Retirement Planning

Fatter Tails

Thursday, May 7, 2009 by Holly Bailey

At the height of panic--and consternation--over turmoil in the financial sector at the turn of the year, many an accusatory finger was pointed at the risk analysis models the finance industry used to establish the value of various types of debts. Often the particular charge was that the simulations produced by Monte Carlo software lacked not only precision but even the capacity for precision. At the time, I responded in this blog that a risk assessment model is only as reliable as the probabilities it is build on.

Now some folks in financial planning firms whose customers experienced the unhappy results of models created with their companies' proprietary Monte Carlo software are becoming believers. They are revisiting the probability distributions that generated those risk simulations, and in comments to the press are citing the need for distributions with fatter tails in order to account for randomness over longer time periods, in order to foresee a Black Swan event. This is no doubt due to the sudden prominence of author Nasim Nicholas Taleb, whose recent fame is due to his timely second-guessing of the markets.

That people who work in finance and investing should start listening to a critic only after coming to grief is not surprising. What I do find surprising, however, is the planners who mention to reporters that they have been limited to the standard bell curve distribution or that their software doesn't provide for sensitivity analyses. Any number of commercially available Monte Carlo software packages have been offering a fairly wide choice of distributions, along with sensitivity analyses for quite a while. Fatter tails aren't hard to come by, so why can't planners seem to find them?

The Trials of Trials

Friday, May 1, 2009 by Holly Bailey

In my last blog I mentioned there has been a dramatic upswing in the use of risk analysis and Monte Carlo software in clinical trials for new drugs. A new unpublished paper by Todd Clark of VOI Consulting makes clear some of the reasons more people in the pharmaceutical industry are turning to operational risk software to guide them in setting up trials.

First of all, a clinical trial is probably not one trial but a process involving a series of trials, each of which takes a number of years and millions of dollars to complete. This process takes place before the company even presents the drug to the FDA for approval. Then, as the U.S. Government Accountability Office, points out, the FDA eventually approves only 1 in 10,000 compounds a safe and effective. No wonder--again according to the GAO--"the number of new drugs being produced has generally declined while research and development expenses have been steadily increasing."

Although there are enormous profits to be made if a drug developed for a large number of patients is approved, there are great sums of money to be lost and many tricky decisions to be evaluated along the way to successful product strategies. As Clark points out, even the planning of a single clinical trial is itself fraught with uncertainty: How many subjects? What kind of subjects? What kinds of physicians? Where to hold the trials? And the answer to each of these questions is in turn a balancing out of numerous variables.

So there's plenty of risk to go around. But potentially plenty of reward. Just made for risk assessment with Monte Carlo.

Modeling in Process Development - Deterministic or Stochastic?

Tuesday, April 21, 2009 by Steve Hunt

Deterministic" and "stochastic" sound like fancy, academic words, but they are vital for everyone in business to understand. Deterministic refers to single numbers, while stochastic refers to probabilities. When modeling a system, a deterministic model uses numerical input values and calculates numerical output values. But a stochastic model uses a probability distribution for each input and estimates a probability distribution for each output. Monte Carlo simulation (@RISK) is the standard tool for stochastic modeling. During Monte Carlo simulation, random values are generated for inputs, and output values are calculated. By repeating this process many times and saving output values, the simulation generates estimates for output distributions.

Stochastic modeling and Monte Carlo simulation have become accepted tools in financial professions. However, engineers working on the development of new products have been slow to adopt these important tools, opting instead for deterministic approaches such as worst-case analysis. These deterministic tools answer some questions, but they are unable to predict process capability, which is the key to success in a Six Sigma environment. Monte Carlo simulation is now an essential tool for every engineer involved in product and process development. Stochastic tools can predict and prevent performance and capability problems before the first prototype is built. What once required months or years to discover now takes only minutes to prevent.

On April 30th, Andrew Sleeper of Successful Statistics will be presenting Accelerating Product Design with Simulation and Stochastic Optimization. Plan to spend hour to learn how to save months in your product and process development projects. It will be time well spent. You can register to attend at www.palisade.com/seminars/webcasts.asp

Social Influence and Network Exploitation

Sunday, April 19, 2009 by Holly Bailey

It was bound to happen. Online communities such as FaceBook and Twitter, which are themselves commercial animals, are being mined by all kinds of enterprises from ad agencies to credit card companies for the commercially valuable data they can yield. A recent opinion piece in the Manchester Evening News rounds up a fair number of potential uses of this socially generated data and tries to sort out the good from the not so good, and the bad from the truly ugly.

