This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental outcomes. This study built on this literature by investigating how child, parent, and family risk factors predicted school readiness in Head Start children using two statistical models. Specific aims of this study included identifying 1) to what degree multiple predictors contributed to school readiness and 2) to what degree the cumulative risk index contributed to school readiness. Participants included 176 Head Start children ages 3 to 5 years. Data were analyzed using multivariate regression to determine if the cumulative risk model was a stronger predictor of school readiness than any risk factor in isolation. Hierarchical regression was also utilized to determine if individual risk factors contributed anything above and beyond the sum, the cumulative risk index. Multiple regression analysis revealed that older age and previous enrollment in Head Start predicted higher scores, while low income predicted lower scores, as did taking the test in Spanish. Analysis also revealed that higher scores on the cumulative risk index predicted lower test scores. The analysis revealed that the individual risk factors did not contribute to the model above and beyond the cumulative risk index. Adding the individual risk factors did not account for more variance than using gender, age, and the cumulative risk index as the only predictors. Similarly, the cumulative risk index did not account for more variance than using age and gender as the only predictors. The current study adds empirical support to the continued use of the cumulative risk model in predicting adverse developmental outcomes.

This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental outcomes. This study built on this literature by investigating how child, parent, and family risk factors predicted school readiness in Head Start children using two statistical models. Specific aims of this study included identifying 1) to what degree multiple predictors contributed to school readiness and 2) to what degree the cumulative risk index contributed to school readiness. Participants included 176 Head Start children ages 3 to 5 years. Data were analyzed using multivariate regression to determine if the cumulative risk model was a stronger predictor of school readiness than any risk factor in isolation. Hierarchical regression was also utilized to determine if individual risk factors contributed anything above and beyond the sum, the cumulative risk index. Multiple regression analysis revealed that older age and previous enrollment in Head Start predicted higher scores, while low income predicted lower scores, as did taking the test in Spanish. Analysis also revealed that higher scores on the cumulative risk index predicted lower test scores. The analysis revealed that the individual risk factors did not contribute to the model above and beyond the cumulative risk index. Adding the individual risk factors did not account for more variance than using gender, age, and the cumulative risk index as the only predictors. Similarly, the cumulative risk index did not account for more variance than using age and gender as the only predictors. The current study adds empirical support to the continued use of the cumulative risk model in predicting adverse developmental outcomes.

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On a basis of the development of credit risk models, this study aims to help managers of financial institutions understand the development of the models so as to develop their own model that will provide objective and reasonable references for banks to decide the lending rate. Furthermore, this study used "Utilization of Long-Term Funding" as the object and studied individual cases of approved loans. By using risk neutral evaluation method to study the difference between the lending rate of loans and the risk-free interest rate of public bonds, to extract implied probabilities of default and required credit risk premiums form actual market data on interest rates. These credit risk premiums of model were used to be compared with the actual markups of banks and the results are as follows: 1.Most values stated in credit risk premium are lower than the actual markups for banks usually consider the burden of other capital costs and the factor of liquidity premium when they set the rating for markup. 2.After a loan is approved, the assumed recovery rate upon application will adjust according to the market value of the collateral. When the recovery rate decreases, the expected loss rate on the loan will gradually increase. Moreover, the higher the assumed recovery rate, the larger the corrected expected loss rate after the loan is approved. 3.In recent years, the non-performing rate for banks in Taiwan has reached a record high. Even though banks face less credit risks when they make long-term loans in "Utilization of Long-Term Funding", the probability of default has increased in recent years, which has contributed to the increase of expected loss rate on the long-term loan. In sum, banks still face credits risks that should not be ignored when they manage long-term loans. Thus, it is necessary to improve loan review to enhance the quality of loans and to increase the efficiency of utilization of long-term fund.

