When it comes to oil price spikes, those in oil trading have a sixth sense for picking out the vulnerabilities of assets and products tied to those prices. All kinds of things happen when oil prices jump: gold looks more attractive, demand for natural gas increases, political instability increases, the dollar drops in value, food costs increase, and on and on. We see the consequences on exchanges and feel the results in our wallets, but we don’t often see the effects of oil price spikes. Enter risk mapping, or risk visualization.
Here’s a basic, self-explanatory risk map:
http://www.wku.edu/Dept/Support/FinAdmin/RISK%20MAPPING.pdf
But the risk map above is dull—we live in a Web 2.0 world, we like dynamics in our visualizations. Kudos, then, to the World Economic Forum for taking a stab at enhancing the risk visualization experience with a tool that highlights “risk networks.” Although the information is incomplete (if not downright tantalizing) for our purposes, it is still a fun little gadget (here) to use to determine—from a bird’s eye view—the quality of relationships between risk factors, or nodes. In WEF’s risk network, choosing a node allows one to view details (on the “risk landscape”) of its probability (given as a percentage), severity (in costs of USD billions) and, in an added dimension to the traditional risk map, the interconnectedness to other risks (by strength). The map is colorful, connections are clearly delineated, and severity and significance are clearly indicated with circles and bold lines: the thicker the lines, the larger the circles, the bigger the risks.
For example, choosing “Iran” tells us that there are very risky connections to “asset price collapse,” “Afghanistan instability,” “financial crises,” and more. But what the WEF means by “Iran” or “financial crises” and the other nodes is only vaguely defined (the tantalizing part of the tool). “Iran” is defined simply as: “Iran’s nuclear programme and its role in the Middle East increases instability and tensions regionally and internationally.” The riskiest node connected to “Iran,” in terms of severity and likelihood, is “asset price collapse.”
Five categories, or domains, of risk opportunities make up WEF’s risk network: economics, geopolitics, environment, society, and technology. Nodes within these domains include “Iraq,” “Migration,” “Major fall in the US $,” and “Data fraud/loss,” to name just four.
For fun, I chose to examine the effects of “Oil price spikes.” The WEF explains the node “oil price spikes” as “sharp and/or sustained oil prices increases/places further economic pressures on highly oil dependent industries and consumers, as well as raising geopolitical tensions.” So, according to WEF, where are the associated risks when this phenomenon is in play? Would you have surmised that oil price spikes correlate strongly with an increase in “Underinvestment in infrastructure”? What are the reasons for this? Likewise, the factor has an intense effect on “Major fall in the US $,” “Iran,” and “CII breakdown.”
Conversely, oil price spikes have weaker correlative effects on other phenomena, like “air pollution,” (higher oil price = less travel), “migration” (because people can’t afford the oil to power their transportation?), and “nuclear proliferation” (seems odd, seeing as how high oil prices might increase a country’s desire to build nuclear plants…).
World Economic Forum
The fun part of the map is being able to click through the risk networks. Thus, with “Oil price spikes” in the center (above), we can see a semi-strong correlation with “Underinvestment in infrastructure.” Click on the latter, the circles wiz around, and voila! A new visualization appears and we see that “coastal flooding” becomes a very serious concern. Click on “coastal flooding,” and… you get the picture.
Back to the oil price spikes. We can see from the risk network that many risk elements correlate strongly with oil price spikes: fiscal crises, major fall in the US dollar, Iran, Iraq, underinvestment in infrastructure, slowing Chinese economy, food price volatility, and a few more.
Take, for example, “Asset price collapse.” WEF defines it as: “A collapse of real and financial assets in advanced and emerging market economies leads to the destruction of wealth, deleveraging, reduced household spending and demand.” Much of this appears obvious. When consumers must spend more of their income filling up the gas tank, they’re going to have less money to shop at Wal-Mart. Take that thinking up a level and you have the same problem with companies: when companies have to spend more to transport their goods, they have less capital to re-invest in expanding business and infrastructure. Ironically, deleveraging can work to minimize a firm’s risk by paying off debts, but what are the specific dots connecting oil price spikes to deleveraging? There’s some homework for you.
