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The investor interest in multi-regional earthquake bonds may drop because high-risk locations are less appealing to investors than low-risk ones. Furthermore, a single parameter (earthquake magnitude) cannot accurately express the severity due to an earthquake. Therefore, the aim of this research is to propose valuing a framework for single earthquake bonds (SEB) using a double parameter trigger type, namely magnitude and depth of earthquakes, based on zone division according to seismic information. The zone division stage is divided into two stages. The first stage is to divide the covered area based on regional administrative boundaries and clustering based on the earthquake disaster risk index (EDRI), and the second stage involves clustering based on magnitude and depth of earthquakes and distance between earthquake events using the K-Means and K-Medoids algorithms. The distribution of double parameter triggers is modeled using the Archimedean copula. The result obtained is that the price of SEB based on the clustering result of EDRI categories and K-Means is higher than the price obtained by clustering EDRI categories and K-Medoids with maturities of less than 5 years. The result of this research is expected to assist the Special Purpose Vehicle in determining the price of SEB.

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time.

The variety of catastrophe bond issuances can be used for portfolio diversification. However, the structure of catastrophe bonds differs from traditional bonds in that the face value and coupons depend on triggering events. This study aims to build a diversification strategy model framework using probabilistic–possibilistic bijective transformation (PPBT) and credibility measures in fuzzy environments based on the payoff function. The stages of modeling include identifying the trigger distribution; determining the membership degrees for the face value and coupons using PPBT; calculating the average face value and coupons using the fuzzy quantification theory; formulating the fuzzy variables for the yield; defining the function of triangular fuzzy membership for the yield; defining the credibility distribution for the triangular fuzzy variables for the yield; determining the expectation and total variance for the yield; developing a model of the catastrophe bond diversification strategy; the numerical simulation of the catastrophe bond strategy model; and formulating a solution to the simulation model of the diversification strategy using the sequential method, quadratic programming, transformation, and linearization techniques. The simulation results show that the proposed model can overcome the self-duality characteristic not possessed by the possibilistic measures in the fuzzy variables. The results obtained are expected to contribute to describing the yield uncertainty of investing in catastrophe bond assets so that investors can make wise decisions.

Most economic and financial time series are trended and not stationary and therefore the raw data need to be detrended by differencing. An appropriate and efficient model is a good practice for evaluating the stock price performance. This paper utilized the Box-Jenkins methodology for modeling the stock price for the healthcare sector in the Malaysian stock market. This study focused on one shariah compliant company, namely Hartalega Holdings Berhad, which is one of the major healthcare companies that manufactured clinical Nitrile gloves in Malaysia. The data of monthly stock price is collected from April 2008 until December 2020. The findings of this study showed that ARIMA(2,1,0) is the best model to represent the time series data according to the smallest values of AIC and SC as well as the satisfaction of Ljung-Box test statistics. Through this study, investors able to monitor the investment portfolio from estimating the stock price performance of healthcare sector, while considering the current healthcare economic situation in Malaysia.

Profit is the aims for Islamic banking and conventional banking. Determination of profit in Islamic banking in Malaysia depends on the profit rate, whereas profit rate is essentially from reference rate which is known as the base rate (BR). However, the determination of the components contained in the BR such as benchmark cost of funds and the statutory reserve requirement (SRR) is non-compliance with the Shariah because its directly proportional to the overnight policy rate (OPR). Therefore, an alternative formula for the profit rate are proposed which is known as the base profit rate (BPR). Construction of BPR formula is based on the principle that are more Shariah-compliant.

Earthquake catastrophe bond pricing models (ECBPMs) employ extreme value theory (EVT) to predict severe losses, although studies on EVT’s use in ECBPMs are still rare. Therefore, this study aimed to use a mini-review approach (MRA) to examine the use of EVT and identify the gaps and weaknesses in the methods or models developed. The MRA stages include planning, search and selection, analysis, and interpretation of the results. The selection results showed five articles regarding the application of EVT in ECBPMs. Furthermore, the analysis found the following: First, the generalized extreme value (GEV) could eliminate extreme data in a period. Second, the trigger model using two parameters is better than one, but the study did not discuss the joint distribution of the two parameters. Third, the autoregressive integrated moving average (ARIMA) allows negative values. Fourth, Cox–Ingersoll–Ross (CIR) in-coupon modeling is less effective in depicting the real picture. This is because it has a constant volatility assumption and cannot describe jumps due to monetary policy. Based on these limitations, it is hoped that future studies can develop an ECBPM that reduces the moral hazard.

A rational speculative bubble is a surge in asset prices that exceed its intrinsic value. Rational speculative bubbles are among the ascription which may lead to the collapse of an economic system. Rational speculative bubble cannot be created but it comes into existence when assets started to be traded. Financial rational speculative bubble and burst have negative effect on the economy and markets. Financial rational speculative bubbles are difficult to detect. This study aims to shows the size of rational speculative bubble in four markets, which are gold, Hang Seng, S&P500 and Nikkei 225 during year 2008 to 2016. In this study, generalized Johansen-Ledoit-Sornette model are used to find the size of the rational speculative bubble. Bubble detection is important for both sides of macro-economic decision makers and to the trader. Especially for a trading system that requires detailed knowledge about the time and the stage of the bubble burst.

The issuance of multiple-event catastrophe bonds (MECBs) has the potential to increase in the next few years. This is due to the increasing trend in the frequency of global catastrophes, which makes single-event catastrophe bonds (SECBs) less relevant. However, there are obstacles to issuing MECBs since the pricing framework is still little studied. Therefore, this study aims to develop such a new pricing framework. The model uniquely involves three new variables: the trigger event correlation, interest, and inflation rates. The trigger event correlation rate was accommodated by the involvement of the copula while the interest and inflation rates were simultaneously considered using an integrated autoregressive vector stochastic model. After the model was obtained, the model was simulated on storm catastrophe data in the United States. Finally, the effect of the three variables on MECB prices was also analyzed. The analysis results show that the three variables make MECB prices more fairly than other models. This research is expected to guide special purpose vehicles to set fairer MECB prices and can also be used as a reference for investors in choosing MECBs based on the rates of trigger event correlation and the real interest they can expect.

According to estimates from the International Agency for Research on Cancer (IARC) in 2012, there were 14.1 million new cancer cases and 8.2 million cancer deaths worldwide [1]. Globally, the incidence of oral cancer holds the eighth position and show epidemiologic variability according to different geographic regions. Besides the proven causative agents like tobacco, alcohol, and human papilloma virus, there are certainly other factors that play a significant rolein the selection of treatment strategies and determination of prognosis in OSCC patients. Betel quid is the second factor that contributing most to the time survival. Since we consider all four independent variables (Fig. 4) as inputs for the MLFF model, then input nodes are four nodes and as survival time is considered as the output, then the output node is one. [...]the appropriate neural network architecture which results in the best multilayer feedforward neural network model for our case can be represented as follows: ...

According to Cox in 1972, the prognostic model of survival has been used rather extensively in biomedicine and engineering [3]. To proof that, a result based on breast and prostate cancer data has indicated that the FK-NN-based method yields the highest predictive accuracy and also has produced a more reliable prognostic marker model by combining both statistical and artificial neural-network-based methods [6]. In 1995, a survival model was developed using the following predictor variables: diagnosis, age, number of days in the hospital before study entry, presence of cancer, neurologic function, and 11 physiologic measures recorded on day 3 after study entry. In 1985, Chen and George [8] investigated the stability of a stepwise selection procedure in the framework of the Cox proportional hazard regression model based on bootstrap resampling procedure.