Research Article
Analysis of Short-Term Interest Rate Models with Stochastic Volatility
Issue:
Volume 10, Issue 2, June 2025
Pages:
12-26
Received:
19 January 2025
Accepted:
7 February 2025
Published:
29 May 2025
DOI:
10.11648/j.ijssam.20251002.11
Downloads:
Views:
Abstract: The examination of short-term interest-rate behaviour is of critical importance in financial analysis, risk management, and in formulating monetary policy. Fluctuations in financial markets in recent times have emphasised the need for strong and reliable models that can effectively model behaviors and dynamics involved in short-term interest-rate fluctuations. Conventional approaches, including Vasicek’s, have been universally embraced; yet such techniques often face difficulty in explaining clustering and autocorrelated volatility in real-world data. This study explores short-term interest rate models with stochastic volatility and evaluates their effectiveness in comparison to Autoregressive Conditional Heteroskedasticity (ARCH) and Generalised ARCH (GARCH) models. Using historical data from Nigerian financial instruments, we carried out Ljung-Box Q-statistic and ARCH tests to examine autocorrelation and volatility clustering. Results indicate that the data exhibits strong autocorrelation and significant volatility clustering. The predictive performance of our stochastic volatility model was measured by 10-day ahead volatility forecasts, which reached the sum of squared deviations of 1.3095, while ARCH had 2.0001 and GARCH had 2.1433. Our findings suggest that the stochastic volatility model outperforms the traditional ones, such as ARCH and GARCH, for interest rate change forecasting. Based on the performance realised, observed stochastic volatility models are recommended to better forecast interest rates, particularly for the emerging markets, where financial data could be volatile.
Abstract: The examination of short-term interest-rate behaviour is of critical importance in financial analysis, risk management, and in formulating monetary policy. Fluctuations in financial markets in recent times have emphasised the need for strong and reliable models that can effectively model behaviors and dynamics involved in short-term interest-rate f...
Show More
Research Article
An EpiMod-QR/Alt Code-Based Model for Smart Campus Attendance Management Using the Differential Transform Method
Helen Onovwerosuoke Sanubi*
,
Augustine Aduge
Issue:
Volume 10, Issue 2, June 2025
Pages:
27-40
Received:
17 April 2025
Accepted:
4 May 2025
Published:
12 June 2025
DOI:
10.11648/j.ijssam.20251002.12
Downloads:
Views:
Abstract: This study presents the Epidemiological Quick Response and Alternative Code (EpiMod-QR/Alt) system, an innovative framework designed to address attendance management challenges in certain Nigerian higher institutions. By integrating QR/Alt code technology with compartmental differential equation modeling, the system offers real-time tracking, predictive analysis, and actionable insights for data-driven decision-making. Leveraging the Differential Transform Method (DTM), the system solves the underlying differential equations with enhanced computational efficiency and accuracy. The model categorizes students into dynamic compartments—scheduled, attending, and absent—allowing for continuous monitoring and analysis of attendance trends. The EpiMod-QR/Alt system is designed to overcome the limitations of traditional and semi-digital attendance systems, such as inaccuracy, time inefficiency, and lack of scalability. It supports hybrid learning environments by accommodating both physical and virtual attendance tracking, ensuring that data collection remains seamless and secure. Through theoretical validation and simulated scenarios, including fixed policies and dynamic interventions, the system demonstrates adaptability and robustness across diverse institutional contexts. Results indicate that the system significantly reduces absenteeism, improves administrative oversight, and supports the optimal allocation of institutional resources. Its predictive capabilities enable proactive interventions and long-term planning, aligning with the broader goals of smart campus transformation. The research lays the groundwork for practical implementation and highlights potential for future enhancements, including the integration of machine learning algorithms and expansion to multi-campus systems. By combining mathematical modeling with technological innovation, the EpiMod-QR/Alt system offers a scalable, efficient, and intelligent solution to modern attendance management in higher education.
Abstract: This study presents the Epidemiological Quick Response and Alternative Code (EpiMod-QR/Alt) system, an innovative framework designed to address attendance management challenges in certain Nigerian higher institutions. By integrating QR/Alt code technology with compartmental differential equation modeling, the system offers real-time tracking, predi...
Show More
