Abstract: Chain Ladder (CL) method, Expected Loss Ratio (ELR) method and Bornhuetter-Ferguson (BF) method, in addition to more complex transition-rate modeling, are commonly used actuarial reserving methods in general insurance. There is limited published research about their relative performance in the context of Mortgage Insurance (MI). In our experience, these traditional techniques pose unique challenges and do not provide stable claim estimates for medium to longer term liabilities. The relative strengths and weaknesses among various alternative approaches revolve around: stability in the recent loss development pattern, sufficiency and reliability of loss development data, and agreement/disagreement between reported losses to date and ultimate loss estimate. CL method results in volatile reserve estimates, especially for accident periods with little development experience. The ELR method breaks down especially when ultimate loss ratios are not stable and predictable. While the BF method provides a good tradeoff between the loss development approach (CL) and ELR, the approach generates claim development and ultimate reserves that are disconnected from the ever-to-date (ETD) development experience for some accident years that have more development experience. Further, BF is based on subjective a priori assumption. The fundamental shortcoming of these methods is their inability to model exogenous factors, like the economy, which impact various cohorts at the same chronological time but at staggered points along their life-time development. This paper proposes an alternative approach of parametrizing the loss development curve and using logistic regression to generate the ultimate loss estimate for each homogeneous group (accident year or delinquency period). The methodology was tested on an actual MI claim development dataset where various cohorts followed a sigmoidal trend, but levels varied substantially depending upon the economic and operational conditions during the development period spanning over many years. The proposed approach provides the ability to indirectly incorporate such exogenous factors and produce more stable loss forecasts for reserving purposes as compared to the traditional CL and BF methods.
Abstract: The assessment of the risk posed by a borrower to a
lender is one of the common problems that financial institutions have
to deal with. Consumers vying for a mortgage are generally
compared to each other by the use of a number called the Credit
Score, which is generated by applying a mathematical algorithm to
information in the applicant’s credit report. The higher the credit
score, the lower the risk posed by the candidate, and the better he is
to be taken on by the lender. The objective of the present work is to
use fuzzy logic and linguistic rules to create a model that generates
Credit Scores.
Abstract: Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
Abstract: Throughout this paper, a relatively new technique, the Tabu search variable selection model, is elaborated showing how it can be efficiently applied within the financial world whenever researchers come across the selection of a subset of variables from a whole set of descriptive variables under analysis. In the field of financial prediction, researchers often have to select a subset of variables from a larger set to solve different type of problems such as corporate bankruptcy prediction, personal bankruptcy prediction, mortgage, credit scoring and the Arbitrage Pricing Model (APM). Consequently, to demonstrate how the method operates and to illustrate its usefulness as well as its superiority compared to other commonly used methods, the Tabu search algorithm for variable selection is compared to two main alternative search procedures namely, the stepwise regression and the maximum R 2 improvement method. The Tabu search is then implemented in finance; where it attempts to predict corporate bankruptcy by selecting the most appropriate financial ratios and thus creating its own prediction score equation. In comparison to other methods, mostly the Altman Z-Score model, the Tabu search model produces a higher success rate in predicting correctly the failure of firms or the continuous running of existing entities.
Abstract: Sub-prime mortgage crisis which began in the US is
regarded as the most economic crisis since the Great Depression in the
early 20th century. Especially, hidden problems on efficient operation
of a business were disclosed at a time and many financial institutions
went bankrupt and filed for court receivership. The collapses of
physical market lead to bankruptcy of manufacturing and construction
businesses. This study is to analyze dynamic efficiency of construction
businesses during the five years at the turn of the global financial
crisis. By discovering the trend and stability of efficiency of a
construction business, this study-s objective is to improve
management efficiency of a construction business in the
ever-changing construction market. Variables were selected by
analyzing corporate information on top 20 construction businesses in
Korea and analyzed for static efficiency in 2008 and dynamic
efficiency between 2006 and 2010. Unlike other studies, this study
succeeded in deducing efficiency trend and stability of a construction
business for five years by using the DEA/Window model. Using the
analysis result, efficient and inefficient companies could be figured
out. In addition, relative efficiency among DMU was measured by
comparing the relationship between input and output variables of
construction businesses. This study can be used as a literature to
improve management efficiency for companies with low efficiency
based on efficiency analysis of construction businesses.
Abstract: Reasonably priced and well-constructed housing must
be an integral and element supporting a healthy society. The absence
of housing everyone in society can afford negatively affects the
people's health, education, ability to get jobs, develop their
community. Without access to decent housing, economic
development, integration of immigrants and inclusiveness, the society
is negatively impacted. Canada has a sterling record in creating
housing compared to many other nations around the globe. Canadian
housing gets support from a mature and responsive mortgage network
and a top-quality construction industry as well as safe and excellent
quality building materials that are readily available. Yet 1.7 million
Canadian households occupy substandard abodes. During the past
hundred years, Canada's government has made a wide variety of
attempts to provide decent residential facilities every Canadian can
afford. Despite these laudable efforts, today Canada is left with
housing that is inadequate for many Canadians. People who own their
housing are given all kinds of privileges and perks, while people with
relatively low incomes who rent their apartments or houses are
discriminated against.
To help solve these problems, zoning that is based on an
"inclusionary" philosophy is tool developed to help provide people
the affordable residences that they need. No, thirty years after its
introduction, this type of zoning has been shown effective in helping
build and provide Canadians with a houses or apartments they can
afford to pay for. Using this form of zoning can have different results
+depending on where and how it is used. After examining Canadian
affordable housing and four American cases where this type of
zoning was enforced in the USA, this makes various
recommendations for expanding Canadians' access to housing they
can afford.
Abstract: This conference paper discusses a risk allocation problem for subprime investing banks involving investment in subprime structured mortgage products (SMPs) and Treasuries. In order to solve this problem, we develop a L'evy process-based model of jump diffusion-type for investment choice in subprime SMPs and Treasuries. This model incorporates subprime SMP losses for which credit default insurance in the form of credit default swaps (CDSs) can be purchased. In essence, we solve a mean swap-at-risk (SaR) optimization problem for investment which determines optimal allocation between SMPs and Treasuries subject to credit risk protection via CDSs. In this regard, SaR is indicative of how much protection investors must purchase from swap protection sellers in order to cover possible losses from SMP default. Here, SaR is defined in terms of value-at-risk (VaR). Finally, we provide an analysis of the aforementioned optimization problem and its connections with the subprime mortgage crisis (SMC).