Value added risk pdf

From that distribution for 1 P , value-at-risk is calculated, as illustrated in Exhibit 1 above. Exhibit 2 summarizes the components common to all practical value-at-risk measures: We describe those components next. Exhibit 2: All practical value-at-risk measures accept portfolio holdings and historical market data as inputs. They process these with a mapping procedure, inference procedure, and transformation procedure.

Output comprises the value of a value-at-risk metric. That value is the value-at-risk measurement. For example, if a portfolio holds shares of IBM stock, shares of Google stock and a short position of shares of Microsoft stock, its holdings are. The two inputs—historical data and portfolio holdings—are processed separately by two procedures within the value-at-risk measure:.

This is called a primary mapping. If a portfolio is large or holds complex instruments, such as derivatives or mortgage-backed securities, a primary mapping may be computationally expensive to value. Such approximations are called remappings. They can take many forms. Such remappings are called, respectively, linear remappings and quadratic remappings. Most of the literature on value-at-risk is either elementary or theoretical, so remappings receive little mention.

This is unfortunate. As a practical tool for making production value-at-risk measures tractable, remappings can be indispensable. Returning to Exhibit 2, we have discussed the two inputs to a value-at-risk measure as well as the inference procedure and mapping procedure that process these. If you think about it, the two outputs of those procedures correspond to the two components of risk. Prediction of creatinine clear- Conflict of interest ance from serum creatinine.

Nephron ; 31— The authors declare that there is no conflict of interest. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Funding Biometrics ; — The statistical analysis performed by ALP was supported by the Stat Med ; 11— Evaluating the predictiveness References of a continuous marker.

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Eur Heart J ; — Stress hyperglyce- Hyperlycemia at Game-theoretic analyses require model- ing the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis meth- ods are well suited to accomplish these tasks. Our analysis is grounded in the Unified Foundational Ontology UFO [12], which was created with the specific purpose of providing foundations for con- ceptual modeling.

UFO is formally connected to a set of engineering tools including a modeling language OntoUML , as well as a number of methodological e. Research shows that UFO is among the most used foundational ontologies in conceptual modeling and the one with the fastest adoption rate [40]. In particular, we rely on the concepts and relations defined in the Common Ontology of ValuE and Risk COVER [33], a novel well-founded refer- ence ontology grounded on UFO that explains value and risk as dual and intrinsically connected notions.

In this section we briefly discuss these ontological foundations. For an in-depth discussion and formalization, one should refer to, for example, [12, 2, 14, 7, 7]. UFO is the theoretical basis of OntoUML, a language for Ontology-driven Con- ceptual Modeling that has been successfully employed in a number of academic and industrial projects in several domains, such as services, value, petroleum and gas, me- dia asset management, telecommunications, and government [15].

Models created in OntoUML have a clear formal semantics, and a comprehensive support for model veri- fication, validation and code generation, including versions in languages such as OWL [15].

In the sequel, we briefly explain a selected subset of ontological distinctions put forth by the Unified Foundational Ontology, which are relevant to our discussion. UFO makes a fundamental distinction between individuals particulars , and types or universals , i. Individ- uals can be endurants roughly, things or object-like entities and perdurants roughly, events, occurrences, processes.

Substantials are existentially independent ob- jects, such as Mick Jagger, the Moon, the United Nations organization. Moments of type a are termed modes; those of type b are termed relators. Relators are individuals with the power of connecting entities. Furthermore, there is a third sort of moments termed qualities. Qualities are individual moments that can be mapped to some quality space, e.

For this reason, the distinctions of the latter are reflected as modeling primitives mostly stereotyped classes and relations of the former. In On- toUML, the stereotypes «phase», «role» and «roleMixin» represent the respective on- tological types of anti-rigid universals which contingently instantiate their instances : phases are anti-rigid universals with an associated intrinsic contingent instantiation con- dition e.

Furthermore, the stereotype «mixin» is used to represent semi-rigid universals which are necessarily instantiated by some of its instances and contingently instanti- ated by others.

Finally, the stereotype «category» is used to represent rigid universals which necessarily instantiate their instances such as physical object, which aggregates essential properties of tables, cars, books.

The reader interested in more details on the modeling primitives of OntoUML is referred to [14, 7, 1]. COVER is grounded on several theories from marketing, service science, strategy and risk management. It is specified in OntoUML in [12].

This ontology character- izes and integrates different perspectives of value and risk. COVER makes the following ontological commitments on the nature of value: — Value emerges from impacts on goals. Value emerges from events that affect the degree of satisfaction of one or more goals of an agent.

