International Conference on Actuarial Sciences [ICActS] 2025

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4496

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Author: search.filters.author.Guruge, M. L.

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    PublicationOpen Access
    Modelling the Indicative Rate of the USD/LKR SPOT Exchange Rate in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rajapaksha, R. G. S. N.; Kumarasiri, P. V. A. L.; Sathsarani, T. V. I. A.; Rambukkana, P. P.; Botheju, W. S. R.; Guruge, M. L.; Peiris, T. S. G.
    This study develops and validates a time series model to forecast Sri Lanka’s daily indicative USD/LKR spot exchange rate using ARIMA and ARCH methods using data from 1st of January 2021 to 4th of June 2025, sourced from Central Bank of Sri Lanka. The original series was first differenced to achieve stationarity since it is not stationary. According to the sample ACF and PACF of stationary series, three candidate models were augmented with an ARCH(2) variance specification based on residual diagnostics. After comparing AIC, SIC, Hannan Quinn metrics and log likelihood, the ARIMA(1,1,1)+ARCH(2) was identified as the best possible model. The diagnostic tests confirmed that residuals are identically and independently distributed without remaining heteroskedasticity. Insample forecasting yielded a MAPE of 0.32% and a Theil U statistic of 0.0036, while out-of-sample validation (June 5 to July 4, 2025) produced a MAPE of 0.087% and a bias proportion near zero, highlighting the model the model’s predictive accuracy. By focusing only on the internal pattern of the exchange rate, this research creates a strong short term forecasting tool for Sri Lanka's volatile currencyenvironment laying ground work for adding outside factors in future improvements.
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    PublicationOpen Access
    Exploring the Determinants of Medical Insurance Expenses: A Quantile Regression Approach
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rathnayake, K; Somasiri, D; Abeygunawardana, T; Nugegoda, K; Fernando, N; Guruge, M. L.; Peiris, T. S. G.
    Healthcare insurance costs are influenced by a combination of biological and socioeconomic factors. This study investigates how age, body mass index (BMI), gender, and discount eligibility affect medical insurance expenses in the United States, using data from 1,338 individuals. Due to the right-skewed distribution of expenses, quantile regression was applied at the 25th, 50th, and 75th percentiles, providing insights across low-, medium-, and high-cost groups. Results show that age and BMI consistently increase insurance expenses, with stronger effects among high-cost patients. Genderdifferences also emerged, with females incurring higher costs than males at certain expenditure levels. Discount eligibility significantly reduced expenses across all quantiles. In contrast, the number of children was not a significant predictor and was excluded from the final model. Compared to ordinary least squares regression, quantile regression provided a more accurate assessment of cost determinants in skewed data. These findings highlight the importance of adopting advanced modeling approachesin insurance pricing and suggest that targeted policies addressing individuals having high BMI and equitable discount programs could improve healthcare affordability and risk management.
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    PublicationOpen Access
    Predictive Model for the SPDR S&P 500 ETF (SPY) using Volatility Analysis Approach
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences,SLIIT, 2025-10-10) Musharraff, N. I.; Fernando, W. S. C.; Godage, T. R.; Jayasooriya, J. M. T. S.; Siriwardhana, H. A. A. T. P.; Samasundara, T. A.; Guruge, M. L.; Peiris, T. S. G.
    The S&P 500 (Standard & poor’s 500) is one of the most widely followed equity indices in the world. The SPDR S&P 500 ETF Trust (SPY) is used to track the performance of the S&P 500 index as closely as possible and can also be traded in the stock exchanges. Not many studies have been carried out to forecast daily closing prices of SPY for recent years. This study presents a time series analysis and forecasting of the daily closing prices of the SPY index. The dataset extends from 2000 to 2025, capturing key financial events, market movements and long-term growth trends. Due to high volatility, we were forced to consider variance equation in additional to the mean equation and the best fitted model identifies is ARIMA (1,1,1) + GARCH (1,1).ARIMA