Quantitative risk measures applied to Alaskan commercial fisheries

2012 ◽  
Vol 69 (3) ◽  
pp. 487-498 ◽  
Author(s):  
Suresh Andrew Sethi ◽  
Michael Dalton ◽  
Ray Hilborn
Keyword(s):  
Bering Sea ◽  
Risk Measures ◽  
Commercial Fisheries ◽  
Random Forest Regression ◽  
Price Variability ◽  
Investment Theory ◽  
Quantitative Risk Measures ◽  
Access Privileges ◽  
Revenue Risks ◽  
The Relationship

Risk measures can summarize the complex variability inherent in fisheries management into simple metrics. We use quantitative risk measures from investment theory to analyze catch and revenue risks for 90 commercial fisheries in Alaska, USA, nearly a complete census. We estimate the relationship between fishery characteristics and catch risk using nonparametric random forest regression to identify attributes associated with high or low risks. Catch and revenue risks for individual Alaskan fisheries are substantial and are higher than risks for farmed food alternatives. Revenue risks are greater than catch risks for most fisheries, indicating that price variability is an additional source of risk to fishermen. Regression results indicate that higher productivity species tend to be higher risk, and there is an increasing gradient of risk moving north and west across Alaskan waters, with the remote western Bering Sea fisheries tending to have the highest risks. Low risk fisheries generally have large catches and support larger fleets. Finally, fisheries with greater catch history under some form of dedicated access privileges tend to have lower catch risks.

ASSOCIATION BETWEEN BERG BALANCE, PHYSIOLOGICAL PROFILE ASSESSMENT AND PHYSICAL ACTIVITY, PHYSICAL FUNCTION AND BODY COMPOSITION: A CROSS-SECTIONAL STUDY

2015 ◽  
pp. 1-7
Author(s):  
D.J. SMEE ◽  
H.L. BERRY ◽  
G. WADDINGTON ◽  
J. ANSON
Keyword(s):  
Body Composition ◽  
Physical Function ◽  
Risk Measures ◽  
Berg Balance Scale ◽  
Cross Sectional ◽  
Physiological Profile ◽  
Falls Risk ◽  
Balance Scale ◽  
The Relationship ◽  
Physiological Profile Assessment

Background: Falls are of great concern to older adults and costly to the health system. In addition the relationship between falls risk and falls risk predictor characteristics is complex. Objective: This study aimed to explore the relationship between two objective fall-risk measures tools, the Physiological Profile Assessment and the Berg Balance Scale and to determine how an individual’s sex, level of physical function, health-related and body composition characteristics impact these objective falls risk measures. Design: A cross-sectional, observational study. Participants: 245 community-dwelling older adults (M age=68.12 years, SD=6.21; 69.8% female). Measurements: Participants were assessed for falls-risk (Physiological Profile Assessment and the Berg Balance Scale), physical activity, physical functional and body composition characteristics. Pearson product-moment correlation coefficients were calculated to examine bivariate relationships and hierarchical multiple linear regression modelling was used to estimate the contribution of each predictor in explaining variance in falls-risk. Results: In females, there was a weak association between the two objective falls-risk measures (r =-0.17 p <0.05). The number of falls in the previous 12 months explained 6% of variance in Physiological Profile Assessment scores, with bone density of the lumbar spine contributing a further 1%. In males and females, variance in the Berg Balance Scale showed that age (25%) and physical function (16% for females, 28% for males) contributed significantly to the explaining variance in the falls-risk measure. Conclusion: Sex differences are apparent and as such males and females should be assessed (and potentially treated) differently with regards to falls risk. Results indicate that both falls risk assessment tools measure aspects of balance but are not interchangeable. The Berg Balance Scale may be more discriminative in older, less functioning adults and the Physiological Profile Assessment is more useful in assessing falls risk in females.

An overview of quantitative risk measures for loss of life and economic damage

2003 ◽  
Vol 99 (1) ◽  
pp. 1-30 ◽  
Author(s):  
S.N. Jonkman ◽  
P.H.A.J.M. van Gelder ◽  
J.K. Vrijling
Keyword(s):  
Risk Measures ◽  
Economic Damage ◽  
Loss Of Life ◽  
Quantitative Risk Measures

Predictors of Adherence in the Rehabilitation of Athletic Injuries: An Application of Personal Investment Theory

1989 ◽  
Vol 11 (4) ◽  
pp. 367-381 ◽  
Author(s):  
Joan L. Duda ◽  
Alison E. Smart ◽  
Marlene K. Tappe
Keyword(s):  
Sense Of Self ◽  
Athletic Injury ◽  
Intercollegiate Athletes ◽  
Injury Rehabilitation ◽  
Investment Theory ◽  
Personal Investment ◽  
Rehabilitation Adherence ◽  
Personal Investment Theory ◽  
The Relationship ◽  
Predictors Of Adherence

This study examined the relationship between the three facets of subjective meaning—personal incentives, sense of self, and perceived behavioral options—and adherence behaviors in the athletic injury rehabilitation setting. Subjects were 40 intercollegiate athletes who had sustained a sport related injury; all completed a questionnaire assessing the three components of meaning specific to sport and injury rehabilitation. Adherence was defined as a composite of attendance at the prescribed sessions, degree of completion of the prescribed exercise protocol, and the athlete's intensity or effort exerted in performing the prescribed exercise. Multiple-regression analyses indicated that each dimension significantly predicted adherence behaviors. Athletes who demonstrated greater adherence believed in the efficacy of the treatment, perceived more social support for their rehabilitation, were more goal directed or self-motivated, and placed more emphasis on mastery or task-involved goals in sport.

