Fuzzy Stochastic Modeling in Financial Risk Assessment and Economic Predictions

The current studies are concerned with introducing fuzzy stochastic models in predicting economics and analyzing financial risk. One class of models that has found notable traction in this domain are fuzzy stochastic models (which combine the merits of fuzzy logic and stochastic processes) as flexible representations for the uncertainty (fuzziness) and randomness (stochasticity) characteristic of economic variables (such as GDP growth, inflation, unemployment rates, or financial asset prices). In the study, introduced are fuzzy drift and volatility parameters to normalize uncertainty and stochastic volatility to portray said features through fuzzy stochastic models to predict macroeconomic indicators and stock prices for a defined duration. The results of the simulation suggest that fuzzy stochastic approach produces more dynamic and multilayered predictions, going beyond classical deterministic models and providing a more integrated perspective on how economic systems respond to uncertainty. Furthermore, a fuzzy Value at Risk (VaR) is applied to evaluate financial risk which also highlights the approach's opportunity to capture both the expected returns price fluctuation. To confirm the effect of altering fuzzy parameters and stochastic volatility on the model performance, a sensitivity analysis is conducted. In addition to suggesting avenues of future research that take advantage of machine learning and real-time data methods for better predictions, the study emphasizes the use of fuzzy stochastic models for financial risk assessment and for economic forecasting as they are well-placed to model risk and uncertainty in hyper-red, volatile non-linear systems.