Beta Distribution: the probability of success on any single trial as the random variable, and the number of trials n and the total number of successes in n trials as constants.
For the Binomial Distribution the number of successes X is a random variable and the number of trials N and the probability of success p on any single trial are parameters (i.e. constants).Wednesday, June 15, 2022
Saturday, June 4, 2022
Friday, March 4, 2022
Identifying Outlier in Ratio detection for skewed distribution
Thursday, August 19, 2021
Asymptotic Single Risk Factor Model (ASRF):
Key assumptions: asymptoticity, a single risk factor, and normality.
PD assumptions and violations:Probability of observing D defaults over N (total number of exposures in the credit portfolio) independent random draws follows a binomial distribution.
PD ASRF model Binomial Distribution assumptions:
i. Each asset in the rating grade has default probability P.
ii. Each pair of assets has default correlation ρ
iii. The conditional correlation between any two assets is constant even if the number of defaults increases.
iv. Normal distribution assumption for the systematic factor
ASRF model assumptions may get violated:
Assumptions (i) and (ii):
Let x1, ..,xn be random indicator variables representing the default behavior of the assets where xj =1 indicates the default of asset j. Define as the probability of default of asset j given that assets 1 to j-1 are known to have defaulted.
Assumptions (i) and (ii) imply that:
P1 = P and P2 = P + (1 - P )*ρ
When assets are independent ρ = 0 than these assumptions lead to the Binomial distribution with Pj = P.
However, If ρ > 0, then x1, ..,xn are not independent than the assumption of Binomial distribution is violated.
Assumption (iii):
If the conditional correlation between any two assets increase as the number of defaults increases will lead to increase in default probability.
The increasing default probability given other defaults results in fatter tails of the Correlated Binomial distribution. Contrast assumption (3) with the Binomial distribution where the independence assumption implies that ρj = ρ for all j=1,..,n assets
Assumption (iv):
Systematic factor may follows an autoregressive process.
Thursday, August 5, 2021
Modeling Low Default Portfolio Dependent Case:
Modeling Low Default Portfolio Dependent Case:
VASCIEK MODEL: Dependence between the default is explained by by Vasicek model.
By using conditional probability from the Vasicek model in the case where there are no defaults, the probability of default is the solution of below equations:
Thursday, July 22, 2021
Low Default Portfolio (PD)
Modeling Low Default Portfolio (Independent Default Events):
Pluto and Tasche method for calculating probability of default for portfolios with none or very few observations of defaults.One-sided upper confidence bound as an estimator of PD.
Assumptions:
- n >0 borrowers in the portfolio.
- At the end of the observation period 0≤ d < n defaults are observed among the n borrowers.
- Default events are independent, hence the number of defaults in a portfolio is binomially distributed:
nCr * p^r * ((1-p)^(n-r)
n is the total number of borrowers, r is the total number of defaults and p is the probability of default.
PD to be logical, it should have the following characteristic:
p1 <= p2 <=p3 <=p4..........
It also means that p1=p2=p3=p4=p5...... In this scenario, all the 500 borrowers belong to the same risk characteristic, i.e. homogenous borrowers.
E.g: https://drive.google.com/file/d/1OmGmQV-AsYPsfdRYowSy1bkZArgEFMvN/view?usp=sharing
Friday, May 28, 2021
Monday, March 29, 2021
Stochastic Integration Techniques
Stochastic Integration Techniques:
Generalization of the classical Riemann-Stieltjes integral. Integrals where the integrator is a continuous martingale
Wednesday, January 20, 2021
Brownian Motion:
Brownian motion is the macroscopic picture emerging from a particle moving randomly in d-dimensional space. On the microscopic level, at any time step, the particle receives a random displacement, caused for example by other particles hitting it or by an external force.
A geometric Brownian motion is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.R3 chase - Pursuit
Change Point Detection Time Series
Change Point Detection Methods Kernel Change Point Detection: Kernel change point detection method detects changes in the distribution of ...
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Excel (Macro enable workbook) and VBA code: https://drive.google.com/drive/folders/18tIKLLg8MfJ2MYDjLPAWHspEbApVIpGz?usp=sharing PCA: Eige...
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Modeling Low Default Portfolio (Independent Default Events): Pluto and Tasche method for calculating probability of default for portfolios...
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Modeling Low Default Portfolio Dependent Case: VASCIEK MODEL: Dependence between the default is explained by by Vasicek model. By using co...