Weighted Scoring Rules and Convex Risk Measures

2022 ◽  
Author(s):  
Zachary J. Smith ◽  
J. Eric Bickel
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Mathematical Finance ◽  
Scoring Rules ◽  
Convex Risk Measures ◽  
University Of Texas ◽  
Probabilistic Forecasts ◽  
Expected Utility Maximization ◽  
The University ◽  
The Relationship

In Weighted Scoring Rules and Convex Risk Measures, Dr. Zachary J. Smith and Prof. J. Eric Bickel (both at the University of Texas at Austin) present a general connection between weighted proper scoring rules and investment decisions involving the minimization of a convex risk measure. Weighted scoring rules are quantitative tools for evaluating the accuracy of probabilistic forecasts relative to a baseline distribution. In their paper, the authors demonstrate that the relationship between convex risk measures and weighted scoring rules relates closely with previous economic characterizations of weighted scores based on expected utility maximization. As illustrative examples, the authors study two families of weighted scoring rules based on phi-divergences (generalizations of the Weighted Power and Weighted Pseudospherical Scoring rules) along with their corresponding risk measures. The paper will be of particular interest to the decision analysis and mathematical finance communities as well as those interested in the elicitation and evaluation of subjective probabilistic forecasts.

SET-VALUED LAW INVARIANT COHERENT AND CONVEX RISK MEASURES

2019 ◽  
Vol 22 (03) ◽  
pp. 1950004 ◽  
Author(s):  
YANHONG CHEN ◽  
YIJUN HU
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Convex Risk Measures ◽  
New Class ◽  
The Relationship ◽  
Distortion Risk Measures

In this paper, we investigate representation results for set-valued law invariant coherent and convex risk measures, which can be considered as a set-valued extension of the multivariate scalar law invariant coherent and convex risk measures studied in the literature. We further introduce a new class of set-valued risk measures, named set-valued distortion risk measures, which can be considered as a set-valued version of multivariate scalar distortion risk measures introduced in the literature. The relationship between set-valued distortion risk measures and set-valued weighted value at risk is also given.

SEARCHING FOR THE OPTIMAL LEVEL: INFLATION AND PRICE VARIABILITY IN TURKEY

2019 ◽  
pp. 1-19
Author(s):  
HATICE KARAHAN ◽  
M. EGE YAZGAN
Keyword(s):  
Functional Form ◽  
Optimal Level ◽  
Parametric Estimation ◽  
Relative Price ◽  
Price Variability ◽  
Relative Price Variability ◽  
Price Data ◽  
Large Fluctuations ◽  
Main Item ◽  
The Relationship

This paper explores the relationship between inflation and relative price variability (RPV) in Turkey for the period 2004–2017 to shed further light on the issue with relatively recent data. For this purpose, we use monthly price data for 12 main item groups and 414 specific items thereof. Analyses show that RPV for the period of interest exhibits large fluctuations, being particularly salient in the categories of communications and food. Regarding the underlying functional form, semi-parametric estimation results indicate a U-shaped relationship between inflation and RPV, where the latter reaches its minimum at an inflation level close to 8%.

scholarly journals Revisiting the relationship between different financial risk measures and the market return on ordinary shares in South Africa

2015 ◽  
Vol 18 (2) ◽  
pp. 218-231
Author(s):  
Elda Du Toit
Keyword(s):  
South African ◽  
Financial Risk ◽  
Positive Relationship ◽  
Risk Measures ◽  
The United States ◽  
Expected Return ◽  
The Difference ◽  
The One ◽  
The Relationship ◽  
A Share

The main aim of this study was to test whether there is a positive relationship between different financial risk measures and the expected return of a share. This study was performed in 1995 by Brümmer and Wolmarans, who obtained results contrary to those of a similar study in the United States of America in 1988. The reasons for the difference were not established. This study follows up the one by Brümmer and Wolmarans to determine whether the passing of 19 years could have brought about any difference in the results. This process was initiated by testing a set of variables from a sample size of 107 JSE-listed companies from 2002 to 2012 for linearity. As there was no such linear relationship between any of the variables, no assumptions can be made about any relationship between share return and the risk measures tested here. If investors were risk averse, one would expect a positive relationship between different financial risk measures and the expected return of a share. This is not the case in the South African market.

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