Quantitative Python
Risk management with PythonBy Rafael Ballestas  April 09, 2019  Category: Philosophy
Now that we have an understanding of risk concepts
such as the loss exceedance curve,
valueatrisk, Bayes Rule,
and fitting distributions,
we would like to have a realiable, extensible and preferably open tool
to perform these computations.
In the background, we have used a spreadsheet, which is hard to extend.
We have used GNU Octave,
which is good, but not a proper programming language.
Our favorite language at Fluid Attacks, Python
,
has modules for
statistics,
scientific computation and even
finance itself.
Let’s take it for a spin around this risky neighborhood.
Python
has a whole ecosystem
for numerical computing (v.g. Numpy
) and
data analysis (Pandas
)
and is well on its way to becoming a standard in
Open Science.
Being a free and open source tool,
there are also many derived projects which make life easier
when coding, such as the Jupyter
Notebook,
which allows us to selectively run code snippets,
much like in commercial packages such as
Matlab
and
Mathematica
.
This enables and encourages, at least, initial exploration,
although it might not be the best fit for developing more involved code.
Let us see how we can automate the generation of a loss exceedance curve (LEC
)
via Monte Carlo simulation.
Here we will closely follow our
article on the subject,
so as not to duplicate information.
In that article, we wanted to find a distribution for losses
based on expert estimations of occurrence likelihood
and confidence intervals for the impact:
So we need to read those values in our script.
Since this is tabular information of the kind that would be
useful to view, say, in a spreadsheet,
it will be convenient to read this into a Pandas
dataframe:
import pandas as pd
events_basic = pd.read_csv('events.csv')
events_basic.head()
We declare an event happens if a number taken at random is beyond a certain threshold, given by the second column in the table above:
def event_happens(occurrence_probability):
return np.random.rand() < occurrence_probability
If and when the event happens, we next need to know the extent of the loss due to this single event. Recall that we modeled this with a lognormal variable, whose parameters we got from the estimated confidence interval:
def lognormal_event_result(lower, upper):
mean = (np.log(upper) + np.log(lower))/2.0
stdv = (np.log(upper)  np.log(lower))/3.29
return np.random.lognormal(mean, stdv)
All of the events in the above table can happen in a single year, so to simulate a scenario, we need to find out, for each of them, if they happen, and how much money they will cost us. Finally, we add all the losses and return that single number as a summary of the losses in a simulated year:
def simulate_scenario(events):
total_loss = 0
for _, event in events.iterrows():
if event_happens(event['Probability']):
total_loss += lognormal_event_result(event['Lower'],event['Upper'])
return total_loss
Now, the crucial step in MonteCarlo simulation
is to simulate many scenarios and record those results.
Let us write a function that does just this,
returning the results in a basic Python
list,
which we could later turn,
if we so wished,
into a Pandas
or Numpy
native structure
for statistical analysis.
The function takes as input the number of times
we want to simulate scenarios:
def monte_carlo(events, rounds):
list_losses = []
for i in range(rounds):
loss_result = simulate_scenario(events)
list_losses.append(loss_result)
return list_losses
Going graphic
Just to get a feeling for the results,
let us run a thousand scenarios and
plot them, that is,
the result of each simulated year,
in the order in which they were obtained.
As foretold, we could convert the results
into a Pandas
DataSeries
, if anything,
to illustrate how they work.
We also need to import Matplotlib
for visualization:
import matplotlib.pyplot as plt
results = monte_carlo(events_basic, 1000)
results_series = pd.Series(results)
results_series.plot()
It can be observed that the vast majority of them are in the fringe between 0 and 15 million. But it is not infeasible to have results that are way beyond the central interval. In order to rule out what’s simple chance and what is really happening due to the distribution of loss, we can simply run more scenarios. Tens or hundreds of thousands of scenarios is a good rule of thumb, without sacrificing performance. A thousand runs takes around 5 seconds, and 10000 takes around 50. At some point adding more simulations does not necessarily improve the quality of results. Your mileage may vary.