According to the Evening News's Paul Taylor, businesses are using social networking sites for everything from checking out individual job applicants to statistical analysis of myriad purchasing decisions with neural network technology. On of the worrisome scenarios he highlights is the probable effect of upcoming legislation by Parliament that would require law enforcement agencies to keep records of web traffic. Another is the move by Google to obtain customers' permission to let Google use cell phone software to keep track of their whereabouts and apply its operation research magic to turn the information it acquires this way into marketable fact. But he balances these possibilities with other brighter ones--such as helping doctors do better risk assessment in creating treatment plans.

Falling somewhere in the space between sinister and beneficial is the use of social networking data for marketing. About the same time as Paul Taylor's opinion piece was published, a marketer's blog for the auto industry laid out the conceptual framework of a strategy based on online communities that it has trademarked as "Social Influence Marketing." A component of any campaign as essential, it claims, as direct marketing and branding.

At the moment, all of this should mean more to you if you are young, because, at the moment, the young are the people who are most attracted to social networks. And they are the ones who will immediately see the utility of network data for marketing and product strategies. But if, as they say, youth is only a state of mind, it won't be long before the rest of us catch up and catch on as the social network and its exploitation evolve.

Is Norway’s Pension Fund Adequately Diversified? Part II

Tuesday, April 7, 2009 by DMUU Training Team

In an earlier blog I allowed myself some raw speculation as to whether holistic risk management thinking is being adequately applied when it comes to the Norwegian government’s management of the state pension fund. This fund represents one of the world’s largest exercises in risk analysis in “retirement planning.” The Fund invests the oil wealth generated in the country in a mix of global equities and % bonds, and whose performance is essentially currently measured against a global benchmark portfolio of bonds and equities.

I specifically asked the question as to whether the fund should be devoting far more significant efforts to invest in non-traditional assets, as a way to mitigate some potential scenarios that could adversely affect both equity and bond investments. Investments that could potentially mitigate some of these scenarios could perhaps include very large positions in alternative energy technologies, and I noted however that although the costs and risks of such an investment strategy could be large (particularly as the scenario which it mitigates may never materialize), it could nevertheless be a prudent one, given the already very large fund that already exists for a small population base of about 5m people. Could it be so bad if 5%-10% of that fund were invested in such technologies (with such investments arguably supporting some of the fund’s other goals – such as ethical or social investments)?

It is therefore with interest that the Financial Times reported last Saturday that the Norwegian government is planning to review the operations of its sovereign wealth fund after it lost €75bn ($100bn, £68bn) on investments last year. I await eagerly the results of this review, specifically of course to see whether such fundamentally new investment strategies will be implemented.

Dr. Michael Rees
Director of Training and Consulting

Best Practices in Risk Modelling

Wednesday, April 1, 2009 by DMUU Training Team

The recent blog positing on best practices in Excel modelling could be thought of as providing a reasonable and robust set of principles for building static Excel models. When building simulation models for risk analysis in Excel (for instance, with @RISK Monte Carlo software), some other points are worthy of consideration:

A risk model may need to be built at an appropriate level of detail. A model which is too detailed will be more complex to add risk distributions to and will require more effort to capture the dependencies between the larges number of variables. In many practical cases, key dependencies will simply not be captured, and the result will have an excessively narrow range (for additive type models e.g. cost budgeting) or an excessively wide range (for subtractive models e.g. profit as the difference between uncertain revenues and costs).
The inclusion or not of event risks. Generically, a static model of a situation in which there are event risks (e.g. something adverse happening in 20% of cases in a reserve estimation model) would not include such a risk as a line item (since the most likely outcome is its non-occurrence), whereas a risk model would.
The prioritisation of event risks to include may be non trivial, and depend on the decision maker's risk profile (i.e. tolerance and decision-making criteria), as well as the potential total number of event risks under consideration. For example, in a retirement planning model where a decision is to be based on the P90 outcome (i.e. the worst or best 10% of cases) it would be more important to include an event with an impact of 1 (with 100% probability) than an event with impact of 100 with 1% probability, as this latter event in isolation would have very little effect on the P90 of the output distribution (Were decisions to be made on the P99.5 value, we would have a different situation, of course.).
The use of DataTables will generally slow down simulation models, as the tables need to be recalculated at every iteration. DataTables may be used when building to model and as an error-checking tool (TopRank may also be used, to check that errors are zero across a range of scenarios), but may need to be removed before running the final simulation model.
The real challenges in risk modelling boil down to those related to model formulation and decision-making; that is: the selection of variables, capturing the true dynamic of the situation in the model, choice of distributions, capturing dependencies correctly etc. (so come to a training course!)
Other principles of model implementation (as discussed in the earlier blog) are essentially identical.

