If the connotation of risk is an intertwined concept and is difficult to quantify, how does a Risk Officer look at it?
Is there any way other than using copula models to determine systemic risk with long tails or a black swan event?
@CQFInstitute @GARP_Risk @SOActuaries
I guess we are worried about Market and Credit Risks or other interrelated financial risks which can create conjoint loss given events.
Any #Gaussian distribution model will enable you to model and predict potential Operational, Liquidity and Balance sheet AL - (Asset - liability) Mismatch, Market and Credit drove losses under normal market conditions.
But in the financial sector, the trend has been to emphasize more on #material and #quantifiable risks, which adhere to SPC #Statistical Process Control such as Credit and Market risks, at least in the context of financial instruments with a #linear pay -off.
Research has shown that even in the financial sector most of the #tail #risk event impacts are #Operational in nature=>
•75- 80% of the loss given events are classified as Operational #LGE - (LOSS GIVEN EVENTS),
•Only 15%- 20% of the LGEs are driven by Market or Credit or both
Stress testing is one tool which can be used to model risks to have correlated material impacts, for e.g. in the Hedge Fund Business.
#Risk managers can use #scenario #analysis based on an expert judgement, that can be more helpful, instead of applying #VaR models blindly.
Most preferred will be #Bayesian Methods applied to stress testing and scenario analytical models to assess #conjoint heavy-tailed risk impacts!
Using the #Gaussian #Copula Model to check Dependency / Co-Dependency can create further problems.
The Financial Markets outcomes are not Normal or i.i.d in any sense.
I assume you are referring to some other Copula Modeling Technique, such as the #Gumbel Copula?
Gaussian Copula model assumes that correlation (strength of association between x and y variables) is Linear and hence presents the symmetric perspective of market-driven loss events.
Operational Risk Modellers faced a lot of difficulty at banks when they were implementing the #AMA - Advance Measurement Approach Models.
Most of the Operational Risks were strongly correlated and hence copulas were used to model dependency/codependency risks.
But that didn't work out well using symmetric event modelling assumptions!
The real #Conditional #Tail #Risk is emanating from #Asymmetric Co-dependent Events!
Ultimately, @BIS_org guidelines were re-issued to Commercial Banks, and now they don't need to apply the AMA - LDA (Loss Distribution Approach) Methodology to compute Operational risk capital required by the deposit-taking institution.
I am sure hedge funds face similar kind of asymmetric risks which also develop co-dependency over time.
Kindly note Operational risk modelling of hazardous and other non -hazardous risk incidents and events require two distributions aka a compound distribution, (which consists of both the Frequency and the Severity Probability Distribution Models).
Both are statistically different from one another!
A #Poisson Probability Distribution Model to measure frequency, whereas stochastic simulation can be used to measure severity for each single event loss.
The loss frequency distribution must be combined with the loss severity distribution for each risk type/business line combination in order to determine a compound loss distribution.
The most common assumption used is that loss severity is independent of loss frequency.
How to accurately simulate this distribution?
This Loss distribution model for e.g. in the context of a Hedge Fund?
Can we induce randomness into the experiment to better capture heavy loss incurring tailed events aka Black Swan Events (having high severity and low frequency)?
#Hull (2015) suggests the following steps be taken in building the #Monte #Carlo #Simulation leading to the modelling of the loss distribution:
1. Sample from the frequency distribution to determine the number of loss events (nnn)
2. Sample nnn times from the loss severity distribution to determine the loss experienced for each loss event (L1, L2,…,Ln)
3. Determine the total loss experienced (=L1+L2+…+Ln=L1+L2+…+Ln=L1+L2+…+Ln)
An MCS Experiment might not suffice in picking up rare unforeseen abnormal loss events. It can help you to only optimize your understanding of normal risk incidents (high frequency and low severity) for a given level of probability.
Probably you need to use #EVT Extreme Value Theory to assess very large and rare losses.
BIS Basel Accord earlier provided guidelines under Basel II Framework (now stands revised).
Kindly refer to the same BASEL II - ORM Taxonomy as laid down in Basel II literature to better understand the vertical dependencies between various risk types/ across various lines
They are Seven pillars of OPs Risk Taxonomy in altogether =>
The following lists the seven official Basel II event types with some examples for each category:
1.Internal Fraud – misappropriation of assets, tax evasion, intentional mismarking of positions, bribery
2.External Fraud – theft of information, hacking damage, third-party theft and forgery
3.Employment Practices and Workplace Safety – discrimination, workers compensation, employee health and safety
4.Clients, Products, and Business Practice – market manipulation, antitrust, improper trade, product defects, fiduciary breaches, account churning
5.Damage to Physical Assets – natural disasters, terrorism, vandalism
6.Business Disruption and Systems Failures – utility disruptions, software failures, hardware failures
7.Execution, Delivery, and Process Management – data entry errors, accounting errors, failed mandatory reporting, negligent loss of client assets
A hedge fund might face similar operational and financial risks.
To better comprehend hedge fund business complexity you need to have a proper FMEA (Failure Modes and Effect Analysis) ....
OR #PDCA (plan -do check -act) / RCA (root cause and analysis) Methodology to properly map and drill down key risk factors and driver/s, which can enable the modeller to better qualitatively understand and visualize the risk incidents and loss given events that have big impacts!
Don't underestimate #ORM Operational Risk
Management that is a classic problem observed in the #FRM Profession, just because it is difficult to do quantitative modelling.

