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Quantitative Python

by Rafael Ballestas on April 8, 2019

Now that we have an understanding of risk
concepts such as the loss exceedance
curve,
value-at-risk, 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:

Table with input data

Figure 1. Table with input data.

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:

Importing pandas and reading data.

import pandas as pdevents_basic = pd.read_csv('events.csv')events_basic.head()

Imported data

Figure 2. Data as imported into Jupyter like in our Monte Carlo
article.

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 Monte-Carlo 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 pltresults = monte_carlo(events_basic, 1000)results_series = pd.Series(results)results_series.plot()

Raw Monte-Carlo results

Figure 3. Raw Monte-Carlo results

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)

Histogram of results

Figure 4. Histogram of results

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 npresult_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:

Simple loss exceedance curve

Figure 5. Simple loss exceedance curve like in our Monte Carlo
article.

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 interpolatexs = 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 curves together

Figure 6. Loss exceedance curves like in our Monte Carlo
article.

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 Monte-Carlo on those, once for each day of a fictitious
month, compute the VaR and tVaR for each day and observe how they
evolve:

Evolution of daily VaR in month

Figure 7. Fabricated VaR monitoring example like in our VaR
article.

Since this was a made-up 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.