Expected value versus probability of ruin strategies

2005 ◽  
pp. 448-456
Author(s):  
L. C. MacLean ◽  
W. T. Ziemba
Keyword(s):  
Expected Value ◽  
Probability Of Ruin

Optimal reinsurance

1970 ◽  
Vol 7 (01) ◽  
pp. 134-156 ◽  
Author(s):  
P. W. A. Dayananda
Keyword(s):  
Fundamental Principle ◽  
Expected Value ◽  
Risk Theory ◽  
Critical Study ◽  
Insurance Company ◽  
Subsequent Work ◽  
Probability Of Ruin ◽  
Optimal Reinsurance

The fundamental principle underlying insurance is that the expected value of claims is equal to the premium. This was established by Bernoulli [4] in 1738. Subsequent work on the development of ‘risk theory’ led to research concerning the probability of ‘ruin’ of an insurance company. A critical study of these investigations was made by Borch [3] in a recent paper.

Optimal reinsurance

1970 ◽  
Vol 7 (1) ◽  
pp. 134-156 ◽  
Author(s):  
P. W. A. Dayananda
Keyword(s):  
Fundamental Principle ◽  
Expected Value ◽  
Risk Theory ◽  
Critical Study ◽  
Insurance Company ◽  
Subsequent Work ◽  
Probability Of Ruin ◽  
Optimal Reinsurance

The fundamental principle underlying insurance is that the expected value of claims is equal to the premium. This was established by Bernoulli [4] in 1738. Subsequent work on the development of ‘risk theory’ led to research concerning the probability of ‘ruin’ of an insurance company. A critical study of these investigations was made by Borch [3] in a recent paper.

The Expected Value Premium

CFA Digest ◽  
2008 ◽  
Vol 38 (3) ◽  
pp. 35-36
Author(s):  
Michael Kobal
Keyword(s):  
Expected Value ◽  
Value Premium

The Expected Value of a Guaranteed Minimum Withdrawal Benefit (GMWB) Annuity Rider

CFA Digest ◽  
2012 ◽  
Vol 42 (1) ◽  
pp. 116-118
Author(s):  
Peter Eickelberg
Keyword(s):  
Expected Value

First-Year Engineering Computing Courses in Canadian Engineering Programs

1969 ◽  
Author(s):  
Sean Maw ◽  
Janice Miller Young ◽  
Alexis Morris
Keyword(s):  
Problem Solving ◽  
Engineering Students ◽  
Expected Value ◽  
Knowledge Development ◽  
First Year ◽  
Recent Past ◽  
Engineering Programs ◽  
Digital Computation ◽  
Pedagogical Philosophies ◽  
Introductory Computing

Most Canadian engineering students take a computing course in their first year that introduces them to digital computation. The Canadian Engineering Accreditation Board does not specify the language(s) that can or should be used for instruction. As a result, a variety of languages are used across Canada. This study examines which languages are used in degree-granting institutions, currently and in the recent past. It also examines why institutions have chosen the languages that they currently use. In addition to the language used in instruction, the types and hours of instruction are also analyzed. Methods of instruction and evaluation are compared, as well as the pedagogical philosophies of the different programs with respect to introductory computing. Finally, a comparison of the expected value of this course to graduates is also presented. We found a more diverse landscape for introductory computing courses than anticipated, in most respects. The guiding ethos at most institutions is skill and knowledge development, especially around problem solving in an engineering context. The methods to achieve this are quite varied, and so are the languages employed in such courses. Most programs currently use C/C++, Matlab, VB and/or Python.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

Cost management for concrete batch plant using stochastic mathematical models

2006 ◽  
Vol 33 (8) ◽  
pp. 1065-1074 ◽  
Author(s):  
Tarek M Zayed ◽  
Ibrahim A Nosair
Keyword(s):  
Mathematical Models ◽  
Production Efficiency ◽  
Cost Management ◽  
Unit Cost ◽  
Expected Value ◽  
Cost Models ◽  
Productivity Cost ◽  
Batch Plant ◽  
Factor Share ◽  
Construction Operation

Assessing productivity, cost, and delays are essential to manage any construction operation, particularly the concrete batch plant (CBP) operation. This paper focuses on assessing the above-mentioned items for the CBP using stochastic mathematical models. It aims at (i) identifying the potential sources of delay in the CBP operation; (ii) assessing their influence on production, efficiency, time, and cost; and (iii) determining each factor share in inflating the CBP concrete unit expense. Stochastic mathematical models were designed to accomplish the aforementioned objectives. Data were collected from five CBP sites in Indiana, USA, to implement and verify the designed models. Results show that delays due to management conditions have the highest probability of occurrence (0.43), expected value of delay percent (62.54% out of total delays), and relative delay percent. The expected value of efficiency for all plants is 86.53%; however, the average total expense is US$15.56/m3 (all currency are in US$). In addition, the expected value of effective expenses (EE) is $18.03/m3, resulting in extra expenses (XE) of $2.47/m3. This research is relevant to both industry practitioners and researchers. It develops models to determine the effect of delays on concrete unit cost. They are also beneficial to the CBP management.Key words: concrete batch plant, delays, management conditions, cost models, cost management, stochastic mathematical models.

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