@RISK Quick Tips: Automating @RISK risk analysis software with VBA

Tuesday, August 31, 2010 by DMUU Training Team
Presented at the Palisade Risk & Decision Analysis Conference New York City
Chris Albright, author of the book VBA for Modelers, presented a number of examples of how to automate @RISK, RISKOptimizer, and StatTools in Excel using Excel's VBA and Palisade's built-in object-oriented Excel Developer Kit. These examples include production applications, scheduling applications, World Series simulation, and more. All examples include macros written by Dr. Albright, so you'll need to enable macros when you open them.

» Download the examples
» Order Dr. Albright's book "VBA for Modelers"

The Better to Be Believed

Friday, August 27, 2010 by Holly Bailey
In his blog yesterday for Smart Data Collective, Dean Abbott, makes a worthy, commonsense observation: no matter how accurate a predictive model is, it is of no use to the enterprise unless it is presented in such a way that all the decision makers understand what factors and techniques went into the analysis and why.
 
The reason that the 'best understood' model is more effective than the 'best' model is that when the people with authority over a particular decision are presented with a statistical analysis that is beyond their ken, they may or may not pretend to understand it.  But in any event, they are not likely to buy into the results if they can't retell the story the model describes.  
 
Take for instance, a Monte Carlo simulation that focuses on credit risk analysis for a particular loan.   Everyone in the line of authority will be held responsible for real world outcome of what the Monte Carlo software describes in the Excel spreadsheet.   And if you are one of these decision makers, how can you take responsibility for something you may not quite understand?
 
The problem of acceptance of a predictive model presents the analyst with a tough question: Do I present the model that I know is true and statistically accurate?  Or do I present a ruder, cruder analysis that presents a story that can be immediately understood?
 
Abbott suggests a compromise: streamline your plot by masking (Abbott says "removing") fields that contribute to the robustness of the analysis but involve statistical twists and turns that are distracting to decision makers who may not be fascinated with technique and just want to see how the story turns out. This, he explains, allows you to work from a model both you and the decision makers can believe in.

Your thoughts? 

Market decline versus speed to market – ‘A bird in the hand...’

Wednesday, August 25, 2010 by DMUU Training Team
I recently saw an interesting @RISK cashflow model from the portable phone industry. It modeled the uncertainty in the length and decline of overall market demand for a particular technology against five strategies for getting various application products to market as soon as possible. 

Using @RISK’s Simtable function, combined with Excel’s Index function, it was possible to run multiple simulations and see which strategy could take best advantage of the potential market, given the uncertainties in the development process, the possibility of competitors, the market take-up and the margins that might be achieved.

As is often the case in all aspects of life, the simulation revealed that ‘a bird in the hand is better than two in the bush’; it’s very comforting to know that @RISK risk analysis solutions can cut through loads of detail and come back with an answer that echoes received wisdom!

Ian Wallace, ACMA
Palisade Training Team

Rating the Polls

Monday, August 23, 2010 by Holly Bailey
With the New York State primaries coming up September 14 and the general election on November 2, I predict that as soon as summer turns the corner into September, we'll start hearing lots and lots about polls that predict election outcomes.  To find out if there was any early discussion of polls, polling, and outcomes, I returned to my favorite election forecast site from the 2008 presidential elections, FiveThirtyEight: Politics Done Right.
 
Sure enough, there it was, a comparative rating of pollsters. This will give people like me, who tend to believe any poll just because it's covered in the news, a way to assess the poll reliability. FiveThirtyEight is the brainchild of Nate Silver, and 538 is the number of members of the Electoral College.  Silver's primary business is Baseball Prospectus, which is also fueled by Monte Carlo simulation and other risk analysis techniques, but FiveThirtyEight has done well enough for the New York Times to want incorporate it in its online coverage during the coming elections.
 
