Python implementations to perform quantitative risk analysis. The library allows an analyst to represent risks with various statistical distributions, simulate the risks using Monte Carlo methods, analyze the results, and perform sensitivity analysis on the input.
Following an increased intereset in quantiataive risk assessements I have decieded to continue develope this project.
I a currently refactoring and updating the original code. I will also update the documentation to better explain how to use it.
I plan to implment new features after the refactoring. I haven't decided what direction to take it. I am considering makeing it application with GUI. Feel free to open up an issue with ideas for features or ways to make this even better.
The library has been designed to allow for flexibility for the analyst to use distributions, simulations, and analysis as needed.
QRALib
├── Risk Portfolio
├── risk
├── analysis
│ ├── MaRiQ
│ ├── Sensitivity Analysis
│ ├── Single Risk Analysis
│ └── Tornado
├── distribution
│ ├── Beta
│ ├── Lognormal
│ ├── PERT
│ └── Uniform
├── simulation
│ ├── Monte Carlo Simulation
│ ├── Quasi-Monte Carlo Simulation
│ └── Randomized Quasi-Monte Carlo Simulation
└── utils
└── ImporterRecommended usage:
| Distribution | Frequency | Impact |
|---|---|---|
| Uniform | X | |
| Lognormal | X | |
| Beta | X | |
| PERT | X | X |
Functions provided: - The setup of a class object takes 2 positional parameters (3 for PERT). Parameters should be greater than 0 and floats - draw(n) - returns n number of samples from the distribution -draw_ppf(percentile_sequences) - uses the inverse CDF (PPF) to calculate variables based on percentile values, the sequence should be a list of values in the range [0,1)
Uniform distribution should be used to represent risk frequency when limited information is available.
The uniform distribution occurs when a random variable is equally likely to fall between two limits A and B (continues) or equally likely to take any integer value from A to B (discrete). The distribution is useful when there is no information on the range and shape of the random variable.
Uniform distribution takes two parameters 'low' and 'high', which represents the lower and upper bound with a 90% confidence interval. I.e., the numbers provided should cover 90% of all cases. The distribution takes the 'low' and 'high' values and calculates a maximum and minimum value for the uniform distribution. The 'low' value must be smaller or equal to high'. Normally uniform distribution is used with values in the range [0,1], but it can handle any values larger than 0.
Note that if the calculation of maximum and minimum values results in a minimum being less than 0, it will be forced to 0.
The lognormal distribution is skewed to one end, can not generate a zero or negative amount, and has a tail. This makes it useful to represent the probability of loss.
The lognormal distribution takes two parameters 'low' and 'high', which represents the lower and upper bound with a 90% confidence interval. I.e., the numbers provided should cover 90% of all cases. The distribution takes the 'low' and 'high' values and calculates a maximum and minimum value for the lognormal distribution. The 'low' value must be smaller or equal to high'.
This is the standard beta distribution.
The beta distribution can take on many shapes depending on the value of alpha and beta when alpha > 0, beta > 0. The shapes range from exponential, reverse exponential, right triangular, left triangular, skew right, skew left, normal, and bathtub. The Beta distribution is commonly used in Bayesian problems as it allows for providing a prior and calculates the posterior.
Assuming we want to assess the probability of a major data breach. It is possible to start with an uninformed prior where alpha = 1, beta = 1, which gives a uniform distribution. By providing additional data from publicly available data such as industry reports of data breaches, it is possible to update the probability. Alpha can then be viewed as "hits" or companies breached, whereas beta can be viewed as "misses" or companies not breached.
The Beta-PERT distribution(usually referred to as PERT, an acronym for Program Evaluation Research Task) was developed by the U.S Navy in the 1950s for the Polaris Weapon Program. The PERT distribution is a variation of the Beta distribution and is commonly used in project management and risk analysis.
PERT uses three parameters minimum, mean, and maximum anticipated values.
QRALib implements several simulation methods and is designed to be extended with new ones.
Currently, three types of Monte Carlo Methods are supported:
Each simulation method takes a list of risks as input to create the class object. The number of iterations is provided per simulation to allow the analyst to run multiple simulations with a different number of iterations.
Simulation method Recommended number of iterations:
| Simulation method | Recommended number of iterations |
|---|---|
| MCS | 100 000 |
| QMC | 10 000 |
| RQMC | 10 000 |
This applies to all Monte Carlo based simulations.
Standard Monte Carlo Simulation draws random numbers from each risks distributions, computes the various results
Quasi-Monte Carlo uses a low-discrepancy sequence (LDS) instead of random numbers. In this implementation, a Sobol sequence is used. As the LDS is deterministic, it is shuffled to vary the order of the numbers.
Random Quasi-Monte Carlo uses a randomized low-discrepancy sequence (LDS) instead of random numbers. In this implementation, a scrambled Sobol sequence is used.
RQMC should, in theory, provide an error rate that is possible to estimate.
