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Financial risk consultants – Strategy @ Risk

Strategy @ Risk

Tag: Financial risk consultants

  • Two letters

    Two letters

    Dear S@R,

    I am not interested in the use of stochastic models, and particularly Monte Carlo simulations.  I believe that these approaches too often lead to underestimating risks of extreme events, by failing to indentify correlated variables, first order or second order variables, and correlations in sample populations. I believe that the use of these models carries an important responsibility in the way banks failed to address risks correctly.
    Best regards,
    NN

    Dear NN,

    We wholeheartedly agree on the errors you point out, especially for the banking sector. However this is per se not the fault of Monte Carlo simulation as a technique, but in the way some models has been implemented and later misused.

    We also have read the stories about bank risk managers (and modellers) forced by higher management to change important risk parameters to make further loans possible.

    We just do not relay only on normal variables with short slim tails and simple VaR calculations. For risk calculations we alternatively use shortfall and spectral risk, the latter to give progressively larger weights to losses that can be disastrous. This will be a topic in a future post on our Web site.

    However I beg to differ with you on the question of correlations. In my experience large correlation matrixs is a part of the problem you describe. Such correlation matrixs will undoubtedly contain spurious correlations giving false estimates of important relations. This is why we model all important relations, using the unexplained variance as a part of the uncertainty describing the problem under study – the company’s operations.

    Many claim that what killed Wall Street was uncritical use of David X. Li’s copula formula, where errors massively increase the risk of the whole equation blowing up (Salmon, 2009). We have therefore never used his work, relaying more on both B. Mandelbrot and Taleb Nasim’s views.

    As we se it, the use of copula’s formua was done to avoid serious statistical analysis and simulation work – which is what we do.

    If you should reconsider, we will be happy to meet with you to explain the nature of our work. To us nothing is better than a demanding customer.

    Best regards

    S@R

    References

    Salmon, Felix (2009,02,23). Recipe for Disaster: The Formula That Killed Wall Street. Wired Magazine, Retrieved 0702,2009, from http://www.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all

  • What we do; Predictive and Prescriptive Analytics

    What we do; Predictive and Prescriptive Analytics

    This entry is part 1 of 3 in the series What We Do

     

    Analytics is the discovery and communication of meaningful patterns in data. It is especially valuable in areas rich with recorded information – as in all economic activities. Analytics relies on the simultaneous application of statistical methods, simulation modeling and operations research to quantify performance.

    Prescriptive analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.

    Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.

    Prescriptive analytics will also tell what probably will happen, but in addition:  when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.

    By employing simulation modeling (Monte Carlo methods) we can give answers – by probability statements – to the critical question at the top of the value staircase.

     

    Prescriptive-analytics

     

    This feature is a basic element of the S@R balance simulation model, where the Monte Carlo simulation can be stopped at any point on the probability distribution for company value  (i.e. very high or very low value of company) giving full set of reports: P&L and balance sheet etc. – enabling a full postmortem analysis: what it was that happened and why it did happen.

    Different courses of actions to repeat or avoid the result with high probability can then be researched and assessed. The EBITDA client specific model will capture relationships among many factors to allow simultaneous assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Even the language we use to write the models are specially developed for making decision support systems.

    Our methods will as well include data and information visualization to clearly and effectively communicate both information and acquired knowledge – to reinforce comprehension and cognition.

    Firms may thus fruitfully apply analytics to business data, to describe, predict, and improve its business performance.

     

  • Projects we have done

    Projects we have done

    Consultancy, in contrast to selling software products, is quite a delicate process. Trust is the most important asset to successfully completing a project, and S@R customers consider discretion to be important. That’s why we have decided to publish relevant contents – which provide insight into our methods of operation – only accessible in anonymous form and often collected from different projects.

    The same applies to naming customers, but large projects has been performed in

    • Finance
    • Banking
    • Pulp & Paper
    • Airport Operations
    • Brewery
    • Aquaculture
    • Mining & Quarrying
    • Car parts
    • Rail coach production

    etc. –  all for multinational companies.

  • Understanding risk, creating value

    Understanding risk, creating value

    We specialize in the positive development of reputations resulting from apposite strategic decision making. We accomplish this through understanding, assessing, managing and financial modelling of corporate risk; ensuring that the best strategic decisions are made. Our approach is holistic and thorough; we focus on quantifying and mitigating downside risk and taking advantage of upside risk opportunities.

    RISK AND OPPORTUNITY ARE EVIDENT IN ALL STRATEGIES

    They embrace and influence almost every organisational strategy e.g. mergers and acquisitions, initial public offerings, investment in new plant, new products, new technologies and new market-places as well as training and skills programmes, labour relations; together with issues relating to purchasing, outsourcing, energy, the physical environment and current / future political dynamics.

    STRATEGIC DECISIONS ARE THE BUILDING BLOCKS OF REPUTATION

    Beyond organisational reputation: the identification and quantification of corporate risk, and strategies for its effective management and mitigation, are becoming required formal statements in corporate announcements in the wake of existing and forthcoming legislation in the US, Europe and elsewhere.
    Working in tune with our clients Strategy@Risk Ltd contributes to the delivery of the right strategic decision(s) i.e. allocating capital and other resources to the highest reward projects adjusted for risk including.

    We develop and implement enterprise-wide risk management programmes establishing ‘best practice’ frameworks dealing with the totality of risks faced by clients.

    Holistically approached and using our expert valuation and unique financial modelling systems we focus upon risk opportunities and rewards and appropriate mitigation strategies together with the effects that uncertainty and changing variables have upon three crucial areas of business:

    • Internal environment – resources, assets, people, skills, culture, systems etc.
    • Market Place – customers, segments, materials, technologies, logistics, substitutes etc.
    • Operating Environment – economic growth, political dynamics, social and demographic changes, regulation and the eco-environment.