Silver's grasp of statistical analysis becomes immediately evident when you go to his page on the pollsters, and he's more than happy to discuss the statistical methods he uses to rate the pollsters--regression analysis of raw data, Monte Carlo software in an Excel spreadsheet, weighting of poll performance data, and so forth. His take on these matters may be of practical interest to any of you who use these techniques in financial risk analysis.

Elections are all about decision making under uncertainty, especially voter decisions under uncertainty, and according to Nate Silver, only polls taken within 21 days of an election are reasonably reliable.  So when the national campaigns are ramping up in October, keep one eye on the polls and one on FiveThirtyEight.  



@RISK Quick Tips: Six Sigma Design of Experiments: Welding

Tuesday, August 17, 2010 by DMUU Training Team
A key application of @RISK is Six Sigma and quality analysis. This model demonstrates how @RISK can be used for a DOE analysis of a welding project.

Suppose you are analyzing a metallic burst cup manufactured by welding a disk onto a ring. You need to make sure weld strength is within safety limits. The model relates the weld strength to process and design factors, models the variation for each factor, and forecasts the product performance in relation to the engineering specifications. @RISK is used to model the variation in each factor, and simulate different outcomes for weld strength. Output includes After Six Sigma statistics for Cpk-Upper, Cpk-Lower, Cpk, and PPM Defects (or DPM). Standard @RISK statistical analysis functions (like RiskMean) are also used.

» Download the example: SixSigmaDOE.xls

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by Steve Hunt
On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.


» Register now (FREE)
» View archived webcasts

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by DMUU Training Team
On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.


» Register now (FREE)
» View archived webcasts

Taking the Price

Friday, August 13, 2010 by Holly Bailey
Everyone should be allowed at least one vice, and mine is horses.  I love them, spend as much time around them as feasible, and find that after years of this I'm still learning. Recently I've met a couple of people know a whole lot about horse racing.  They don't know a thing about the horse itself, but they have a very sophisticated understanding of the mathematics of predicting performance.
 
So that I could keep up my end of our conversations, I looked further into handicapping and discovered that horse races themselves are only a kind of graphical display to show the results of some massive efforts at statistical analysis, including some of the quantitative forecasting techniques used by financial analysts and whole lot of custom Excel programming.  This should surprise no one--after all, what is betting on a horse if not decision making under uncertainty?--but what did surprise me is level of technical discussion about the math and how to work it through in Microsoft Excel statistics.
 
Take a look, for instance at a recent blog on "taking the price" from the U.K.'s Simon "The God of Odds" Rowland.  Taking the price is locking in the odds when you bet.  He discusses how to correlate a horse's rating--the amount of weight the horse has been assigned to carry--with the actual odds on this competitor.  He then gives the mathematical recipe for his custom Excel spreadsheet, which combines Monte Carlo simulation and the related Markov Chains technique. He wraps up his demonstration with a standard disclaimer: "It must be immediately apparent that this process is very susceptible to the GIGO (garbage in, garbage out) principle. No manner of mathematical manipulation will make up for essential shortcomings in the ratings and in the confidence attributed to those ratings."
 
No matter how good your model, it's still You Play, You Pay.  And Rowland's disclaimer echoed a comment an influential racing veterinarian made to me: "Never invest in something that eats while you sleep."     

@RISK Quick Tips: RiskSimtable to Perform Multiple Simulations

Tuesday, August 10, 2010 by DMUU Training Team
@RISK's use of Monte Carlo simulation allows for powerful features, like RiskSimtable.

The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the risk analysis model, for example to changes in the parameters of a distribution. This model is of a business with a base case expected revenue of 100 and cost of 80, giving a profit of 20.

The risk model assumes that the revenue and cost distributions are determined from a mean and standard deviation. The RiskSimtable feature is used to test the sensitivity of the distribution of profit to changes in the standard deviation of the revenues. Three values are tested of which the first is our original @RISK model. The number of simulations is therefore set at 3. A RiskSimtable can be set up either by directly typing in the required format, or by inserting it as for other Excel functions via the Insert Function menu option. The model also uses some @RISK statistical analysis functions to report the probability for each simulation that the profit exceeds 50.

