You can download and read our White Papers & E-books on Commodity Risk Management from here. Our publications cover policy, procedure, processes and best-practices related to Risk Management especially for energy / commodities players. We’ll be glad to have your comments and suggestions on all our publications – you can write to us on email@example.com.
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Profit and Loss Attribution Analysis
P&L Analysis for a Linear and a Non-Linear Portfolio
Most companies with a dynamic portfolio of assets usually analyze their daily / monthly P&L in great detail. One of the most commonly desired analyses is P&L Attribution – which can show the traders a breakdown of various factors which have affected the P&L.
The attribution can give a clear picture of which fundamental factors have impacted the P&L positively or negatively and by how much – and the analysis can be done for any time period. The results can then be checked against trader’s views on each of those fundamental factors before they built / changed the portfolio to its current shape. It not only acts as a check for the traders, but also as a feed-forward mechanism for the teams who can see clear patterns emerging in the underlying fundamentals and their calls on those fundamental factors.
What can you learn from this paper?
This whitepaper on P&L Attribution Analysis aims to identify the factors that contribute to the change in a portfolio’s market value between two time periods. The factors could be market movements, new positions, closure of positions or contract amendments or even corrections.
Since P&L attribution gives insights into the reasons behind a certain change in the Portfolio MTM month-on-month or week-on-week etc., it gives a sort of operational control on the portfolio and also highlights any aberrations (like amendments or corrections) which may have otherwise given a false impression of a huge spike or fall in the MTM.
Predicting Credit Defaults
Using Machine Learning Algorithms
Imagine a Counterparty with a history of default, beginning to slip on its payments again; or a relatively newer Counterparty with steadily increasing exposures beyond company’s comfort levels. You’d want to see red flags on these and other such cases much before it actually hits the reality, right? But today’s systems and processes are just about geared towards generating either pure Bayesian framework based or pure score-card based models that fail to take into account several external factors that often play a large role in predicting these red flags.
Risk Edge has come up with a new whitepaper on using Machine Learning for predicting Credit Defaults, which can also be used for Credit Scoring. Although based on individual data (instead of corporate data), the model is quite similar to the one that can be used to predict Counterparty defaults.
What can you learn from this paper?
The paper shows multiple models that can be used for predicting defaults, along with their results on how they fared in terms of accuracy. The paper not only gives a brief about the models, so as to not give them a “black-box” feel, but also shows how they can be tweaked in multiple ways using advanced Machine learning techniques to improve their accuracy !
- Understanding the factors that affect play a role in predicting defaults and visualizing a couple of them to make sense of the data.
- Which factors are more important than others when it comes to building the model ?
- How to use different models like Decision Trees, Rules, Adaptive Boosting, etc. to build different equations that solve our problem?
- How can some of them be tweaked in a way that allows us to improve the way the model understands our specific problem?
Predicting Crude Oil Prices for 2016
Using Big Data Analytics & Machine Learning Algos
Where are Brent Crude prices headed? This is a question that continues to baffle most of us despite the significant drop in Crude oil price in the last year. Several theories and projections are in place already, some predicting sub-$20 prices through the year 2016, while others expecting a moderate recovery ! Most predictions are based on technical / fundamental analysis of oil and use analyst outlook to further support the projections. This paper uses a unique approach of modelling prices and fundamental data by using Artificial Intelligence / Machine Learning based techniques. It not uncovers the hidden relationships between different variables affecting the oil prices, but also uses the model for predicting Brent crude prices.
How does it work? :
- We used month-end prices and fundamental data over the last 14 years
- Key Fundamentals most likely to be associated with Crude Oil Price Discovery were chosen
- Fundamental data includes Real GDP of OECD & Non-OECD Countries, Crude Production and disruptions in key geographical areas,
- Supply and consumption patterns in major countries, Spare Crude Production capacity and OECD Inventory levels, etc.
- Decision forests with multiple regression techniques were used for building the model
What can you learn from this paper?
The paper is an abridged version of our complete Research paper (not yet released). So while you might have to research on some terms and concepts that we have used (especially if you are not familiar with Machine learning techniques), the paper gives in brief the various stages of a machine learning based prediction model. Thus, we expect this paper to intrigue you, and while it provides you a glimpse into what Big Data Analytics and Machine Learning can do for you, it will also leave you with several interesting questions. And we hope this curiosity will take you down the path of exploring more about this subject !
- Understanding the relationships between several fundamental factors that affect crude oil prices.
- How to visualize relationships between various factors. Should only factors with good correlations be taken into the model?
- Which factors are more important than others when it comes to price discovery of Crude Oil?