And what about another consequence of oil price spikes, “Retrenchment from globalization (emerging)”? Again, the WEF: “Multiple emerging economies adopt policies that create barriers to flows of goods, capital and labour and fail to engage with multilateral governance structures to address global challenges.” Ah, yes. The higher the price of oil, which comes from just a handful of countries, forces many without the fuel source to put up their defenses, to look out for number one. In extreme cases, an emerging economy may even choose to nationalize oil companies doing business within its borders in order to get a grip on pricing and distribution, among a myriad of other possible economic strategies. But again, the steps between these causes and effects are decidedly lacking in WEF’s risk network. More homework!
Food for thought
Clearly the WEF’s risk network is insufficient for risk managers to use in everyday situations. They don’t even delve into the nuances of financial trading instruments, hedging scenarios and so forth. But that’s not the purpose of bringing it to light. The point of this post is to get risk managers thinking about various ways to map risks, to better see the various risks and measure their correlation to other risks.
It’s a project that has been on the back of my mind for a few months now. How do we in energy risk management visually convey various risk exposures? Is it with a simple X-Y axis graph, like the one above? Or are energy risk managers utilizing innovative methods from outside the traditional risk management network to get a better grip on vulnerabilities? And in doing so, do they create a-HA! moments?
What did WEF get right with this matrix? What risk elements associated with oil price spikes are missing? As noted, the financial side of this is clearly absent. But so are some physical operations, like pipeline risk. What else? Credit freezes, which lead to underinvestment in infrastructure? Precious metals pricing perhaps? Increases in the price of gold often correlate with the price of oil. Higher oil prices means more investment in precious metals which means mining ventures continue to operate, sometimes in marginal, risky countries, which may also have adverse environmental and political effects, and on and on and on.
Can we map that?
What elements would you add? (The possibilities are endless.) Could an energy risk manager effectively (and visually) network physical risk (e.g. pipeline operations) AND financial risk (e.g. volatility)?
Is it helpful to be able to visualize risks? Does doing so add a measure of risk awareness?
Is the WEF model helpful? What other risk visualization models are helpful?
Read more >>
Here’s a basic, self-explanatory risk map:
http://www.wku.edu/Dept/Support/FinAdmin/RISK%20MAPPING.pdfBut the risk map above is dull—we live in a Web 2.0 world, we like dynamics in our visualizations. Kudos, then, to the World Economic Forum for taking a stab at enhancing the risk visualization experience with a tool that highlights “risk networks.” Although the information is incomplete (if not downright tantalizing) for our purposes, it is still a fun little gadget (here) to use to determine—from a bird’s eye view—the quality of relationships between risk factors, or nodes. In WEF’s risk network, choosing a node allows one to view details (on the “risk landscape”) of its probability (given as a percentage), severity (in costs of USD billions) and, in an added dimension to the traditional risk map, the interconnectedness to other risks (by strength). The map is colorful, connections are clearly delineated, and severity and significance are clearly indicated with circles and bold lines: the thicker the lines, the larger the circles, the bigger the risks.
For example, choosing “Iran” tells us that there are very risky connections to “asset price collapse,” “Afghanistan instability,” “financial crises,” and more. But what the WEF means by “Iran” or “financial crises” and the other nodes is only vaguely defined (the tantalizing part of the tool). “Iran” is defined simply as: “Iran’s nuclear programme and its role in the Middle East increases instability and tensions regionally and internationally.” The riskiest node connected to “Iran,” in terms of severity and likelihood, is “asset price collapse.”
Five categories, or domains, of risk opportunities make up WEF’s risk network: economics, geopolitics, environment, society, and technology. Nodes within these domains include “Iraq,” “Migration,” “Major fall in the US $,” and “Data fraud/loss,” to name just four.
