Topics In Pension Risk Management: Fill & Download for Free

Asset allocationFrom Markowitz to Black-LittermanYou might not do anything else than improving asset allocation models at your work for example, given old MV models are all ye' olde shite.Risk managementeverything from Greeks to VaRYou could end up as market risk manager doing nothing else than evaluation of VaR, PV01, DV01, inverse gamma etc from a trading desk. Or invent new risk performance adjustment methods outside hogwash like Sharpe etcStochastic differential equations/stochastic processesBrownian, Euler, Taylor, Cameron-Martin-Girsanov etcYou need to understand the concept of these models. They are extremely easy, it's just that certain risk tools will use different methods of risk calculation, and as quant you need to be able to understand deviations in figures while using a X or Y based model. You will get the question why does my VaR of my pension portfolio look different when I use a Taylor based approach rather than a Standardized approach.Ability to understand codeFrom basic notepad fiddling in VB to anything elseDude, you gotta fucking code at work. Preparing a language is fucking useless as every firm uses different languages, so be flexible.Policies and regulationsRegulators enforce new QF models all the time for financially regulated firms, this is a must to be on top of it, and read and implement about what's coming.I raised that in a different topic about weather derivatives. You might end up spending weeks to calculate the risk in your portfolio if the overall temperature increases by 1 degree.Bayesian econometricsBayesian allocation models work better than normal asset allocation models. Understand the concept of Bayesian. Their distribution and ability to allow for a prior informed by senior management often allows for a more accurate distribution you could use for your models.And this field can be applied to every topic above.What is not relevant.Utility modelsNo one gives a fuck. No one cares. It's all philosophical bullshit and tinfoil hogwash driven by looney economic students with some maths background. If you face these topics in your QF classes try to avoid these models. The debate risk aversion/risk seeking is one you can hold for weeks, with no agreement in sight, and a wasted week you could spend on something else.All of this is relatively easy. It just takes time to understand the concept. Rinse and repeat.

An FRM certificate doesn't help for a job in London.An FRM helps for 2 kind of people.Graduates who want to be better than their peers. A grad with a FRM is favorable over a grad without (obvious) - as you don't need a FRM when you are already working in risk management.Someone who works in finance but wants to change careers. Like from pension risk analyst to market risk manager. An FRM will then come in handy.An FRM is useless (besides interesting) in any other case. It's not a prestigious certificate and a grad in risk management in London would have covered every topic in FRM within his first 2 years before he leaves his grad scheme.This is regardless of where you live, (NY/London/Warsaw/Delhi).

The modules for Actuarial Science and Financial Risk Management (FRM) are quite similar but different in focus. In my opinion, the actuarial programme is more extensive than the FRM.The Actuarial science programme at Cass is quite vigorous and is best suited for the insurance industry. My quant colleagues at Cass— quantitative finance, financial mathematics, mathematical trading and finance— not only take the CFA but also the FRM — these combination gives the professional a wider scope of the finance industry.These are the modules for MSc Financial Risk Management at UCL:Financial Data and StatisticsFinancial EngineeringMarket Risk, Measures and Portfolio TheoryStochastic Processes for FinanceOptional modulesStudents choose 60 credits of optional modules from the following:Applied Computational Finance (15 credits)Compliance, Risk and Regulation (15 credits)Equities, Foreign Exchange and Commodities Modelling (15 credits)Financial Institutions and Markets (15 credits)Machine Learning with Applications in Finance (15 credits)Market Microstructure (15 credits)Networks and Systemic Risk (15 credits)Numerical Analysis for Finance (15 credits)Operational Risk Measurement for Financial Institutions (15 credits)Quantitative Modelling of Operational Risk and Insurance Analytics (15 credits)These are the modules for MSc Actuarial Science at Cass:Financial Mathematics (CT1)Finance and Financial Reporting (CT2)Probability and Mathematical Statistics (CT3)Business Economics (CT7)Research Methods for Actuarial ProfessionalsModelling (CT4)Contingencies (CT5)Statistical Methods (CT6)Financial Economics (CT8)Optional modulesAn Introduction to Machine LearningIntroduction to Copula ModellingIntroduction to Model Office Building in Life InsuranceModelling and Data AnalysisSocial Insurance in Emerging MarketsStochastic Claims Reserving in General InsuranceTopics in Quantitative Risk Management Financial DerivativesEthics, Society & the Financial SectorLiability InsuranceVBA with Application for FinanceMarine, Aviation and Transport InsuranceMatlabPension FinanceTechnical Analysis and Trading SystemsFinancial Derivative