What is the Gaussian copula and how to use it to derive the joint probability of the default of two assets?
This is an interesting question, but I would like to discuss its implications and how this kind of model added fuel to the global financial crisis fire back in 2007.
Risk Management is like a Greek Tragedy, where actors laugh to express their sorrow. Hence, here what mimics laughter is the Normal (Gaussian) PDF and its assumptions.
I believe the strongest voice that emerged in the Post - Crisis years was that of Dr Nassim Taleb who heavily criticized risk management models and techniques that assume the Normality of returns, and its volatility in financial markets.
Most of the Credit derivatives and the structured products (Toxic Assets) that were financially engineered by Wall Street and the City Quants to provide a market-beating rate of returns used such techniques to pool assets having negative correlations
and a low conjoint PD.
Hence, completely overlooked how asymmetric correlations and extreme tail events(extending beyond 3 standard deviations) could complicate market and credit risk hedging in the event of a full-blown non - -normal financial crisis!
#Gaussian #Copula well and truly assumes that a financial risk statistical model will exhibit control properties on most of the days (Similar to the Gauge R & R - Repeatability & Reproducibility Analysis done in the Industrial Reliability Engineering field/s
to measure measurements/ standards) for a chosen confidence level and a risk horizon, but on the contrary, it did not live up to its reputation in backtesting.
This model was brought into use previously by Actuaries, and later infected the mindset of Quants/ Financial Engineers too!
Its rise in structured product and mathematical trading markets was idealized by the Chinese Quant Dr Andrew Lee (from China) who was working on Wall Street
Hence, it is crucial to read this article, I will add at the end of this thread, to get the hang of the full story and how it devastated the world economy back in 2007!
One big reason for the model’s failure was the lack of data samples available in the market to backtest the credit derivative losses with accuracy.
This led to complacency when undertaking model validation by the MBS Portfolio Product Developers and Marketers.
However, a successful risk/ pricing model would require not just validation but also cross-validation using training _ testing the data split technique used in data science and machine learning.
Professor Li didn't do any of that!
Please click on the URLs below to get the full story behind this financial engineering/ risk management model and its apocalyptic characteristics.

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More from @SAH16928046

8 May
According to Hendry and Richard (1982), a final acceptable model should
satisfy several criteria (adapted slightly here). The model should:
● be logically plausible
● be consistent with underlying financial theory, including satisfying any
relevant parameter restrictions
●have regressors that are uncorrelated with the error term
● have parameter estimates that are stable over the entire sample
have residuals that are white noise (i.e. completely random and exhibiting no patterns)
● be capable of explaining the results of all competing models and more.
Read 4 tweets
8 May
When we talk of IR #Interest #Rate #Risks we must understand the markets in which this product operates, and the fundamental pricing, trading, and hedging dynamics of this financial #derivative asset class.
Banks normally use IR Derivatives and Structured Products for on and/or off-balance-sheet ALM Asset Liability Management and Immunization, Bond Risk Hedging, NII Risk Hedging, Arbitrage Opportunity Exploration using the treasury based fixed income desks, Rate Speculation, etc.
Of course, we have other financial market participants such as Pension Funds, Hedge Funds, Insurance Companies, and several other specialized asset management firms, that have strategies and asset allocation models, which use IR derivative for both Macro and Micro-hedging.
Read 26 tweets
19 Apr
What factors are considered by banks when assessing credit risk to customers?
Credit Risk Management is part of IRA - Integrated Risk Assessment that is carried out by banks to measure transaction and obligor default risks.
The credit risk assessment goes through stages =>
Front Office (RMs at the branches and/or Head Office prepare the credit application/ Clp for further processing).
Middle Office (Financial Risk Management Analysis for checking the Basel Pillar 1/2 Compliance Requirements, to check the BRMC - Board Risk Management Approved and Assigned Risk Appetite Limits etc.)
Read 22 tweets
19 Apr
What is better for working in the banking and financial services industry, a Master’s in #Economics or #Finance?
@CFAinstitute @LSEEcon @LBS
A very broad question!

How would you define Finance Roles across FI and Non -Fi Settings?
Finance work is required both within and outside the financial services industry. I can share my collective understanding of roles(that might require Economics or Finance related skillsets) based on my experiences, that I have collected on my CV.
Read 14 tweets
10 Apr
Does #risk management involve a lot of maths?
Yes and No!

It's a well-rounded discipline.
For #Actuarial, Insurance, Financial Engineering, Quantitative Finance and Investment Management, Mathematical Trading, and Financial Risk Assignments, you might require a lot of Maths and Statistics.

Is like asking whether we need milk and sugar for making ice cream ;)
Read 9 tweets
8 Apr
Highlights "Winsemius in Singapore's Economic History" via @YouTube
We must remember what Lee Kuan Yew used to say about the prospects of political system democracy in underdeveloped societies.
This American or UK Style one man one vote political system has no cultural, political, societal, economic, or dialectical antecedents in several of the Asian, Latin American or African Countries.
Read 4 tweets

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