- How to combine fundamental and price data to predict crude oil (or any other commodity, for that matter) prices?
Insights: World’s Biggest Risk Management Failures
All of us have read or at least heard of case studies of big organizations incurring huge losses. But the questions surrounding those failures are perhaps bigger than any single event.
Have you ever wondered? :
- Were those failures primarily due to poor Risk Management, or just chance events?
- Which single factor caused most of the failures?
- What did the total losses from various events measure up to?
- What are the lessons for us from these failures?
Besides covering 21 case studies of different Risk Management failures, we analyse what caused those failures. We also analyze the events by industry and time period to derive deeper insights into patterns of these failure events.
Here’s what you’ll read inside:
- Analysis of World’s 21 Biggest Risk Management Failures, by Industry and time
- Case Studies covering each of the failure events very briefly
- 21 Lessons to be learnt from these Failures
- References for further reading
Demystifying Commodity Currency Relationship
The Commodity – Currency Relationship has been a topic of many debates, and it still is ! Several practical questions hound us – Do Commodities drive currencies or is it the other way around? If they do influence each other, how much is the influence? Is there a way to know for sure that one will influence the movements in the other?
In this whitepaper, written purely for practitioners with such concerns, we decode the Interplay between Currencies and Commodities. We use 5 currencies and see how they influence Crude Oil prices. We tell you why correlation is a bad way of measuring this relationship, and how a Multi-variate Linear Regression Model can be used to establish it. We also do Causality tests and Principal Component analysis to back up the results.
What you’ll learn in this Whitepaper:
- How do Uni-variate and Multi-variate Linear Regression models work?
- How do currencies influence Crude Oil prices?
- Do all currencies have more or less similar influence on Crude prices?
- How having a far more diversified Currency portfolio can help with Commodity portfolio risks?
Back-testing Expected Shortfall
Back-testing of Expected Shortfall has received a lot of attention post financial crisis, with organizations realizing that just managing Risks using VaR may not be sufficient. However, lack of proper research and models on using Expected Shortfall as a more “definitive” Risk measure has been a consistent hurdle for companies in adopting Expected Shortfall.
We have used 6 different commodities to test these models and all of them gave encouraging results. The paper ends with a conclusion that if 2 of the models are used in conjunction, they can not only offer a far more definitive protection to an organization from huge draw-downs, but also allow gradual build-up of Risk Capital instead of a huge, bullet payment into the Risk Capital account.
What you’ll learn in this Whitepaper:
- Bootstrap Back-testing results of VaR and Expected Shortfall
- 2 new Back-testing parameters, with models that reach deeper into the fat-tails of distribution
- Results from the 2 Back-testing parameters
- How to implement the Back-testing parameters and avoid Extreme Loss scenario
- An ERR (Excess Risk Ratio) driven model to assess and manage tail-end loss scenarios
Measuring RoI on Risk Management
In one of the World’s first White papers on RoI on Risk Management for Energy / Commodity companies, we discuss the Returns that can be achieved from investments in Risk Management. We present a model to measure RoI on Risk Management that can be easily customized and applied to any company. We also introduce a new Ratio ERR (Excess Risk Ratio) that uses Expected Shortfall (ES) and Value-at-Risk (VaR) to arrive at the Returns from Risk Management. The paper draws upon observations of ERR from different commodities.
To learn more about Expected Shortfall (ES), read our blog post “Commodity Risk Management Beyond VaR“.
We start with putting the problem in context, & understanding why measuring Returns on RM is so difficult. We also look at related / similar studies in other sectors.
Our results indicate that the RoI for Risk Management for Energy / Commodity Companies is in the range of 3.2% – 14% of average Portfolio Value.
How to Optimize Hedging
Hedging in Oil Refining business has been a perennial problem for practitioners. Managers often have to choose between better margins and risk management and are constantly on the look-out for better hedge models to accomplish this feat. Academicians too have pitched in with many models from time to time showing variations / better results than existing models.
Studies have shown that Hedging Costs are often under-estimated by considering only Direct Costs (Brokerage, Bid-Ask Spread). However, Indirect Costs (Cost of Lost Upside, Opportunity cost of Margin Capital) can be far greater. In fact, when considered together, hedging costs can form up to 10% of total costs.
This Whitepaper: How to Optimize Hedging, looks at a new way of optimizing Hedging. While the Hedging model presented here can be adopted for various industries, the paper focuses specifically on applying this model to Oil Refining Businesses.
