113,661 research outputs found
Risks of investment in personnel development: evidence from Ukrainian IT companies
In this paper, we examine key factors that influence the risks of investment in the
development of human capital of a firm in the IT sector and estimate their weight
in the overall risk. In particular, we single out the risk of premature voluntary termination
of an employee, the risk of ineffective training, and the risk of a firm’s
incorrect employee development strategy. Moreover, to support management of
the mentioned kinds of risks, we enumerate the factors that influence them and
classify those factors into three main groups: related to the employee, related to
the firm, and related to the external environment. Based on this division, we build
a model for estimating the risks of investing in the development of personnel
using the Analytic Hierarchy Process (AHP)
Analysis of operational risk of banks – catastrophe modelling
Nowadays financial institutions due to regulation and internal motivations care more intensively
on their risks. Besides previously dominating market and credit risk new trend is to handle operational risk systematically. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. First we show the basic features of operational risk and its modelling and regulatory approaches, and after we will analyse
operational risk in an own developed simulation model framework. Our approach is based on the
analysis of latent risk process instead of manifest risk process, which widely popular in risk
literature. In our model the latent risk process is a stochastic risk process, so called Ornstein-
Uhlenbeck process, which is a mean reversion process. In the model framework we define catastrophe as breach of a critical barrier by the process. We analyse the distributions of catastrophe frequency, severity and first time to hit, not only for single process, but for dual process as well. Based on our first results we could not falsify the Poisson feature of frequency, and long tail feature of severity. Distribution of “first time to hit” requires more sophisticated analysis. At the end of paper we examine advantages of simulation based forecasting, and finally we concluding with the possible, further research directions to be done in the future
Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6
A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis
Analytic Models of the ROC Curve: Applications to Credit Rating Model Validation
In this paper, the authors use the concept of the population ROC curve to build analytic models of ROC curves. Information about the population properties can be used to gain greater accuracy of estimation relative to the non-parametric methods currently in vogue. If used properly this is particularly helpful in some situations where the number of sick loans is rather small; a situation frequently met in periods of benign macro-economic background.validation; credit analysis; rating model; ROC; Basel II
Quantifying the Influence of Component Failure Probability on Cascading Blackout Risk
The risk of cascading blackouts greatly relies on failure probabilities of
individual components in power grids. To quantify how component failure
probabilities (CFP) influences blackout risk (BR), this paper proposes a
sample-induced semi-analytic approach to characterize the relationship between
CFP and BR. To this end, we first give a generic component failure probability
function (CoFPF) to describe CFP with varying parameters or forms. Then the
exact relationship between BR and CoFPFs is built on the abstract
Markov-sequence model of cascading outages. Leveraging a set of samples
generated by blackout simulations, we further establish a sample-induced
semi-analytic mapping between the unbiased estimation of BR and CoFPFs.
Finally, we derive an efficient algorithm that can directly calculate the
unbiased estimation of BR when the CoFPFs change. Since no additional
simulations are required, the algorithm is computationally scalable and
efficient. Numerical experiments well confirm the theory and the algorithm
- …