Dr. Michael Rees
Director of Training and Consulting

Real World Gamesmanship

Monday, March 30, 2009 by Holly Bailey

A few years ago, the online magazine for computer gaming Gamesutra published an article by a game developer Alan Carpenter extolling the virtues of using risk analysis to balance role-playing games. In this case balance meant designing a game that was neither too difficult nor too easy, and it was the costly, time-consuming goal of game development companies--costly meaning an average of $3 million a title!

Carpenter had observed that by using the same operational risk software that the oil and gas industry uses to make decisions under uncertainty, a game designer could take advantage of the thousands of possible scenarios spun out by a Monte Carlo simulation to add a new element of reality to games where context and emotion may be big draws for the gamer but can't sustain entertaining play.

Carpenter advises game developers that many games could be designed using the same Monte Carlo Excel spreadsheet. He offers a lengthy technical blueprint for games that are based on conflict--war, street fights, etc.--and in revisiting it what intrigues me is the idea that the same infrastructure of algorithms and probability functions that Carpenter lays out to capture events in an imaginary world could just as easily be applied to real-world political and military events.

If risk simulation is being used to help plan world events, it's not widely talked about. But I suspect this is going on, and I would love to hear from anyone out there who knows more about this than I do.

Advanced Analytics for Business Intelligence

Thursday, March 12, 2009 by DMUU Training Team

Business Intelligence (BI) is all the rage. Businesses want business intelligence. Analytics and reports are at the heart of BI. Decision makers want decision intelligence. Analysis, especially quantitative risk analysis and Monte Carlo simulation, yields more thorough intelligence for effective decision making under uncertainty.

Some, Ralph Kimball among them, challenge that advanced analytical tools, “as powerful as they are, can be understood and used effectively only by a small percentage of the potential … business-user population.” What’s missing from the assertion is a recognition that data are about what has already happened. If you’re forecasting or planning strategically, you need predictions moving forward. It’s not just about data mining, it’s how you employ the data to make effective and well-informed decisions.
According to one BI developer, “use of advanced analytics has been limited to power analysts” leaving reporting capabilities to the bottom of the technology pyramid. But risk and decision analysis tools from Palisade Corporation are not just for power analysts — anyone can make ready use of these Monte Carlo software tools. Besides, several large corporations make @RISK accessible to business and production sides in the organization.

@RISK, and each of the DecisionTools Suite applications, has built-in reporting capabilities to communicate the analyses you’ve performed. In @RISK 5, you can even have your reports generated immediately after completing a simulation (see the Simulation Settings dialog), whether those reports are in standard form and layout, or as customized templates. Any statistic you can find from a data set can be reported in easy to use Excel spreadsheets. The quants can utilize the reports to communicate the risk analytics of a strategic decision as well as those on the front lines who need to generate a quick assessment of uncertainty in operational actions.

Thompson Terry
Palisade Training Team

Recalculating the Calculators

Thursday, March 12, 2009 by Holly Bailey

In an article this week in the online publication TheStreet, Taylor Smith takes aim at online calculators for retirement planning. He says the problem with them is that most of them are based on Monte Carlo software, and then goes on to make some broad and inaccurate statements about the characteristics about Monte Carlo simulation and the ways it skews risk assessment.

For starters, he quotes an investment "expert," who is, coincidentally, president of concern that produces risk analysis software not driven by Monte Carlo software, to the effect that good financial planning should take into account, more than market returns--including taxes, income, and expenses. This implies the Monte Carlo simulation is not capable of factoring these elements into its predictions. Nothing, as the a recent risk analysis model offered by both the Society of Actuaries and the Casualty Society aptly demonstrates, could be farther from the truth. This model, developed by professors of finance and mathematics at Illinois State University and the University of Illinois Champaign-Urbana, helps pension and insurance planners to to forecast not only projected market returns but the effects such critical economic factors as interest rates, equity price levels, inflation and unemployment rates, and real estate prices.