Stabilizing Monte Carlo VaR Results
This Whitepaper addresses one of the most debated issues in Risk Management – The Stability of Monte-Carlo VaR Results. As Risk practitioners are acutely aware, Monte-Carlo is a preferred method when valuing non-linear, path-dependent instruments including complex derivatives. However, unstable VaR results is a very pervasive issue due to the inherent sampling variation in Monte-Carlo. The assumption made by this model about the stochastic process due to its dependence on random sampling leaves it vulnerable to unstable results. The variance in end results can sometimes be so huge, that it renders Risk Management meaningless for most decision-makers.
Having incorporated the research in this area into our flagship Risk Management software, RiskEdge, we’ve used it to analyze the entire spectrum of simulations by increasing it gradually.
Historical or Implied Volatility – Which one to use for computing VaR?
Many Risk teams still struggle with improving Risk estimation through various techniques. As part of this helping them in this endeavour, we keep coming up with Research on many of these techniques that can help them. One of our earlier whitepapers, Stabilizing Monte-Carlo VaR, dealt with one such topic. This Whitepaper deals with yet another open question for many Risk teams – Implied or Historical Volatility – Which one to use for computing VaR?
Although volatility is accepted as the most critical factors in Risk Measurement, there is generally little consensus on the how to measure volatility or even which volatility to use. In our Knowledge Series Article – Historical vs Implied Volatility, we introduced these 2 different kinds of volatilities and demonstrated how different results they can eventually produce. However, it is still a very moot point for most companies in the industry as to which volatility should be used for Risk Measurement.
The paper examines back-testing results for 2 commodities – Coffee and Sugar using both Volatilities – Historical (EWMA, lambda as 0.94), and Implied Volatility (ATM near month Option).
5 reasons why Spreadsheet based Risk systems wont work
If you are still managing your organization’s Commodity Price Risks from spreadsheets, its time to re-think. This white paper is driven by our research and industry feedback and lists the top 5 reasons against using spreadsheets for risk management.
Benefiting from VaR: Quick Guidelines for Commodity Risk Practitioners
All Management is Risk Management: Douglas Barlow
If there are gaps in your Risk Management Function, you can never use it to benefit the business.
Since Risk Management is critical for all Energy / Commodity companies today, many companies are putting in efforts to bring it into their strategic fold. It is high time you and your risk team took charge to structure your entire risk process in a way that can deliver clear benefits to your business.
And that’s why our consultants and analysts have worked together to bring out an e-book for commodity risk practitioners. Each section in this E-Book is written around a clear business benefit and how to achieve it.
Here’s what you will read inside:
Quick Guidelines to use Risk Management to achieve following Business Benefits:
- Reduce Portfolio Risks
- Increase Turnover
- Reduce Costs, Improve Bottom-line
- Comfort Shareholders
- Cut Losses from Random Shocks
- Get Deeper Business Insights
The Complete Handbook of Commodity Risk Software Requirements
How to achieve the most suited Risk system?
If you are looking for a Commodity Risk System to manage your market and credit risks, you must have a very well-documented set of requirements for the system. This E-Book is the Complete Handbook of Commodity Risk Software Requirements that you’ll ever need. It covers the Commodity Risk Management system requirements that would be suited for most companies, with minor customizations. It is written with an objective to save precious time and effort by companies in drafting such requirements and give them a huge head-start in kicking off this initiative. You can simply refer to the requirements in this document and tweak it a little bit over a couple of days to suit your business, and it’s done
Here’s what you can expect to learn from this E-Book:
- Broad overview of Risk Systems
- Requirements for Risk Measurement – Market Risk and Credit Risk
- How to comply with Regulatory Reporting
- What kind of Risk Reporting and Risk Control should you have
- Risk Analytics requirements
- Technology challenges and how to manage them
- Integration requirements for a stable system
Please note that this E-Book will be made available to people from Energy / Commodity companies only, so please register with your official email id.
Our Top 3 Most Read Blog Posts
Which Blog Posts have most of you loved the most?
We analysed and collated our top 3 blog posts that got most amount of adulation from you, and put it in an easy-to-read, downloadable format of an E-Book so you can re-read it and keep it with you for future reference ! Thank you for reading our blogs and for all your wonderful comments and suggestions – do keep them flowing to us !
As a first Article in our Knowledge Series, we present one of the most critical, but also the least understood concept in Commodity Risk Management – Volatility. In this short article, we tell you how to calculate volatility, the common pitfalls to avoid, and the various methods to calculate it.
Historical vs Implied Volatility
In this part of our Knowledge Series, we present the difference between Historical (Realized) and Implied Volatility. We take a couple of examples of Coffee and Sugar and see how their Historical and Implied volatilities moved over a period of time.