Reliable projection of government bond markets is crucial for effective public debt management, development financing, and reducing rollover risks. In Nepal, bond markets serve as a key instrument for mobilizing resources, yet their future trajectories remain under explored despite their growing role in fiscal planning. This study investigates the mathematical exploration and computational performance of time series models ARIMA, RNN, and LSTM are applied to the government bonds of Nepal. The analysis examines trends, seasonal patterns, and trajectories using descriptive statistics to capture underlying market behaviors. An optimal ARIMA order was identified to effectively capture the linear growth path, while the RNN demonstrated strong capability in learning nonlinear patterns and outperformed the other models in predictive accuracy on unseen data. In contrast, the LSTM model, constrained by the limited size of the dataset, showed weaker generalization despite achieving comparable or lower training errors. The results highlight that Nepal’s bond market is characterized by a steady trajectory in Development Bonds, uncertainty in Citizen Saving Bonds, and weak participation in Foreign Employment Bonds, with total borrowing projected to rise. These findings suggest that while ARIMA emphasizes stability, deep learning approaches reveal momentum-driven growth potential, offering complementary perspectives. The paper aims to inform policymakers by presenting insights into how bond market forecasting may strengthen long-term development financing, mitigate refinancing risks, and foster wider participation in underutilized bonds, ultimately enhancing the effectiveness of debt management in Nepal.

This study evaluates the Adaptive Market Hypothesis (AMH) in the Nepalese stock market using daily index returns from July 1995 to February 2025, representing nearly three decades and the longest high-frequency analysis of the market to date. Comprehensive efficiency diagnostics reveal clear time-varying dynamics: persistent inefficiency from 1999 to 2019 is followed by emerging signs of efficiency from 2021 to 2025, coinciding with financial digitization and regulatory reforms. Employing GARCH and Markov-switching models, the results indicate highly persistent volatility (α + β ≈ 0.90) and the presence of two distinct volatility regimes. Low-volatility states persist for approximately 15 trading days, whereas high-volatility episodes last around 9 days, and are associated with major macroeconomic shocks, including the 2015 earthquake and the COVID-19 pandemic. Unlike developed markets, NEPSE does not exhibit a statistically significant leverage effect (γ₁ = 0.0279, p =0.120), suggesting symmetric volatility responses to positive and negative shocks. These findings contradict the assumption of static market efficiency and provide empirical evidence in support of the AMH’s prediction of evolving efficiency in emerging markets. The results carry important implications for regime- dependent investment strategies and countercyclical regulatory policies during high-volatility states.