My point is that a Monte Carlo simulation is what you make it. It can be very simple and limited to one or two economic factors, or it can be a complex mix of many factors. If the online retirement calculators are too simplistic to usefully account for reality, their builders have plenty of room to improve them.

Lost at Sea

Monday, March 9, 2009 by Holly Bailey

Last week, when the U.S. Coast Guard called off its search for three men, including two NFL football players, who were pitched into the Gulf of Mexico when their fishing boat capsized, its spokesman commented, "We're extremely confident that if there are any survivors on the surface of the water that we would have found them."

Following up on this, Scientific American interviewed an oceanographer in the Coast Guard's Office Search and Rescue, to ask about the source of this confidence. It turns out that the Coast Guard uses Monte Carlo software to plan every search operation. It's the basis of the system they call SAROPS (Search and Rescue Optimal Planning System). What SAROPS simulates is the drift trajectory for search targets--people, vessels. Based on historical data on drift patterns for similar objects, it projects drift scenarios for various starting locations and times. At the same time, it factors in an environmental risk analysis of wind and current.

The models produce tightly defined options for search patterns, which save search time and increase the likelihood of finding castaways while they are still alive. But, as the Coast Guard oceanographer pointed out about the case of the missing fishermen, "There’s always uncertainty, of course, which is why we’re having a search in the first place."

A Good Read on a Bad Year

Monday, March 2, 2009 by Holly Bailey

Warren Buffet did not have a good year. This fact about 2008 was detailed last week in a New York Times article and in a letter from Mr. Buffet to stockholders of his Berkshire Hathaway company. This letter offers plenty of plain-spoken criticism of the risk assessment failures in the financial sector to go around--'beware of geeks bearing formulas."

But he reserves his harshest criticisms for derivatives--"weapons of mass destruction.". He not only cites the hazards of using risk analysis models to value these complexly structured investment products, but he points out something I haven't seen mentioned before: derivatives create a "web of mutual dependence" among financial institutions that lingers for years.

About this web of dependence, he says, “Participants seeking to dodge troubles face the same problem as someone seeking to avoid venereal disease,” he wrote. “It’s not just whom you sleep with, but also whom they are sleeping with.”

I recommend Warren Buffet's letter if only for its Olympian view of the exponential decline in the credit markets and his list of the intriguing entertainments he has planned for his shareholders at their annual meeting this May. His bad year makes for some good reading.

Palisade Corporation Exhibiting at the ASQ Lean Six Sigma Conference

Monday, February 23, 2009 by Steve Hunt

During these deteriorating economic times, it is more important than ever for organizations to be more vigilant about cutting costs and boosting the bottom line. An option is to instill a Lean and Lean Six Sigma culture when tackling projects to save money. The American Society for Quality (ASQ) is once again hosting the 2009 Lean Six Sigma Conference, March 2–3, 2009, in Phoenix, Ariz., to teach professionals needed skills to take back to their organizations.

Palisade will be on hand to demonstrate the use of Monte Carlo Simulation and to explain the benefits of utilizing @RISK to save time and money in your Lean Six Sigma and Design for Six Sigma projects. If you are planning to attend, please make time to come by to pick up a free trial CD of @RISK and to say hello.

Despite the current state of our economy it appears there will be an excellent turnout for this event.

Because Palisade Corporation will be exhibiting, our customers can save 50% on their second registration for the conference. Call ASQ Customer Care at 800-248-1946* and use priority code CEJDB69 to take advantage of this great savings opportunity and to start making a difference in your organization and career today! They will fill you in on the rest of the details when you call before Feb 25.

Hope to see you there!

R.I.P. All Over Again

Tuesday, February 17, 2009 by Holly Bailey

An item from the Department of More Things Change, the More They Stay the Same.

Last week, speaking at a conference on managing retirement income, an executive with a U.S. division of Deutsche Bank announced that with the "failure" of diversified investing strategies, Modern Portfolio Theory was dead. R.I.P. balanced portfolios. R.I.P. the Nobel Prize-winning work of Harry Markowitz. R.I.P. Monte Carlo simulation projections.

Instead, announced Phillip Hensler, "Advisors who offer predictability will prevail"-- isn't predictability the goal of all those portfolio managers who rely on statistical analysis techniques for risk assessment? And he foresees that we will enter a new era of "Outcome Driven Investing"--isn't outcome what drives all investment activity?

