Discover and read the best of Twitter Threads about #ltcm
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The LTCM Crisis taught #Quants one thing in common.
Never trust the risk pricing/hedging models blindly.
The Black Scholes option model assumptions and the Value at Risk Metric both failed miserably.
The liquidity assumptions of the #VaR Model provided a false sense of security.
Never trust the risk pricing/hedging models blindly.
The Black Scholes option model assumptions and the Value at Risk Metric both failed miserably.
The liquidity assumptions of the #VaR Model provided a false sense of security.
The most worrying thing is that if #LTCM which was a hedge fund managed by two @NobelPrize winners in Economics could not get things right, then what should one expect from humble risk practitioners like myself!?
imagine the amount of risk that is concealed by Black Box Models.
imagine the amount of risk that is concealed by Black Box Models.
Always backtest and stress test your #risk models.
Not just the mathematical components, but also the semantics, the #hermeneutics, the syntax that is used for coding the automata, and the symbols which are shown on the analytical dashboard.
It is a big #CON industry at work!
Not just the mathematical components, but also the semantics, the #hermeneutics, the syntax that is used for coding the automata, and the symbols which are shown on the analytical dashboard.
It is a big #CON industry at work!
I have even met some foolhardy alternative investment aka asset managers that use the BSOP Black Scholes Option Pricing Model Calculator to price oil exploration, drilling and other natural resource mining risks.
BS Option Pricing Model like its cousin the CAP-M is widely misused
BS Option Pricing Model like its cousin the CAP-M is widely misused
Neither BSOP nor CAP-M were invented to price the risks of tangible engineering projects and other physical activities of commercial nature such as mining, drilling, real estate ventures, REITs, Securitized Toxic Assets, Embedded Bonds, etc.
Model Abuse leads to Modelling Risk!
Model Abuse leads to Modelling Risk!
Net thread Inshallah ->
What is the difference between the value at risk (VaR) and the conditional value at risk (CVaR)?
What is the difference between the value at risk (VaR) and the conditional value at risk (CVaR)?
Value - at - Risk is the purported worst-case loss under normal circumstances/market conditions developed using a computational technique and further specific modelling assumptions tailored as per the risk reporting requirements of a Trading / Investment Desk.
But I tend to disagree with the term WCL - (the Worst Case Loss), as mostly used by some authors in the field of FRM - Financial Risk Management.
How seriously is past volatility a fair estimate of future volatility or risk useful in financial models?
@GARP_Risk
Historical Volatility based on empirical data sample observations.
@GARP_Risk
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
This technique is becoming popular as data science and machine learning advancements are taking place
