Simulate multiple portfolio assets (such as stocks, bonds, real estate, or projects) to maximize return while minimizing risk. Calculate Value-at-Risk, or the probability of different losses on a portfolio.
{Palisade @RISK 5.7 } 14
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With a broad library of probability distributions, data fitting tools, and correlation modeling, @RISK lets you represent any scenario in any industry with the highest levelof accuracy.Add Decision & Data Analysis with The DecisionTools SuiteThe complete risk and decision analysis toolkit, including @RISK, PrecisionTree, TopRank, NeuralTools, StatTools, Evolver, RISKOptimizer, and ScheduleRiskAnalysis.
Solution:The Microsoft Windows Installer Cleanup Utility may be able to repair this condition; please contact Technical Support at support@palisade.com for a download link and instructions.
If you have @RISK 5.0 initial release (not part of The DecisionTools Suite), please visit palisade.com/Updates to update your software. If you are unable to update your software, here are three alternatives to choose from:
In practice, the problem of an invalid correlation matrix, i.e. one that has negative eigenvalues, can also very easily arise in the context of risk analysis for equity portfolios. This is because there are frequently asynchronous gaps in the historical stock exchange time series. The chance that slight inconsistencies in the data from which historical correlation coefficients are calculated can lead to negative eigenvalues grows rapidly as the size of the correlation matrix increases. ... Since equity index or portfolio analysis typically involves many underlying assets, the risk of negative eigenvalues of the correlation matrix calculated from historical data is particularly large.
Are you modeling a risk that may or may not occur, with a range of possible severities? You might have a formula like=RiskBernoulli(0.4)*RiskTriang(10,25,40)or=IF(RiskBinomial(1,0.4)=1, RiskTriang(10,25,40), 0) (It could be any continuous distribution, not just RiskTriang.) To model a maybe-yes-maybe-no risk, RiskCompound is a better choice:=RiskCompound(RiskBernoulli(0.4),RiskTriang(10,25,40))The RiskCompound counts as one input, not three, so it simplifies your tornado graphs. Using a different discrete distribution in RiskCompound models risks that may occur multiple times, as explained in Combining Probability and Impact (Frequency and Severity).
It's almost always more useful to take the percentile of the sum, the green figure in the example. For example, that tells you how much to set aside in reserves if you want 95% confidence that you've set aside enough. There's a 95% chance that the total of all eight risks will turn out to be less than that figure. Sure, some risks may be above their individual 95th percentiles, but most will be below their 95th percentiles. The green figure is the realistic measure of your total risk.
Suppose you want a sort of worst-case scenario? What if every risk happens to come in at its 95th percentile? (Of course that is very unlikely, only about one chance in 25,600,000,000.) The total of the eight individual 95th percentiles is the orange figure in the P95 column. If you set aside that larger figure as a reserve, you're making a reserve for a situation with only 0.004% of a millionth of a chance of occurring.
First, does this happen with only one particular workbook? For example, can you simulate one of our examples successfully? (Click Help Example Spreadsheets.) If it's only a particular workbook, please email it to us at support@palisade.com and we'll have a look.
The choice of enabling or disabling Smart Sensitivity Analysis can be made for each workbook, and you can also set it in Application Settings to apply to new models. These settings are available in 5.x,-7.x, except @RISK 5.0.0. If you do not have this choice, please visit palisade.com/Updates to request an update to our current release.
The choice of enabling or disabling Smart Sensitivity Analysis can be made for each workbook, and you can also set it in Application Settings to apply to new models. These settings are available in @RISK 5.0.1 and above and in all releases of @RISK in The DecisionTools Suite 5.x. If you do not have this choice, please click Help About @RISK to verify that you have @RISK 5.0.0, and in that case please visit palisade.com/Updates/ to request an update to our current version.
Open Computer or My Computer and navigate to the directory where you installed Palisade software. The default location is C:\Program Files\Palisade, or C:\DTools for older versions. Then open the subfolders RISKPJ41 Tutorial riskproj_wmvs. Open the Overview file and then the rest of the files in numerical order.