In this new era financial planners will help their clients match their "health risks, market risks, and longevity risks with specific guaranteed and non-guaranteed" investment products. Two questions: What else have financial planners been doing for the past decade? And just how are they going to measure that risk?

Maybe in this new era, sound investment advice won't be based on Modern Portfolio Theory and risk evaluation won't be the work of Monte Carlo software. But just exactly what will be the era's guiding principles and analytical techniques? Post-Modern Portfolio Theory and Las Vegas computational tools?

Tornado Graphs: Various Roles per Risk Analysis stage

Wednesday, February 4, 2009 by DMUU Training Team

Tornado graphs in @RISK are often thought of as providing an indication of the importance of a variable in determining the amount of variability (risk) in the output of a model). In an earlier posting we briefly described this interpretation (which is generally most valid for linear models where variables are independent of each other).

Perhaps more overlooked is the role of the graphs at different stages of the risk modelling process. The risk modelling process is often thought of as consisting of various stages (usually some variation of the sequence risk identification, risk modelling, and risk management, with the sequence conducted in an iterative way).

During the first pass through this sequence (i.e. the construction of the first qualitative risk model) a tornado graph can provide an idea of which variables are assumed to have the most variability. At this stage the graph can provide a check as to the quality of the model and its calibration. For example, a graph with one very dominant bar would raise questions as to whether in reality there is only one significant source of risk (generally realistic situations have several sources of risk that are of importance). The model may need to be recalibrated or rebuilt in some way to create a more realistic model.

During the second and subsequent passes through the sequence (i.e. the availability of the first “correct” risk model), the graph may provide an idea of where risk mitigation measures may be found. However, generally speaking the model would not contain enough information to make any definitive conclusions. For example, neither the cost of risk mitigation actions nor the issue as to whether there is the possibility to influence the risk factors would generally be included in the model at this stage. Such features may need to be added to the model, and indeed may generally be the subject of additional (perhaps out-of-model) analysis.

Ultimately, the number of passes through the sequence would not be infinite; rather at some point one has deemed that all relevant (worthwhile/economically effective etc) risk mitigation actions have been planned for (and included in the model). Arguably, this is the first time when a model has been built against which risk can truly be measured. The resulting model can be considered to be a “base case” risk model, which in some sense is optimized (i.e. all worthwhile risk mitigation measures and their costs and impacts have been included). At this stage, the notion of risk and its measurement is that of a “residual uncertainty” or something that cannot be efficiently controlled (according to the decision-maker’s own criteria). The tornado graph at this stage provides a description of the sources of the residual risk, but arguably provides no actionable information to the decision-maker.

Dr. Michael Rees
Director of Training and Consulting

The Shape of Things to Come

Tuesday, January 20, 2009 by Holly Bailey

It's Inauguration Day, when everyone is looking to the future, which is always a brighter spot than the particular one we happen to be in. But there are of course people who look to the future every day. They make a profession of anticipating it. These folks, from meteorologists to economists to financiers to farmers, all have a common stock in trade: probability. They are interested in pinpointing the moments when randomness and a particular event can meet.

For their work professional future-watchers use mathematical expressions that trace the path of likelihood through chance to happening: probability functions. These functions can be plotted graphically, and they come in many shapes and carry many names--Wikipedia lists at least a hundred different kinds of probability functions. So, depending on whether you're trying to calculate value-at-risk, doing statistical analysis for production forecasting, or helping a client with retirement planning, it's probable that there is a function formulated for that purpose. Choosing the correct probability function is crucial to credible forecasting.

Whether they are standard-issue or designer-created, probability functions have work to do. Introduced into mathematical models (such as those spun out by Monte Carlo software) they mediate the force of chance to specify the future outcomes in, say ---? Population trends? Widgets? Income? Depending on which future you're watching, the curve of a probability function is the shape of things to come.

Read This While You Can Still Access It

Thursday, January 8, 2009 by Holly Bailey

The best-laid plans are. . . .subject to change. An article by Joe Nocera in this week's New York Times Magazine causes me to put on the back burner my plans to blog on the concept of probability and its various expressions. I can do that later, but right now I want to persuade you to read Nocera.

Offering a really good capsule history of Value-at-Risk modeling for the uninitiated, Nocera delves into a theme that has pervaded my recent blogs for Palisade Corporation: it may not be the model but more likely the person managing the modeling who introduces slop into risk analysis. He has talked to a good many risk management experts, and is able to present a balanced view of both the limitations of VaR techniques and the shortcomings of the people who relied so heavily on their risk assessment techniques as to bring about the collapse of those sectors of the financial markets that depend on hedging and mortgages.