The same object or experience may be valuable to a person and of no value to another. Even though value can be ascribed to objects, it is ultimately grounded on experiences. For instance, in order to explain the value of a smartphone, one must refer to the experiences enabled by it. The value of an object can vary depending on the context in which it is used. This means that an event might be simultaneously considered as a risk by one agent and not as a risk by another it may even be considered as an opportunity by such an agent.

This means that we ultimately ascribe risk to events, not objects. Thus, the risk an object is exposed to may vary even if all its intrinsic properties e. These are classified into Impact and Trigger Events.

The former are those that directly impact a goal or bring about a situation named Impactful Outcome that im- pacts a goal. On contrast, Trigger Events are simply parts of an experience that are identified as causing Impact Events, directly or indirectly.

To formalize goals, COVER reuses the concept of Intention from UFO [12], as a type of mental state that describes a class of state-of-affairs that an agent is committed to bring about.

Risk Experiences focus on unwanted events that have the potential of causing losses and are composed by events of two types, namely threat and loss events. A Threat Event is the one with the potential of causing a loss, which might be intentional or uninten- tional. A Threat Event might be the manifestation of: i a Vulnerability a special type of disposition whose manifestation constitutes a loss or can potentially cause a loss from the perspective of a stakeholder ; or ii a Threatening Capability capabilities are usually perceived as beneficial, as they enable the manifestation of events desired by an agent.

The second mandatory component of a Risk Experience is a Loss Event, which neces- sarily impact intentions in a negative way captured by a Hurts relation between Loss Event and Intention. Similarly, in [32] Sales et al. By deriving patterns from COVER, they provide clear real-world semantics for its constituting elements, thus reducing the ambiguity and conceptual complexity found in previous approaches to model value and risk in the literature.

In the sequel we briefly describe three patterns that are relevant in the context of our paper, namely the Value Experience Pattern, the Experience Valuation Pattern and the Risk Experience Pattern. For a more detailed discussion on value and risk modeling patterns, one should refer to [35] and [32], respectively. Value Experience. This pattern allows modelers to detail experiences that creates value for a given stakeholder.

As shown in Fig. This pattern allows modelers to describe value judgments made towards experiences. According to Sales et al [32] risk experiences focus on unwanted events that have the potential of causing losses and are composed by events of two types, namely threat and loss events. A threat event is the one with the potential of causing a loss. As described in [32], it might be the manifestation of a vulnerability or a threatening capability. The second mandatory component of a risk experience is a loss event, which necessarily impact intentions in a negative way.

For the sake of sim- plicity, we represent here a simplified version of the Risk Experience pattern Fig. Note that, as argued in [34], value can be ascribed to past, actual or envisioned experiences.

Risk, however, is only ascribed to envisioned experiences that may but are not certain to happen [33]. For instance, if a depositor decides to withdraw her money instead of retaining it in the bank, then the action of retaining the money will not happen. The smaller the standard deviation, the closer the probability distribution and thus the lower the risk. According to Mubyarto , the value-added is a value of a product before it is processed by the post-processed per unit.

The results of this study is to analyze the added value of the processing contained oyster mushrooms into nuggets present in SMEs Ogan Ogan Ulu. More detail can be seen in Table 1. Table 1. To get the value added of oyster mushrooms into nuggets then the output value of Rp. An entrepreneur must have a soul to be able to take decisions when difficult though, and able to take risks in a state of uncertainty. How to cope in the face of the risk that there are several things including: understand that the risk is not to become an obstacle to progress.

In detail can be seen in Table 2. Following: Table 2. Judging from the results of the coefficient of variation exists, then the risk analysis that will be faced by SMEs nugget fairly small mushroom is only at 2. Risks faced in terms of costs, marketing, profits are at risk that are not too harmful to SMEs nuggets oyster mushrooms.

Based on the research that has been done, it can be concluded as follows: The processing of oyster mushrooms into nuggets provide the value added was positive and more viable interesting to do. The value addded of processing into nuggets is Rp. Analysis of income risk on SMEs nuggets mushrooms in Ogan Ogan Ulu shows that the risks faced by small, it is shown on the value of the coefficient of variation in the amount of 2. This means that the risk of loss experienced by SMEs nuggets very small mushroom and deserves to be cultivated by Baturaja community.

Suggestion Based on the conclusions of this research, there are some things that need to be advised as follows: 1.

To obtain the optimum gain an entrepreneur both large and small scale should pay attention to the form of value added to be generated, so as to attract consumers with innovative products and packaging. To obtain optimum revenue and reduce the risk of maximum effort is required, such as the strategy undertaken in order to minimize uncertainty in the future. Processing of Food and Agro-industry Jamur.