This study proposes a novel approach that investigates the dynamic dependency among exchange rates by extending time-varying copulas' parameters following an autoregressive moving average (ARMA) process. The process consists of an autoregressive part that explains the effect of the previous parameters and a forcing variable that measures the dependence structure between marginal variables. We apply this model to the daily data of the exchange rates of five Asian countries with the strongest economies before and during the 2020 pandemic, namely CNY/USD, IDR/USD, INR/USD, JPY/USD, and KRW/USD. The ARIMA-GARCH model was used to model the exchange rates data and estimate the dynamic dependence using time-varying copulas with the extended parameters. The dynamic dependencies between Chinas and the four countries' exchange rates before and during the 2020 pandemic was evidenced. Moreover, India is the country whose exchange rate has been most strongly affected by the pandemic. Some of the highlights of the proposed approach are: • This paper provides two algorithms to investigate the dynamic dependencies among exchange rates data during a crisis and forecast the data using time-varying copulas with the extended parameters. • There are four extended time-varying copulas' parameters which can measure the dynamic dependencies between variables. • The computation procedure is easy to implement.

In this paper, we will explain critical illness insurance calculations with stochastic interest rates. The survival model is multiple states, while the interest rate is the Cox-Ingersoll-Ross stochastic interest rate model. In determining the survival model, we use the prevalence and mortality rates of certain critical diseases, such as neoplasms, endocrine diseases, and diseases of the digestive system. Furthermore, the Monte Carlo simulation will be used to simulate the possibility of interest rate pathways in determining critical illness insurance premiums.

International Financial Reporting Standard 17 (IFRS 17) is a global accounting standard developed by the International Accounting Standards Board (IASB). It guides the accounting treatment of insurance contracts in financial statements. In IFRS 17, claim reserving is vital for measuring insurance contracts and determining the Contractual Service Margin (CSM). The claim reserving process involves estimating expected claim payouts over the contract’s duration. These estimates are crucial for calculating the liability for incurred claims and determining the CSM, representing unearned profit. Claim data is treated as ordered data by sorting the run-off triangle row-wise, resulting univariate time series with missing values. The state space model provides a framework for modeling time series, assuming system development over time. By taking the log of claim data so that the data is normally distributed, the lognormal state space model, along with Kalman recursion algorithms, can be applied for Incurred but Not Reported (IBNR) projections. The lognormal state space model’s IBNR reserve estimate is more precise and sufficient to compensate loss than the chain ladder method. Furthermore, the performance of the lognormal state space model outperforms the chain ladder method by means of model validation using RMSE, MAE, RSE, and R2.

This paper describes the construction of morbidity-mortality table if the dataset is only available at ages interval. The table used to measure the interaction between morbidity and mortality on the eleven chronical diseases (ECD). The probabilistic framework is based on the multiple-states Markov model. The transition intensities are assumed constant in each age interval. This paper finds high probability someone stays in state ECD and higher probability someone dies because of ECD than other causes.

In this paper we will discuss the dynamic model of Indonesian National Health Insurance (JKN) participation number. In this model, we divide JKN participation types into three categories: PBPU, PBI and PPU. The dynamic model is built using ordinary differential equation (ODE) system from a multiple states model that describes the transition of JKN participation types from time to time. The parameters of the model are estimated from data that is refined by an exponential smoothing time series method. As a result, the estimated number of JKN participation for each type until 2045 is obtained.

This study proposes a multi-objective optimization model for capital allocation in an energy stock portfolio. The model integrates two financial objectives (maximizing return and minimizing value-at-risk) and four environmental objectives (minimizing carbon, energy, water, and waste intensities), providing a more comprehensive representation of corporate environmental performance in the energy sector. A closed-form analytical solution is developed to enhance theoretical clarity, analytical tractability, and interpretability without relying on iterative simulations. Methodologically, the model adopts a weighted utility function approach to aggregate multiple objectives into a single unified function, and the optimal solution is derived using the Lagrange multiplier method. The proposed model is then implemented on Indonesian energy stock data selected based on the lowest aggregate scores of financial and environmental attributes. This selection yields four stocks across three different energy subsectors: oil, gas, and coal. This implementation demonstrates that the optimal portfolio solution is simply and efficiently obtained without the need for iterative numerical approaches. Additionally, this implementation also shows a clear, representative, and rational trade-off between financial aspects and environmental impacts. This study makes a theoretical contribution to the sustainable portfolio literature and has practical implications for investors seeking to balance financial and environmental objectives quantitatively.