One thing that will be a relief to any of you who are doing quantitative risk assessment, Nocera never points a finger at Monte Carlo software or any other category of quantitative analysis software. So, the problem isn't the tools. It may be the---

I don't want to spoil this excellent article for you.

Risk Management Exams Required for Chinese Financial Managers

Wednesday, December 31, 2008 by DMUU Training Team

As a result of the world financial crisis, China is taking risk management seriously. The Chinese financial regulatory system has instituted testing for all workers in the financial sector, including risk managers, certified financial analysts and information security engineers. The workers are required to pass exams within one year, or lose their jobs.

“There are new sources of volatility that threaten our sound and stable growth,” the China Banking Regulatory Commission said in a statement in October. “It is important to recognize these new problems and make careful decisions to cope with them.” (NYTimes.com, December 25, 2008) The new exams seem to be an effort to ensure that all financial sector workers are familiar with good practices in decision making under uncertainty.

It appears that China has not been affected as drastically as the West during the recent financial turmoil. While China owns a lot of real estate in foreign markets that have taken a hit, the total holdings are only a fraction of China's wealth. Because Chinese banks are more cautious and more heavily regulated, they have managed to avoid the worst downturns. The financial sector is aiming to stay diversified, and it appears they may even be planning to enter new markets such as financial derivatives. Risk analysis is central to these efforts.

Perhaps the success in avoiding the worst of the market pitfalls has reinforced the historic tendency toward regulation in China. While many in Western markets are pointing to the U.S. Securities and Exchange Commission for its role in neglecting regulation, China is doubling up on its own regulation and adding risk assessment as a central part of the equation. In order to be most effective, Monte Carlo software such as @RISK should be a part of the new initiatives.

DMUU Training Team

The Lost Decade and How to Avoid Another One

Friday, December 26, 2008 by Holly Bailey

A broad-reaching reevaluation has been forced on both financial planners and retirees by the drastic down-trend in the financial markets. In my last commentary on retirement planning, I cited the work of Wharton professor Jeremy Siegel, who has used risk analysis and other forms of statistical analysis to estimate that it takes about twenty years for a "balanced" stock portfolio--retirement or otherwise--to produce optimum returns.

Okay, 20 years, two decades. I assume this includes the ten years that the financial media keep referring to as the "lost decade"--the ten years leading from 1998 to the present. Apparently, the reason it is dubbed lost is that after inflation is accounted for, the S&P 500 has gained only 1.3% in the past decade, and investors in equities saw their funds stand still while the economic engines idled. Ten years is a big portion of a person's life and puts a big dent in a retired person's income security. The large-scale effect of this is magnified by the fact that increasing numbers of people have turned to equities in their retirement planning.

Most of the comment I've read on how investors can avoid another lost decade of flat returns and outright losses identifies portfolio balance as the best protection. Although balance is defined by any number of criteria, the key element is diversification--both among types of investments and among stocks. And interestingly enough, Monte Carlo simulation is consistently cited as the tool to calculate the returns and timing of returns of various balancing schemes.

Is Norway’s Pension Fund Adequately Diversified?

Monday, December 1, 2008 by DMUU Training Team

As a regular visitor to Norway, it is hard not to be impressed by the wealth generation in the country. Even more impressive is the discipline of the government and population to accept that the majority of the vast oil windfall of the country should be invested for the future (in a pension fund) and not spent today (high tax rates and price levels being one testament to that).

In this blog I allow myself some raw speculation as to whether holistic risk management thinking is being adequately applied when it comes to the government’s management of the wealth generated by this windfall.

In the spring of 1997, the Ministry of Finance decided that the Government Pension Fund–Global (previously known as the Government Petroleum Fund) should invest parts of its portfolio in equities. In January 1998 the fund consisted of bond investments worth NOK 113 billion (about USD 15 billion at current exchange rates). Since then inflows of new capital into the fund (also boosted by the high oil price) have been significant; in 2007, capital inflows averaged more than USD 300 million per trading day. By January 2008 the fund was worth over NOK 2000 billion (about USD 300 billion) and it is forecast to be worth over NOK 4000 billion (about USD 600 billion) by 2015 (according to the National Budget 2009)—the ultimate in retirement planning. Over time the fund’s investment guidelines have been relaxed, with the fund currently consisting of about 50% equities and 50% bonds, including government, corporate, securitized and inflation linked bonds.