Coastal flooding, caused by sea level rise (SLR), storm surge, and tropical cyclones, is a growing threat. Previous studies have documented mortality associated with historical coastal flooding and developed predictions of mortality risk based on SLR and human development. This study updates those estimates and provides a new model by including new mortality data from events between 2010 and 2020 and an updated method for estimating the population exposed to coastal flooding events. Primary data sources include the Emergency Events Database (EM-DAT) and the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) model. We first characterize trends in exposed populations and mortality associated with coastal flooding between 1990 and 2020. A mixed effect regression model estimates mortality associated with coastal flooding and investigates the influence of variables including Human Development Index (HDI), country population, and event frequency. The frequency of coastal flooding events between 1990 and 2020 has increased, while there was an overall decrease in recorded deaths associated with coastal flooding events. The association between mortality and coastal flood exposure is reduced in countries with higher populations. This result suggests countries with larger populations may buffer risks in exposed regions. Results showed significant reduction in mortality risk, by approximately 34% (95% CI, 17-47%), associated with an increase of approximately 61 million in country-level population. Additionally, a 7% increase (95% CI, 3-11%) in mortality risk with each additional occurrence of coastal flooding events was observed. By leveraging this knowledge, decision-makers can develop targeted policies and interventions to enhance community preparedness, reduce vulnerability, and ultimately save lives in the face of increasing coastal flooding risks. / Master of Science / This study aims to explore the association between coastal flooding deaths and socio-economic variables globally. Additionally, it seeks to analyze trends in coastal flooding mortality, exposed populations, and flooding frequency across global regions, as well as income regions differentiated by the World Bank, from 1990 to 2020. Coastal flooding mortality data for every coastal flooding event were sourced from EM-DAT, a widely utilized disaster database. We utilized a climate model to retrieve the population exposed to coastal flooding for every event. Human Development Index (HDI) data and country population from 1990 to 2020 were taken from United Nations Development Programme (UNDP) and World Bank databases, respectively. A statistical model was used to estimate mortality risk associated with coastal flooding events and to investigate the influence of variables including Human Development Index (HDI), population, and event frequency. The frequency of coastal flooding events between 1990 and 2020 has increased, while there was an overall decrease in recorded deaths associated with coastal flooding events. The association between mortality and coastal flood exposure is reduced in countries with higher populations. This result suggests countries with larger populations may buffer risks in exposed regions. Results showed significant reduction in mortality risk, by approximately 34% (95% CI, 17-47%), associated with an increase of approximately 61 million in country population. Additionally, a 7% increase (95% CI, 3-11%) in mortality risk with each additional occurrence of a coastal flooding event was observed.

This study constructs a cross-market risk model based upon local multi-factor risk models of Taiwan and China equity markets. We employ world, country, industry, and global risk factors to build a structural model which could explain the relationship between local factors across markets by further decomposing local factor returns. Under the structure, this model allows each local market to adopt different local factors rather than force all local markets to use one parsimonious set of factors. Therefore, this model could provide both in-depth and broad coverage analysis of international equity portfolios. The innovative methodology is first introduced by Barra as the Integrated Model. Moreover, we build a simple portfolio and its corresponding benchmark to illustrate the power of our model. Once the contents of a portfolio are decided, this model could provide not only the risk estimation and decomposition in advance but also the performance attribution compared with the benchmark after the portfolio is realized. The analytical viewpoint could also easily change with different numeraire perspectives. The result demonstrates that this model is practical and flexible for international equity portfolio analysis.

The cumulative risk model is used to explain the coexistence of small gender differences and large health disparities between husbands and wives. Specifically, the current model incorporates conflict (a risk factor), support (a protective factor), and coping (a moderator of the conflict-stress link) to predict cortisol slopes for newlywed husbands and wives. One hundred and seventy-two couples completed both global and daily measures of protective factors (empathy, responsiveness, and perceived support), risk factors (withdrawal, loyalty, self-silencing, and negativity), and coping (self-distraction, substance use, emotional support, and rumination). For the six days that participants provided daily reports of these constructs, participants also provided waking and evening saliva samples for later determination of salivary cortisol levels. I hypothesized that men would incur more protective factors than would women, and that these protective factors would be associated with steeper cortisol slopes (i.e., healthy cortisol slopes.) Further, I hypothesized that women would incur more cumulative risks than would men, and that these risks would be associated flatter cortisol slopes (i.e., unhealthy cortisol slopes). Finally, I hypothesized that the association between cumulative risk and cortisol slopes would be moderated by coping, such that theoretically-effective coping strategies would blunt the impact of cumulative risks whereas ineffective coping strategies would exacerbate the impact of cumulative risks. Support for these hypotheses was mixed. Women did incur fewer cumulative protective factors than did men; however, there were no gender differences in cumulative risks for this highly satisfied newlywed sample. The impact of both cumulative protection and cumulative risk on cortisol slopes differed for men and women. Coping moderated the impact of cumulative risk on daily cortisol slopes, but again these patterns were different for men and women. Future work must continue to isolate gender differences in relationship processes to understand resulting health implications. With further refinement, the proposed model can provide a more holistic explanation of gendered health disparities, and perhaps identify ways that women and men can experience more equivalent health benefits from romantic relationships. / text