• • •

Missing some Tweet in this thread? You can try to force a refresh

Keep Current with Risk Manager(Banks,Asset Management,Insurance)

Risk Manager(Banks,Asset Management,Insurance) Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!


Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @SAH16928046

27 Jan
Why do young people leave quantitative trading 5 to 7 years in their career, and what's your advice for aspiring quantitative traders?
@CQFInstitute @RiskDotNet @icmacentre @RiskMinds
The burnout (losing interest in the job) and dropout(leaving the job) rates are stupendous.
#Quantitative Specialist Roles as they exist in the Dealing Room in the form of #Treasury, Brokerage, Fund Management, #Investment Management, #Portfolio #Asset Management, #Derivative Market Making, and various other Front -Office #Risk Roles are highly demanding jobs indeed!
Most of the traders are asked to take a mandatory leave of up to two weeks or more at financial institutions, so they can relax a bit by staying away from the financial markets.
Read 10 tweets
25 Jan
I am into risk management.
Most of the risk managers are now required to have an advance background in operating technological applications such online trading and price data terminals (Bloomberg/Reuters, etc),
FINTECH, Crypto Assets, Digital Marketing based vendor systems, DLT(Distributional Ledger Technologies) - Blockchain, AI / ALGO based trading in financial markets, Derivatives and Risk Pricing Engines and other Software Computational Programs,
Risk MIS/ERP Project Management Tasks, 4GL Fourth Generation Languages, Data Warehousing, BI, MI, SQL, NoSQL, and so on etc.
Read 6 tweets
15 Jan
How seriously is past volatility a fair estimate of future volatility or risk useful in financial models?
Historical Volatility based on empirical data sample observations.
Data Sample Observations can be historic baseline data for a particular asset class/exposure or simulated data derived from iterations using some historical data sets.
Another branch of data which can be used to observe future volatility is exploratory data drawn from within a sample or a population using data #visualization tools.

This technique is becoming popular as data science and machine learning advancements are taking place
Read 22 tweets
11 Jan
Would you start your career as a model validator in a major bank if you hope one day to become a front office quant?
@GARP_Risk @actuarynews
@Actuary_Dot_Org @actuarialpost
@iSixSigma @artemisbm @SOActuaries
@CQFInstitute @aier @CFAinstitute
Many Financial / Middle Office Risk and other Quantitative Economics/ Financial Market-led research roles interface at one level or the other across FIs.
I don't know if a bank uses Employee Rotation, to foster employee learning, training and development across the 3LOD Model?
But, most of the banks, in the Advanced Markets, to use job rotation as a tool, to disseminate professional knowledge and understanding of financial market operations, among their employees.
Read 21 tweets
22 Dec 20
Did the Asian Financial Crisis (1997) had any influence in the 2008 crisis?
No, Not really!

#SOX Compliance came after ENRON and WORLD COM Frauds and Financial Reporting Failures.

You cannot mix the two events.
Asian Financial Crisis came about as a result of Unsound Macroeconomic Policies, disrespect for stabilization, excessive price competition among trading nations, lack of Asset Liability Risk Management done at the Central Banks, monopolistic market structures,
Read 33 tweets
13 Dec 20
What are the different ways in which banks can reduce and manage different types of risk?
@GARP_Risk @actuarynews @SOActuaries @RiskDotNet
By understanding what risk is in terms of its purported definition?
If you get the definition wrong, you won't understand the technical expression in either theory or practice.
So, get that right first!
#Risk is a two-sided phenomenon, like a coin!

2. The one side of the coin presents an Opportunity!
2.The another side of the coin presents a Threat!

On which side would you like to do betting?
Read 18 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!