To some extent there is a natural diversification in the fund. For example, to the extent that it is believed that global equities in aggregate are negatively affected by high oil prices, then there is a natural hedge in the portfolio, as increases in the oil price will reduce the equity value but lead to increased capital inflows (although the balance of this will change as the equity portfolio becomes larger). Similarly, oil-related new inflows and the investment in inflation-linked bonds could also provide some protection against long-term inflation (arguably, equities may or may not be a good long term inflation hedge). In addition, the fund of course also uses advanced tools of portfolio management, which are surely applied with rigor. However, as we know from the credit crisis, such tools can lead one to a false sense of confidence if they miss the big picture (deckchairs on the Titanic, etc!).

In this context, I allow myself to speculate (hypothesise?!) as to whether the fund should be devoting far more significant efforts to invest in non-traditional assets. (The fund’s performance is essentially currently measured against a benchmark portfolio of bonds and equities and so such efforts or investments would be hard to justify against these objectives).

The most obvious scenario in which the fund could lose out significantly would be a shift in the world’s energy sources (over the many decades of pension obligations), which could create an environment that is simultaneously largely unfavorable for most asset classes in the fund. Conceivably the potentially massive costs associated with creating a low-carbon global economy could produce a situation that is unfavorable for most global equity investments, that could unleash inflationary forces that reduce the value of many bond investments, and potentially reduce demand for oil (and its price). Such a “nightmare” scenario for the fund does not seem beyond the realms of reality.

The most obvious strategy to mitigate the effects of this scenario would be for the fund to proactively take very large positions in alternative energy technologies. Such positions would presumably need to be very large (and possibly require the fund to itself create and support the development of new innovative companies in this area, not just to passively invest in existing ones). The costs and risks in doing so would be large (particularly as the scenario may never materialize), but it could be a prudent one, given the already very large fund that already exists for a small population base of about 5m people. Could it be so bad if 5%-10% of that fund were invested in such technologies? (Such investments would arguably support, or at least relate to, some of the fund’s other goals—such as ethical or social investments). A risk assessment would be a good idea. Now, back to the real world!

Dr. Michael Rees
Director of Training and Consulting

"Financial Modelling in Practice" Now Available at Amazon.com

Saturday, November 29, 2008 by DMUU Training Team

This week saw my book "Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level" (John Wiley&Sons) being available to a US audience on amazon.com (the original plan was for the launch date to be end December 2008).

I have high hopes for the book in the US market, being one of the most sophisticated markets for financial modelling and its applications. My belief is that many modellers have a reasonable knowledge of core Excel functionality, but desire to increase and consolidate their knowledge in a way that is prioritised, practical, and application-driven. In addition, I felt that there were few if any really good texts out there which help modellers to design, to structure and to build models which are relevant, accurate, and readily understandable. Many texts and training courses in the modelling area put their emphasis either on Excel functionality, or on financial theory, or on mathematical models, but hardly address the modelling process. Finally, most modelling texts either do not adequately treat the topic of risk analysis, or otherwise treat it from a mathematical perspective that is both inaccessible to many modellers and lacking in practical tools.

The book starts with a review of Excel functions that are generally most relevant for building intermediate and advanced level models, including functions relevant to statistical analysis. It then discusses the principles involved in designing, structuring and building relevant, accurate and readily understandable models. Topics covered include the use of sensitivity analysis, best practice modelling principles and related issues, and model auditing tools. A Chapter is devoted to the modelling of financial statements and of cash flow valuation using discounted cash flow analysis. It then moves on to discuss risk assessment and uncertainty modelling. Many practical applications and example models are presented in an intuitive and accessible way and the @RISK Monte Carlo software from Palisade Corporation is used to implement most models. The topic of options and real options modelling is then covered, treating these as a natural extension of risk modelling. Classical option valuation methods are discussed, as well as practical methods of modelling real options, including the implementation of decision trees. Chapter 6 covers VBA for financial modelling applications. The topics selected for inclusion were established by consideration of the core types of financial models that frequently require the use of VBA and provides beginners in this area with a solid base on which to discover the richer possibilities available to modellers by using VBA.

» Buy now at Amazon.com
» Buy now at Amazon.co.uk

Dr. Michael Rees
Director of Training and Consulting