» Download the example: BasicBusiness.Simtable.xls

Oops! Didn’t see that coming! Part 4

Monday, August 9, 2010 by Steve Hunt

This is the conclusion of Dave Roy’s guest blog, we hope you have found them informative. Again, Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Also, look for Dave’s free live webcast on August 19th, Assessing your New Product, Process or Service Introduction Methodology: Is yours premier? Does it enable Six Sigma performance?



Oops! Didn’t see that coming! Part 4
 

 

As a continuation from the July blog, we are now concluding with the “Optimize” and “Validate” phases of the ICOV (Identify-Conceptualize-Optimize-Validate) framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

 

These phases are important because it allows time and methodology to optimize the design, develop all of the detailed documentation, verify performance and capability under operating conditions and manage an orderly transition to the new state.

 

The Optimize phase consists of a single stage (Design Optimization) and associated Tollgate 5 to validate successful completion of the requirements. 

 

The Design Optimization stage involves completing all of the detailed design documentation, building Prototypes of the design, simulating/analyzing Process Capability, preparing all Control Plans and updating the Design and Process Scorecards.

 

Tollgate 5 Exit Criteria:

o    Agreement that functionality and performance meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Validate stage.

 

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Simulation, Change Management, Control Plans, Reliability and Robustness.

 

The Validate phase consists of two stages (Verification and Launch Readiness) and associated Tollgates (6 and 7) to validate successful completion of the requirements. 

 

The Verification stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

 

Tollgate 6 Exit Criteria:

o    Agreement that functionality and performance from the pilot meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Launch Readiness stage.

 

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Change Management, Control Plans, Statistical Process Control (SPC), and Confidence Analysis.

 

The Launch Readiness stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

 

Tollgate 7 Exit Criteria:

o    Agreement that transition plans and training plans have been developed and are executable.

o    Approval to proceed with the Production stage.

 

Formal tools which can be used in this phase are Transition Plans, Training Plans, Process management, Change Management and Control Plans.

 

Following the ICOV model we have now used a formal methodology that allows us to validate performance at progressive economical stages and have improved the ability to detect unknown risks thus avoiding the Oops! Didn’t see that coming!. It should be mentioned that the methodology should be flexible and scalable to adjust for level of invention and risk. A brand new invention (Research & Development) that has never been deployed in similar conditions is much different than implementing a known solution (Application Engineering) under new conditions.

 » Part 1
 » Part 2
 » Part 3
 

 

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

 

Graphing with your Mouse – Part I: Drag and Drop

Thursday, August 5, 2010 by DMUU Training Team
Monte Carlo simulation is a very powerful tool for modeling uncertainty. But perhaps the most critical step in any simulation analysis is the meaningful presentation of results to others. Decision makers won’t act on the results of a simulation if they don’t understand what they are seeing. Graphs are the most powerful way to communicate these important insights.

There are lots of ways to make graphs from the data generated by Monte Carlo simulations. But what is the easiest? Microsoft Excel statistics offers its own graphing engine, but you have to tell it which data to use for what. 

@RISK comes with a powerful graphing engine built-in, and you can create meaningful graphs just by dragging and clicking things with your mouse. In this three-part series, we’ll cover the most common ways to get valuable graphical results in @RISK without ever touching your keyboard.

First off, @RISK automatically generates thumbnail graphs of input variables and output results during a simulation. These are accessible in the @RISK Model window (for inputs) and @RISK Results window (for outputs). You can expand any small thumbnail graph from these windows to full size just by dragging it off the window and onto the spreadsheet. 