No matter the number of scenarios,
the results are not as useful as they could be
until we aggregate them, v.g., in a histogram.
Pandas
also provides a shorthand for this:
results_series.hist(bins = 15)
We’re getting closer to the loss exceedance curve, but not there yet.
We can estimate probabilities simply by counting occurrences
and normalizing by dividing by the number of rounds and multiplying by 100.
Hence the estimated "probability" of a single value
is the normalized number of times that value appeared in the simulation.
So let us take evenly spaced values,
and count the number of times each of those values is exceeded (or matched).
The numpy
function cumsum
does just that,
except in the opposite order:
it adds the values seen up to a moment.
So if we take the intervals and the counts separately,
revert the counts list and then do cumsum
on it,
we get what we need, in reverse order.
To fix that we simply revert again:
import numpy as np
result_nparray = np.array(results_list)
hist, edges = np.histogram(results_nparray, bins = 40)
cumrev = np.cumsum(hist[::1])[::1]*100/len(results_nparray)
plt.plot(edges[:1], cumrev)
And voilà, we get our loss exceedance curve as we sought:
We can repeat the procedure with a more moderate dataset
to obtain the inherent risk LEC
,
in the sense that the probabilities and impact CIs are lower.
And finally, to obtain the risk tolerance curve,
we give a few points obtained from interviewing someone in charge,
as described in the original article
and fitting a curve to it using SciPy’s
Interpolation functions:
from scipy import interpolate
xs = np.array([1,2,3,7,9])*(1e6)
tols = np.array([100,60,10,2,1])
xint = np.linspace(min(xs), max(xs))
fit = interpolate.interp1d(xs, tols, kind='slinear')
plt.plot(xint, fit(xint))
All together in a single plot:
Risk measures
Now obtaining the 5% value at risk is simply a matter
of asking for the 95^{th} percentile of
the "distribution", i.e., the actual simulation results,
in its Numpy
array incarnation:
>>> np.percentile(results_nparray, 95)
23360441.53826834
Hence the VaR
, according to this particular simulation
is a little over $23 million.
It is just as simple to obtain the
tail value at risk.
If we had a mathematical function for the distribution
we would have to compute an integral in order to obtain it,
but since what we have is a discrete approximation to it,
i.e., a simple table of values,
we can just average the values that are under the VaR
:
>>> np.average(results_nparray[results_nparray >= var])
31949559.99328234
Thus in case of a VaR
breach,
we can expect the loss to be of little less than $32 million.
Let us simulate the input values for the simulation,
as if we were running the simulation every day
with different occurrence probabilities and impacts.
Let us make up a DataFrame
with random values for the inputs:
def gen_random_events():
probability_column = np.random.random_sample(30)*0.1
lower_ci_column = np.random.random_sample(30)*(1e6)
upper_ci_column = np.random.random_sample(30)*(9e6)+1e6
dicc = {'Probability' : probability_column,
'Lower' : lower_ci_column,
'Upper': upper_ci_column}
events_rand = pd.DataFrame(dicc)
return events_rand
Next we run MonteCarlo on those,
once for each day of a fictitious month,
compute the VaR
and tVaR
for each day
and observe how they evolve:
Since this was a madeup example and
the probabilities are sampled simply,
i.e., from a uniform distribution,
the results are, well, uniform.
However for the sake of conclusion,
we can imagine there is a steady, if slow,
trend towards raising the VaR
.
It is interesting that the highest peak in tVaR
corresponds to a VaR
that is not that different from its neighbors.
This goes to show that one is not just a simple function of the other,
which is often the case in dealing with uncertainty.
References

C. DavidsonPilon (2019). Probabilistic Programming and Bayesian Methods for Hackers.

C. Motiff (2019). Monte Carlo Simulation with Python

B. Mikulski (2018). Monte Carlo simulation in Python
Appendix: Full script
Download Python script or as Jupyter notebook and input data for inherent risk and residual risk.
quant.py
link:quant.py[]