Study on Quantitative Risk Modeling with Ai and Monte Carlo: An Application to Nifty 50 Index

The accurate forecasting of financial time series continues to be a major challenge in quantitative finance, especially for emerging market indices like India's Nifty 50, which are known to be highly volatile, non-linear, and having fat-tailed returns distributions(Mahajan et al., 2022; Nerlekar & Nerlekar, 2021a). While traditional econometric models tend to miss these intricacies, the spurt in state-of-the-art machine learning (ML) techniques calls for a thorough and systematic comparative evaluation(Li et al., 2025; Naik & Mohan, 2019). This study fills an essential gap through a multidimensional assessment that considers models on predictive validity as well as risk assessment measures within a systematic, iterative improvement process a common omission in studies of the Indian market(S Baranidharan & Amirdha Vasani Sankarkumar, 2025). This research utilizes a comparative approach to assess three different modelling approaches on the Nifty 50 index: a deep learning framework (Long Short-Term Memory, LSTM), a tree-based ensemble algorithm (Random Forest), and a gradient-boosting framework (XGBoost)(Wu, 2024; Xing, 2024). The paper systematically records an iterative, model-driven feature engineering and hyperparameter optimization process aimed at optimal performance(Kumar et al., 2025). In addition, it performs an extensive risk analysis by computing and interpreting Value at Risk and Conditional Value at Risk from the forecasts of each model, compared to measures computed from a standard Monte Carlo simulation benchmark(G. J. Alexander & Baptista, 2004a; Dupačová & PolÍvka, 2007; Pasieczna, 2019). The performance and risk measures of the models are statistically confirmed through a z-score of directional accuracy and Kupiec's Unconditional Coverage test for forming a solid basis for model selection(Vaniya & Gor, 2022). The results are firm evidence that the optimized and trained Random Forest model outperforms other models by a significant margin in directional forecasting, with an outstanding directional accuracy of 83.91%. One of the findings is the paramount significance of model-specific tuning; the performance of the LSTM declined when it was presented with a wide range of technical indicators but elevated significantly when given a feature set that complemented its sequential learning design(Li et al., 2025; Sang & Li, 2024), like lag variables. The work also sheds light on an essential difference between risk evaluation based on a forecast model, which provides a more conservative by removing noise, and an overt Author: Chandu R Naik Co-author: Dr Uday K Jagannathan Ramaiah University of Applied Sciences (FMC)2 simulation, which captures the entire scale of historical tail risk(Glasserman et al., n.d.; ThoughtsOnVar_s1_Allen_, n.d.). This article adds an in-depth, end-to-end methodology for financial forecasting and risk analysis, providing real-world advice for practitioners and a broad picture of contemporary quantitative financial analysis.