Here is a quick video showing how easy it is to do this:



» View short videos on recently added @RISK features

A Little Limelight

Tuesday, July 27, 2010 by Holly Bailey
Limelight--and by this I mean positively glowing publicity-- shines only occasionally on quantitative analysis, and rarely on Monte Carlo simulation.  But there was, 6 years ago, Michael Lewis's Moneyball, which established a place for statistical analysis in major league baseball.  Now there is Relativity Media, LLC, currently one of the heaviest hitting movie production companies in the business, and, more specifically, there is Ryan Kavanaugh, its CEO, and Ramon Wilson, its executive vice-president of business development.
 
Two things about Kavanaugh and Wilson make them unusual: they are leading a movie production firm that is not only alive but growing, and they use quantitative analysis for lots of decision making under uncertainty.  What can be more uncertain than investing in a movie? Only somewhat unusual for the movie business is the fact that these two decision makers are under thirty-five--it's a youth oriented business--and maybe this is correlated with their emphasis on making decisions by the numbers.  
 
"You can't think of it as money," Kavanaugh has been quoted as saying.  "You have to think of it as math."  Given the multimillion-dollar budgets Relativity underwrites--the raw size of the risks involved--it's probably more comfortable for everybody at Relativity to think math.  The kind of math Kavanaugh is particularly devoted to is Monte Carlo simulation, and he talks quite openly about his company's use of it.  When it comes to variables, he names names: principal actor,  genre, director, release date, PG  or R, although in all probability (sorry), each of these variables is probably a set of variables.  
 
"Everything has to run on the principle of profit.  We'll never let creative decisions rule our business decisions.  If it doesn't fit the model, it doesn't get done."  That doesn't mean, he has explained, that if he really likes a project, he and Wilson can't juggle the variables to make the film project fit the model.  They change the parameters to reveal the path to profit.  And profit he has--the estimated assets of Relativity are about $2 billion.  

So Kavanaugh qualifies as a mogul, a math-for-movies mogul.  When the spotlight falls on him, Monte Carlo simulation isn't far out of it.

  

Sharing Simulation Models – Part III: The @RISK Library

Friday, July 23, 2010 by DMUU Training Team
Another great way to collaborate with others is through the @RISK Library. This is a SQL-based database that lets you store customized @RISK functions with the specific parameters that you need.  Then others can pull the same @RISK function for their risk analysis models, directly from the standard Define Distribution window. This way you can be sure everyone is using consistent parameters in the statistical analysis.

You can also store simulation results in the @RISK Library. This is a great tool for auditing simulations to see exactly what happened, what changed, and what affected the outcome. Furthermore, these simulation results can be used as inputs for other simulations.

A quick video on the @RISK Library:



» View short videos on recently added @RISK features

@RISK Quick Tips: Use of RiskTheo to Represent Distributions as Discrete Ones

Tuesday, July 20, 2010 by DMUU Training Team
@RISK, risk analysis software using Monte Carlo simulation, has many powerful features that help you create powerful models for decision making under uncertainty.

For example, you can use the RiskTheo function in @RISK to determine the parameters of a discrete distribution based on a continuous one. In this example, the RiskTheo functions of @RISK work out the P10, P50, and P90 percentiles of a continuous distribution (in this case the LogNormal), and the Mean and Standard Deviation of a RiskDiscrete distribution which has these X-values and some assumed probabilities (or weights). It then uses Excel's Solver to work out the probabilities required so that the discrete distribution based on these percentiles and probabilities would have the same mean and standard deviation as the continuous distribution.

» Download the example: CtsToDiscrete.xls
» See "Uses of the RiskTheo functions in
   @RISK to match distributions
"
 » See "DMAIC Failure Rate using RISKTheo" for a
    Six Sigma application of the RISKTheo function

Oops! Didn’t see that coming! Part 3

Monday, July 19, 2010 by Steve Hunt

We are pleased to welcome back to my blog consultant and trainer David Roy from Six Sigma Professionals, Inc.