For fun, I chose to examine the effects of “Oil price spikes.” The WEF explains the node “oil price spikes” as “sharp and/or sustained oil prices increases/places further economic pressures on highly oil dependent industries and consumers, as well as raising geopolitical tensions.” So, according to WEF, where are the associated risks when this phenomenon is in play? Would you have surmised that oil price spikes correlate strongly with an increase in “Underinvestment in infrastructure”? What are the reasons for this? Likewise, the factor has an intense effect on “Major fall in the US $,” “Iran,” and “CII breakdown.”
Conversely, oil price spikes have weaker correlative effects on other phenomena, like “air pollution,” (higher oil price = less travel), “migration” (because people can’t afford the oil to power their transportation?), and “nuclear proliferation” (seems odd, seeing as how high oil prices might increase a country’s desire to build nuclear plants…).
World Economic ForumThe fun part of the map is being able to click through the risk networks. Thus, with “Oil price spikes” in the center (above), we can see a semi-strong correlation with “Underinvestment in infrastructure.” Click on the latter, the circles wiz around, and voila! A new visualization appears and we see that “coastal flooding” becomes a very serious concern. Click on “coastal flooding,” and… you get the picture.
Back to the oil price spikes. We can see from the risk network that many risk elements correlate strongly with oil price spikes: fiscal crises, major fall in the US dollar, Iran, Iraq, underinvestment in infrastructure, slowing Chinese economy, food price volatility, and a few more.
Take, for example, “Asset price collapse.” WEF defines it as: “A collapse of real and financial assets in advanced and emerging market economies leads to the destruction of wealth, deleveraging, reduced household spending and demand.” Much of this appears obvious. When consumers must spend more of their income filling up the gas tank, they’re going to have less money to shop at Wal-Mart. Take that thinking up a level and you have the same problem with companies: when companies have to spend more to transport their goods, they have less capital to re-invest in expanding business and infrastructure. Ironically, deleveraging can work to minimize a firm’s risk by paying off debts, but what are the specific dots connecting oil price spikes to deleveraging? There’s some homework for you.
And what about another consequence of oil price spikes, “Retrenchment from globalization (emerging)”? Again, the WEF: “Multiple emerging economies adopt policies that create barriers to flows of goods, capital and labour and fail to engage with multilateral governance structures to address global challenges.” Ah, yes. The higher the price of oil, which comes from just a handful of countries, forces many without the fuel source to put up their defenses, to look out for number one. In extreme cases, an emerging economy may even choose to nationalize oil companies doing business within its borders in order to get a grip on pricing and distribution, among a myriad of other possible economic strategies. But again, the steps between these causes and effects are decidedly lacking in WEF’s risk network. More homework!
Food for thought
Clearly the WEF’s risk network is insufficient for risk managers to use in everyday situations. They don’t even delve into the nuances of financial trading instruments, hedging scenarios and so forth. But that’s not the purpose of bringing it to light. The point of this post is to get risk managers thinking about various ways to map risks, to better see the various risks and measure their correlation to other risks.
It’s a project that has been on the back of my mind for a few months now. How do we in energy risk management visually convey various risk exposures? Is it with a simple X-Y axis graph, like the one above? Or are energy risk managers utilizing innovative methods from outside the traditional risk management network to get a better grip on vulnerabilities? And in doing so, do they create a-HA! moments?
What did WEF get right with this matrix? What risk elements associated with oil price spikes are missing? As noted, the financial side of this is clearly absent. But so are some physical operations, like pipeline risk. What else? Credit freezes, which lead to underinvestment in infrastructure? Precious metals pricing perhaps? Increases in the price of gold often correlate with the price of oil. Higher oil prices means more investment in precious metals which means mining ventures continue to operate, sometimes in marginal, risky countries, which may also have adverse environmental and political effects, and on and on and on.
Can we map that?
What elements would you add? (The possibilities are endless.) Could an energy risk manager effectively (and visually) network physical risk (e.g. pipeline operations) AND financial risk (e.g. volatility)?
Is it helpful to be able to visualize risks? Does doing so add a measure of risk awareness?
Is the WEF model helpful? What other risk visualization models are helpful?
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