic/theoretical. Personally I am more of a theorist and will advocate the second approach, but to have the maths for the second is a bit of a step up, and is built around stochastic calculus

Modelling using Stochastic Calculus:

@Quantes, It's good you took lectures from the maths department, and it seems like you have a sufficient grounding for normal calculus. As you mentioned though in order to understand how variance/risk adds and combines in a financial setting you will have to take a course in Stochastic Calculus, for which the starting point is normally an overview of measure theory. However, pick up any introductory book (many typically recommend Steele or Shreve) and skip ahead to Brownian motion, then martingales, then semi-martingales, Ito's lemma, and stochastic/Dolean exponential. The final two tools you will need are then Girsanov's Theorem and change of measure theory. Once you have learnt this you will be ample prepared for any course on mathematical finance!

That though just gives you the mathematical tools, as for an intuition into solving financial problems the only extra thing that I suspect would be well worth looking into is change of numeraire, (extension of change of measure).

The book which I personally would recommend, and think is both comprehensive and self contained enough to skip from chapter to chapter is:

- "Introduction to Stochastic Calculus with Applications" - 3rd Edition - Fima C Klebaner

albeit that is my own preference and maths level, and this will change between individuals.

With regards to your thesis: An Example topic

I can only make a guess about how what direction of risk analysis you would want to go into regarding your thesis, (you could learn all of the above and then look into many topics which don't use any of this). To have an idea about what sort of topics this will let you look into, here are a few examples.

- Basic derivatives pricing theory (e.g. The Black-Scholes framework).

- Volatility modelling.

- Interest rate modelling and forward curves for fixed income markets.

The last of these I think is generally the most interesting (my own bias), but undoubtedly could be linked into a discussion of banking failures surrounding the US subprime mortgage crisis. E.g. having a mathematical idea of how mortgage products are priced and sold is not too difficult an idea qualitatively, the tricky quantitative bit is to put numbers on the risk (e.g. VaR), and to model the risk and sensitivities correctly. E.g. the prices quotes on US mortgage pools (cf. TBA products in the [Agency] MBS market) can be found by most suppliers (Bloomberg), and a large focus is on the risk analysis and interconnections between prepayment and default risk, which to understand quantitatively requires both statistics and a good grasp of financial calculus.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.