 

 

Oops! Didn’t see that coming! Part 3
 

 

As a continuation from the June blog, we are now covering the “Conceptualize” phase of the ICOV framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

 

This phase is important because it conceives, evaluates and selects good design solutions through robust process methodology which ensures alignment to the customer and the business needs.

 

Many design solutions skip this phase and become typically named as “Launch and Learn”.

 

The Conceptualize phase consists of two stages and associated Tollgates to validate successful completion of the requirements. 

 

The Concept Development stage involves translating Customer requirements into solution free Functional requirements, developing the System Level Conceptual Design, generating Concepts for required functions, Concept selection and translation of the Functional Requirements to Design Parameters.Click to Enlarge

An example of a Functional Requirement for a Customer Want of “Speedy Service” could be “Speed of Service” and a Design Parameter could be “Waiting Time

 

Tollgate 3 Exit Criteria:

  • Assessment that the Conceptual Development Plan & Cost will satisfy the customer base
  • A Decision the design represents an economic opportunity (if appropriate)
  • Verification adequate funding will be available to perform Preliminary Design
  • Identification of the Tollgate Keeper & the appropriate staff
  • An action plan to continue flow-down of the design Functional Requirements

 

The Preliminary Design stage involves creating the design documentation and configuration management, performing design analysis and testing, translating the Design Parameters into Process Variables and formulating the Production strategy.

An example of further mapping the Design Parameter of “Waiting Time” to a Process Variable could be “Number of Phone Lines

 

Tollgate 4 Exit Criteria:

  • Acceptance of the selected Solution/Design
  • Agreement the Design is likely to satisfy all Design Requirements
  • Agreement to proceed with the next stage of the selected Solution/Design
  • An action plan to finish the flow-down of the design Functional Requirements to design parameters and process variables

 

Formal tools which can be used in this phase are QFD, TRIZ/Axiomatic design, Measurement System Analysis (MSA), Failure Mode effect Analysis (FMEA), Design scorecard, Process mapping, Process management, Pugh Concept Selection, Robust Design, Design Scorecards, Design for X and Design reviews.

 

The next and final blog will cover the Optimize and Validate phases.

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds a BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

 


 » Part 1
 » Part 2


Sharing Simulation Models – Part II: Saving @RISK Simulations

Thursday, July 15, 2010 by DMUU Training Team
You can save @RISK risk analysis simulation results directly in your Excel workbook. This makes it easy to pass results around to others. Colleagues can see the benefits of risk analysis using Monte Carlo simulation. Just save your workbook like you normally would, then click Yes when prompted if you want to save simulation results. When you reopen the workbook with @RISK running, you will have access to those results again.



» Check out this video to see how

Free Webcast This Thursday: “Why be Normal? Selecting the Best Distribution Models ”

Thursday, July 8, 2010 by DMUU Training Team
On Thursday, July 15, 2010, Andy Sleeper will present a free live webcast entitled. "Why be Normal? Selecting the Best Distribution Models "

Distribution models are important aspects of many types of statistical analysis, including Monte Carlo analysis. The choice of model is vitally important, since the wrong model can be worse than no model at all. But with dozens of distribution families to choose from, the choice of distribution model can be confusing and mystifying. This free live webcast takes the mystery out of distribution model selection and explains the powerful tools built into @RISK and StatTools. How often have you wondered which type of graph is best suited for selecting distribution models? Which goodness-of-fit test is best for you? Is Kolmogoroff-Smirnov a new kind of vodka? All this and much, much more shall be revealed with demonstrations of Palisade software during this unique webcast.

» Register now (FREE)
» View archived webcasts

Customised Solutions Using @RISK and VBA for Excel

Thursday, July 8, 2010 by DMUU Training Team
If you missed Palisade trainer Rishi Prabhakar's webcast "Customised Solutions Using @RISK and VBA for Excel," you can still view it in our archive.