Savo darbe mes nagrinėjame Gerber-Shiu baudos funkciją klasikiniame rizikos modelyje atveju, kai žalų dydžiai pasiskirstę pagal Pareto dėsnį. Pagrindinis uždavinys yra susikonstruoti algoritmą funkcijos reikšmių gavimui. Tiriamas Gerber-Shiu diskontuotos baudos funkcijos atvejis, kada vidinė baudos funkcija w tapačiai lygi vienetui. Dėl sudėtingos transformuoto Pareto skirstinio formos analitiškai paskaičiuoti sąsūkų nepavyko. Tam tikslui naudojamas interpoliavimas kubiniu splainu. N kartų kartodami sukonstruotą algoritmą gauname pirmąsias n sąsūkas laisvai pasirinktiems pradiniams parametrams: Pareto skirstinio laipsnio rodikliui α, pradiniam kapitalui u, santykinei draudimo priemokai θ, diskontavimo parametrui (palūkanų normai) δ ir Puasono proceso parametrui λ. Lentelių pagalba parodome funkcijos priklausomybę nuo skirtingų modeliuojančių parametrų reikšmių. Išvadose teigiame jog pasiūlytas metodas skaičiuoti Gerber-Shiu diskontuotos baudos funkciją nors ir išpildomas tačiau yra neefektyvus. Kai kuriais pradinių parametrų pasirinkimo atvejais susiduriama su tikslumo problema. Norint tiksliai paskaičiuoti funkcijos reikšmes reikia didesnių eilių transformuoto Pareto skirstinio sąsūkų, o tam reikalingi dideli resursai. Kita vertus, pradinio kapitalo u reikšmėms didėjant tikslumas didėja ženkliai. / In this paper we consider Gerber-Shiu discounted penalty function in the classical risk model for Pareto claims. Our main goal is to construct an algorithm for obtaining values of the discounted penalty function (considering penalty function w=1). Due to the complicated form of the transformed Pareto distribution function we cannot obtain its convolutions analiticaly. We use numerical methods provided by Maple (cube spline) to find interpolating functions instead. Continuously applying recursive formulas we obtain first 5 interpolated convolutions. Then we calculate values of Gerber-Shiu discounted penalty function for certain arbitrary parameters: α – degree of Pareto distribution function, initial surplus u, security loading θ, discounting parameter δ and Poison process parameter λ. We present data tables and graphs of the discounted penalty function for some variations of parameters in later sections. Finally we state that the method that we use is quite complicated. For better accuracy of the discounted penalty function values one may require to get many convolutions of the transformed Pareto distribution function and that may require too great of the resources. However the quantity of the convolutions needed rapidly decreases for large values of the initial surplus u.

In this Master thesis, we use a singular and regular perturbation theory to derive an analytic approximation formula for the expected discounted penalty function. Our model is an extension of Cramer–Lundberg extended classical model because we consider a more general insurance risk model in which the compound Poisson risk process is perturbed by a Brownian motion multiplied by a stochastic volatility driven by two factors- which have mean reversion models. Moreover, unlike the classical model, our model allows a ruin to be caused either by claims or by surplus’ fluctuation. We compute explicitly the first terms of the asymptotic expansion and we show that they satisfy either an integro-differential equation or a Poisson equation. In addition, we derive the existence and uniqueness conditions of the risk model with two stochastic volatilities factors.

In this thesis, annuity-due and annuity-immediate discrete time risk models are introduced and ruin probabilities in these two models under dependent forces of interest are discussed. Recursive and integral equations for these ruin probabilities are given. Inequalities for the ruin probability estimation are derived by an inductive approach. Finally, an example is given to illustrate the application of these results.