International Conference on Actuarial Sciences [ICActS] 2025
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4496
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Publication Open 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.Publication Open 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.Publication Open Access Development of Time Series Model to Predict the Weekly Percentage of Python Programming Language usage(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Gunawardane, D. M. N. M.; Herath, H. M. P. T.; Pitiyekumbura, W. S.; Samodhika, P. L. D.; Athauda, A. M. B. T.; Amarasinghe,E. J. C. U.; Peiris, T. S. G.Python's super popular and getting bigger fast. Figuring out how it will be used is super important for planning what to teach, training tech workers, and making good rules, especially in places like Sri Lanka that are just now getting into digital stuff. Therefore, this study aims to predict the weekly global usage of Python. We looked at data from April 21, 2019, to April 21, 2024. We got 262 weeks. This data is entered into Kaggle from Google search interest scores (Nextmillionaire, 2023). This dataset shows the highest interest score for Python in the general world. After trying out a bunch of models, theARIMA (1,1,1) model with seasonal stuff seemed like the best fit. We taught the model with data from April 21, 2019, to January 28, 2024 (250 weeks) and checked it with data from February 4, 2024, to April 21, 2024 (12 weeks). We tested the model to make sure it was doing things right, and the leftovers looked random, which is a good thing. The MAPE (Mean Absolute Percentage Error) for the validation data is 6.04%. This shows the ARIMA model is pretty good at guessing Python usage over time. Because theguesses are pretty accurate and consistent, it looks like Python usage of global is going up steadily. This means Python is a big deal with both Data Science & Analytics, Machine Learning & AI, Cloud Computing & DevOps, Automation & Scripting. This info should help schools, training places, and the government make smart choices about teaching digital skills.Publication Open 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