If a company does not quantify and periodically monitor and control the risk inherent in its future margins, the economic result at the end of the period may differ from the expected, sometimes with irreversible consequences for the business.
Most companies in the energy and industrial sectors can be affected, in a negative way, in their economic results by the simple random behavior of a significant number of market indices, also known as risk factors, which make up the complex formulas of its margins.
The company must know at any time the level of risk to which the margin of its business is exposed and must be prepared to act on the risk factors that are most negatively affecting the uncertainty of its future results.
Managing the margins in these sectors is a complex task, since they depend on many external factors beyond the control of the company (raw materials, exchange rates, commodities, interest rates, inflation, expected sales volumes, stock, etc.).
Reaching the expected margins depends to a large extent on a correct measurement and dynamic management of the risk existing at each moment.
Prediction and optimization
Predicting future events with a certain degree of accuracy, as well as optimizing day-to-day processes and activities, are common tasks in any company in the energy and industrial sectors.
Planning the future requires making reliable forecasts about what we expect will happen during the next hours, days, weeks, months or even years. Mathematical models based on machine learning techniques are, by definition, approximations of a complex reality, which will help us to propose more objective actions and strategies to carry out.
Among the applications and tools most requested by our customers are:
Prediction models for spot and forward prices with hourly, daily, weekly
and monthly discrimination in different markets and countries.
Prediction models with hourly, daily, weekly and monthly discrimination
for customer consumption, demands, weather variables, as well as hydro, wind
and solar productions, etc.
Classification models to predict the likelihood of customer churn in a
portfolio, the probability of insolvency of potential new customers, the
probability of the sign of the deviation of the electrical system, etc.
Classification or grouping
customers according to different attributes for the design of marketing
strategies, etc.
Optimization tools to
maximize production margins satisfying all the physical and economic
constraints of the system, as well as to find the optimal hedging volume in
asset portfolios that diversify with each other, etc.
Advanced Monte Carlo simulations
(using copulas) for the short, medium and long term of a set of variables
preserving the means, standard deviations, correlations, autocorrelations and
density functions.
Training
We offer in-company training sessions tailored to the
specific needs of each client.
The areas of knowledge are:1. Market and margin risk. 2. Counterparty or credit risk. 3. Financial products and hedging strategies. 4. Classification methods based on machine learning techniques (logistic regression, random forest, neural networks, etc.). 5. Prediction methods based on machine learning techniques (advanced regression, neural networks, time series, etc.). 6. Methods and models of simulation and stochastic optimization. 7. Basic programming in R and Python.
