Day: August 18, 2009

Risking Research on Real Risk

Last week in his blog for the New York Times, science writer John Tierney posed an intriguing and scary question: "Should the United States and other governments start supporting climate engineering research?" 
 
What climate engineering refers to is modifying the climate to offset the adverse effects of climate change.  Some of the research involved would examine the feasibility of solutions worthy of treatment in serious sci-fi: spraying aerosol particles into the stratosphere to mimic the cooling effect of volcano eruptions, wind-powered robot vessels that would launch a mist of sea water droplets skyward and cause the clouds over the ocean to brighten and reflect more sunlight away from the earth. 
 
Tierney’s question is not if these methods would work, but if we should fund research into their feasibility. It’s essentially a cost-benefit question, a puzzle in risk assessment and decision evaluation involving hundreds of billions of dollars.
 
But–this was something of a surprise to me–none of the blog entries responding to this return-on-research-investment proposition addressed the economic question.  Although a number of reader’s objections were based on the observations that predictive modeling is still based on curve-fitting probabilities and that the predictive value of models focused on natural phenomena was limited, nobody wanted to go there, either to the economic question or to the idea of climate engineering.  
 

Clearly, I wasn’t the only one who found the prospect of climate engineering–or even research on its methods–scary.  The value-at-risk is just too real.  

Using @RISK and Principal Component Analysis (PCA) for Valuing a Portfolio of Natural Gas Futures

The use of custom Excel VBA programming and @RISK APIs allows the automated analysis of historical data and construction of sophisticated risk models. Here, we present an application in the energy sector as an example.

Palisade Corporation developed an add-in that automates the construction of a risk analysis model to assess the Value-At-Risk  (VaR) of a portfolio of gas future contracts.  This application uses a Principal Component Analysis (PCA) to describe the variability of historical correlated forward price curves; this analysis allows the creation of a @RISK Monte Carlo simulation model to generate forward price curves and compare them against the current positions of the portfolio.

PCA is a statistical technique which can identify the main independent components (sources of risk or information) in data (In this example, historical prices of natural gas forward contracts.). There will generally be as many components as there are forward contracts in the analysis. Therefore, if we are analyzing monthly contracts up to 36 months forward, the analysis would reveal 36 components. In data which is highly correlated (such as natural gas forward prices), typically only 2 or 3 components are significant, accounting for nearly all the variation or “movement” in the data set. For the forward price curves of natural gas, the first principal component generally corresponds to a parallel shift in prices, while subsequent principal components correspond to relative price changes (i.e. a change in calendar spreads).

Using a VBA macro, historical data is analyzed using PCA. The macro constructs an Excel model and @RISK runs a simulation to generate forward price curves so the risk profile of the portfolio can be assessed. The figure below presents a result that shows the predicted performance a of sample portfolio where the VaR (@ 5%) is shown:

Valuing Natural Gas Storage Using Seasonal Principal Component Analysis,  Carlos Blanco, Ph.D., Financial Engineering Associates, 2002.

If you are interested in the implementation of this type of model, @RISK can be of great help. You can concentrate on the quality of the model and input data and let @RISK deal with the simulation and generation of reports.

» More about Palisade Custom Development

Dr. Javier Ordóñez
Director of Custom Development