If the utility is indistinguishable from 1, then you're trying to take the logarithm of 0, which can't be done, so you get #VALUE. Essentially you're saying that your risk tolerance is greater than any of the possible negative outcomes on this branch of the tree. Mathematically, a larger R would resolve the #VALUE error, but it way not be a good representation of your risk tolerance. (For example, an R of 3000 would prevent #VALUE errors in the attached example.)
Second: lmgrd and the vendor daemon palisade.exe communicate with each other on the vendor daemon port. Check the log to find which port is being used, and verify that communications can take place on that port, both inbound and outbound. You can test whether a given port is open by using PowerShell (v4 or newer) or Telnet:
If you're upgrading an existing product to a later release, please contact Palisade Technical Support at tech-support@palisade.com for customized instructions. When you contact us, please include both the old and new serial numbers and mention that you're getting an Error 1316 during a second network server install.
Please visit our Web page at and select your preferred language in the box immediately below "Software Updates and Upgrades". Then choose the correct product and edition. Caution: @RISK cannot be activated if your license is for the Suite, and the Suite cannot be activated if your license is for @RISK. If you don't see your product and edition listed, or you're not sure, please contact Technical Support at support@palisade.com and include your serial number in your message.
Network users with a license for @RISK 5.5 or DecisionTools Suite 5.5 don't have to do anything on the server side. On the client side, an individual client can install second-language @RISK of the same edition (Industrial, Professional, Standard) using the standalone installer. If in doubt, please email your serial number or Activation ID to support@palisade.com and we'll make sure you get the correct installer.
Email us at support@palisade.com and you should hear back within 24 hours (usually sooner), apart from weekends. You can speed things up by including your serial number or book title and ISBN. Be as specific as possible with your question or problem. If you're getting an error message, attach a screen shot. If the software seems to be misbehaving, attach your workbook.
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Effective prevention can reduce the burden of diseases. Strategies have been promoted such as appropriate use of antimicrobials, use of contact precautions and protective personal equipment to care for infected patients, effective cleaning and disinfection of equipment and the environment, and early recognition of disease as primary prophylaxis [71]. As CDI is an infectious disease, the population at risk would benefit from an effective vaccine, which is currently under development [72, 73].
Furlonge, H.I. (2011), "A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business", International Journal of Energy Sector Management, Vol. 5 No. 4, pp. 471-493.
An important source of risk is the difference in expected price between Valencia and Navel oranges and how this varies through time. Based on stochastic dominance testing, the reworking strategy was dominant for price conditions experienced by the industry over the last thirty years. However, in the future, this will depend on demand and supply conditions in fresh fruit and processing markets.
This paper compares these three investment strategies. Benefit/cost analysis is applied to development budgets. The potential impact of price uncertainty is analysed using Monte Carlo simulation techniques within @RISK as well as stochastic dominance analysis. Clearly the outcome of these strategies depends on the relative prices of processing Valencias and fresh Navels in the future. Industry outlook information and historical prices are the basis of the risk analysis below.
Price data for fresh Navels were taken from annual monthly average prices received at the Flemington markets over the period 1967/68 - 1996/97 (Table 2) . Monthly prices were transformed into annual average prices to smooth out monthly fluctuations. A total of 30 observations were recorded for wholesale prices of Navel oranges sold in NSW during this period. Data which were not available for the years 1978/79 and 1981/82 were calculated as an average of the price that fell either side of those years. Processing prices for Valencia oranges were taken from the Australian Commodity Statistics 1996 for the period 1973/74 to 1996/97 (Table 2) . Nominal prices were converted to 1997 dollars using the Australian CPI Index. The on farm price of navels on a per tonne basis was estimated by multiplying the real per case price by 50, the number of cases of navels picked that end up at the wholesale market less $350 per tonne which is an allowance for packing costs and a proportion of navels that go to processing. These prices were used in both a deterministic and risk analysis of the strategies. A real price of $100 per tonne, received for processing Valencias in the Riverina in 1997, was also considered in the deterministic analysis. 2ff7e9595c
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