Uncertainity And Risk: Literature Review: Frank H. Knight

Risk is defined by the probability of injury, harm, loss or danger. We all take risks every day, and don’t even think about implications.

The idea of “risk” is used in many fields and industries. There has been large efforts made towards the understanding of risk. Since, risk varies so much depending on the field of study, the need for learning about it is warranted. As can be imagined, the importance of risk in a market economy is crucial. In the 1990s, JP Morgan made the Value at Risk (VaR) a central component of its work efforts (Cecilia-Nicoleta, Anne-Marie, & Carmen-Maria, 2011).

Risk: A risk is the chance, high or low, that any hazard will actually cause somebody harm. (the likelihood of it happening).

There are two sorts of uncertainty, which are behavioral and intellectual. Behavioral uncertainty is the level of uncertainty to how individuals will act and subjective

When we measure risk per unit of return, Collections, with its low expected return, becomes the most risky stock. The CV is a better measure of an asset’s stand-alone risk than because CV considers both the expected value and the dispersion of a distribution—a security with a low expected return and a low standard deviation could have a higher chance of a loss than one with a high but a high .

Danielsson and De Vries explain how predictions of low probability, worst-case outcomes are extremely poor using Value at Risk. These, importantly, are the most influential events that deserve the most attention. Such a blind spot is unacceptable. As such, they offer an alternative: an extreme value estimator. When tested against VaR and historical simulations far out in the tails, it performs much better in locating the depths of a negative shock. It charts tails according to particular parametric distributions, “develop[ing] a straightforward rule for obtaining multi-period VaR from the single period VaR.” It is still fraught with several problems, but makes much more realistic assumptions about the distribution of stock market returns. Moreover, it more closely mimics the actual returns of the market.

Barry starts off his explanation by comparing the strength and conviction of uncertainty against the weakness of uncertainty,

When people take risks they are willing to put themselves into situations that they can both lose or gain something valuable. The object can be both tangible and tangible. Uncertainty is when a person is unsure about a situation. An example of uncertainty is when a person is in doubt and not sure how to handle the situation. Risk and uncertainty are separate, but they are typically found within the same situation (McConnell, 2015).

Defined by Coopers textbook, risk is the exposure to the consequences of uncertainty and has two elements: the likelihood of something happening that has an impact on the project objectives, and the positive or negative consequences of something impacting the project objectives (Cooper, Grey, Raymond, & Walker, 2005)

In life, there are many circumstances, in fact multiple in every second we breathe, where we must make a decision or formulate an idea or position on a situation. Within the time of this decision, there comes a point where there is a level of certainty and or doubt that persists. By definition, certainty and doubt are antonyms; however, there can be relationship of certainty and doubt where they are able to coincide with one another in a very fragile state, but as soon as a certain position turns even slightly doubtful, the subject may never claim full certainty. In other words, once doubt is presented, there cannot be total certainty.

There is no single definition of risk. Many insurance authors traditionally have defined risk for uncertainty. A risk is an uncertainty concerning the occurrence of a loss.

Risk can be defined as “The possibility of a (negative) event occurring”. Risk and uncertainty go hand in hand. When you are certain about something that you do then there is less or no risk involved. There is more risk when there is uncertainty about a particular outcome and you still go for it.

This is the first article in a two-part series analyzing the accuracy of risk measurement models. In this first article, we will present an overview of backtesting methods and point out the importance of conducting regular backtests on the risk models being used. In the second article, we will present an alternative to measuring VaR using a top-down or “macro” approach as a complementary tool to traditional risk methodologies.

Central to this is the urge to classify, to create taxonomies so that, even in the instance where deep Uncertainty does emerge, everything else that can be known, is. This is a wider view than many others here, implicit in whose work is the assumption that Knightian Uncertainty is a constant, rather than a symptom of particularly complex environments. It is only through this mapping of classes of unknowns, and the gradual expansion of information about them, that Firoozye and Ariff aver we are able to manage Uncertainty and ‘prepare for foreseeable outcomes’

Concept of risk, risk assessment, risk management and how uncertainty affects the process will be discussed.