The hour-long presentation explores the use of VBA for Microsoft Excel to control @RISK functionality, to simplify the process of risk analysis for resource-strapped businesses. Rishi explains the advantages (and limitations) of macro control for modelling and running simulations.

Simple examples are worked through to show the XDK (@RISK’s automation library) in action, from generic examples to a cost estimation model. This addresses elements of model construction, various simulation settings and finally reporting. The emphasis is on exposing the viewer to the various possibilities the XDK lends to the user rather than an in-depth VBA for Excel coding session.

Rishi Prabhakar holds a BSc in Mathematics from the University of Technology, Sydney Australia. Rishi has experience in the resources, infrastructure and primary industries, telecommunications, scientific research, banking and finance with an emphasis on operational risk.

With technical skills in the areas of modelling, simulation, statistical analysis, cost estimation, time series forecasting, customised solutions utilising VBA for Excel, and extreme value theory, Rishi has provided training and consulting services in risk and decision analysis for Palisade’s Asia Pacific office since 2005.


» Customised Solutions Using @RISK and VBA for Excel
» Webcast archive

@RISK Quick Tips: Insurance Claims with RiskCompound Cell Referencing.

Tuesday, July 6, 2010 by DMUU Training Team
Modeling Uncertain Number of Events, Each with Uncertain Parameters
@RISK (risk analysis software using Monte Carlo simulation) is widely used in insurance and reinsurance for premium pricing and loss reserves modeling. A 2006 survey identified @RISK as the third most widely-used software by actuaries, after Microsoft Office and in-house actuarial tools.

@RISK's RiskCompound function allows for the sampling of frequency-severity distributions. This is often required in insurance modelling, as well as in some operations management situations. For example, to determine the total insurance claims payout, one must account for the uncertainty in both the total number of claims (frequency) and the dollar amount of each claim made (severity). 

A powerful feature of the function is that the argument that corresponds to the severity may be a reference to a cell containing a formula (rather than just a single distribution function). 

For example, one could use the function in the form RiskCompound(RiskPoisson(5), A10). The Poisson distribution would describe the frequency (occurrence) of events (e.g. an individual sample may determine that three events occur), and cell A10 would contain a formula that is separately evaluated for each of these three events (before returning the sum of these three as the sampled value of RiskCompound). 

A simple example could be A10 = RiskLognorm(10000,1000)/(1.1^RiskWeibull(2,1)), with the Weibull distribution representing the time to settlement of an insurance claim, which is used to discount the basic claim value sampled from the Lognormal distribution of severity. For example, once a claim is filed for a nominal amount, the actual payment may be delayed due to court actions or disputes, which may reduce the cost of the claim to the insurer.

» Download the model: RiskCompoundCellReferencing.xls

Easy, But Is It Rigorous?

Friday, July 2, 2010 by Holly Bailey
Value-at-Risk is a calculation that predicts a worst case scenario in which the maximum loss for a specific investment would be realized.  Recently the San Francisco Chronicle investment blog Investopedia, ran a short series posts on VAR.  One of the more intriguing of these demonstrated three ways of calculating Value-at-Risk for a single stock investment for more than one time period.  
 
The three methods were historical simulation, variance-covariance, and Monte Carlo simulation. What was intriguing about the comparison of methods was the observation that best choice among these methods was the variance-covariance method because it was easy. The downside of using the historical method was the need to crunch data and the downside of getting out your Monte Carlo software--no mention of using historical data to inform your model--was that the Monte Carlo method was "complex."  
 
Does that mean that risk is simple enough to require only simple statistical analysis?  And doesn't this kind of thinking encourage financial planners to take a direct but drastically reduced view of the possible outcomes of an investment?  And isn't this the same turn of mind that led to the collapse of the financial markets only a year or so ago?
 
Variance-covariance assumes volatility only in terms of standard deviation, and volatility doesn't come in one flavor or standard deviation.  Neither does risk.