The simulation returns a nested dictionary that contains a summary of the simulation and results for each risk.
The summary states the number of iterations and the risk list used.
The results are a list of dictionaries. Each risk has: - Its ID -frequency: A list of the frequencies from the simulation - occurrences: the result of passing the frequency array to the Poisson distribution (this is the number of times the risk occurred in a given simulation year) - impact: A list of the impacts from the riks impact distribution, it contains the impact for the sum of all occurrences -single_risk_impact: A list of impacts, one for each simulation year. This is used to calculate the ALE in certain analysis methods (multiplying frequency and impact) - total: A list of the impact per simulated year. Each entry is the sum of the number of impacts from that simulation. The number of impacts is based on the 'occurrences'
simulation_result = {
"summary":{
"number_of_iterations": 10000,
"risk_list": <Class Object>,
},
"results":{
"id" : 'R254',
"frequency" : [0.434, 0.123, 0.0678, 0.33],
"occurances" : [2, 0, 1, 0],
"impact" : [4502, 9543, 23895, 10235],
"single_risk_impact": [8041, 7422, 98723, 3212],
"total" : [14045 , 0, 23895, 0]
}
} The MaRiQ model has been implemented as an example of analysis that can be done. MaRiQ provides two analysis modes; Total Risks and Single Risk.
Source: The MaRiQ model: A quantitative approach to risk management
The Single Risk Analysis (SRA) function helps the analyst dive into a single risk and understand its distribution and result.
Setup the SRA by providing the simulation results dictionary from the simulation method used.
Usage: call the object and function with the number of the risk that is being analyzed.
The function returns: - a table with the minimum, 5th percentile, mean, 95th percentile and max value for impact and frequency. - Boxplot of the frequency and impact - An impact exceedance curve for the total risk impact
Sensitivity analysis can determine which input variables affect the output the most or verify interaction effects within the model. This can help to understand and verify the model or simplify and prioritize factors that affect the model the least and most, respectively.
QRALib implements three different Sensitivity Analysis methods:
The Tornado Chart uses a One-at-a-time (OAT) Sampling method. It goes through each risk and calculates the impact each risk has to the average by changing between the 5th percentile value and 95th percentile value. The result is calculated and presented in a Tornado chart with the risk that has the highest impact on top. The left and right bars show the positive and negative impact it has. Using this shows which risk has the most variation in its impact.
The Tornado Chart can be used to look at the variation each risk has on the total impact and also see the variation of each risk frequency and single risk impact on the ALE.
The method of Morris is an OAT method developed in 1991 by Max D. Morris. The method uses two sensitivity measures to classify the inputs in three groups:
The implementation takes the number of samples as an input (1000 is recommended). It outputs a graph and table showing the two measures mu_star and sigma.
Sobol' indices are a variance-based method that is capable of computing sensitivity for arbitrary groups of factors. Sobol's method compute three indices, first-order sensitivity index S_1, second-order sensitivity index S_2, and total sensitivity index S_T. The current implementation in QRALib does not implement second-order sensitivity index S_2.
The first-order index (S_1) shows the effect of each input on the total. The first-order index is sometimes called 'importance measure' or 'correlation ratio'.
The total sensitivity index (S_T) measures the contribution to the output variance of Xi, including all variance caused by its interactions, of any order, with any other input variables.
QRALib provides utilities to help with certain tasks. Currently, it implements the tools to import data from a CSV or nested JSON.
Examples are provided in the example folder.
The library provides the functionality for an analyst to import, simulate, and analyze data. The analyst has to combine the necessary parts.
Steps: 1. Import necessary classes from the library 2. Setup parameters, 'number of iterations' is always needed - In this example, 'risk_tolerance' is added for the MaRiQ analysis 3. Import the data, either from JSON or CSV, then generate the list of risks to simulate 4. Run simulation 5. Run analysis 6. Run sensitivity analysis
from QRALib.riskportfolio import RiskPortfolio as Risks
from QRALib.simulation.mcs import MonteCarloSimulation as mcs
from QRALib.analysis.mariq import MaRiQ as mariq
from QRALib.analysis.sa import SensitivityAnalysis as sensitivity_analysis
from QRALib.utils.importer import Importer as importer
# Setup parameters
number_of_iterations = 10000
risk_tolerance = ([0, 600000, 1000000, 1500000, 3000000], [100, 90, 50, 20, 0])
# Import data
risk_dictionary = importer.import_csv(inp_csv)
risk_list = Risks(risk_dictionary)
# Run simulation
simulation = mcs(risk_list)
risk_results = simulation.simulation(number_of_iterations)
# Run analysis
analysis = mariq(risk_results1, tolerance)
analysis.total_risk_analysis()
analysis.single_risk_analysis()
# Run sensitivity analysis
sa = sensitivity_analysis(risk_list)
morris = sa.impact_morris(1000)