The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios

Abstract

Abstract Dynamics of financial contagion rapidly and drastically transformed by diversifying the investment preferences. Eventually increased diversification in the investment environment coupled with successive global events induced more complex and non-linear connections between the traditional and emerging markets. In this respect, this research explores the dynamic, asymmetric, and non-linear volatility transmissions among the Decentralized Finance (DeFi), Commodity, Energy, Technology, and Clean Energy Markets by incorporating Long Short Term Memory (LSTM) into the Time Domain of Time Varying Parameters Vector Auto Regression (TD-TVPVAR) model to eliminate the shortcomings of the former studies. Results compare the outputs of the Frequency Extension of TVPVAR (FC-TVPVAR) and LSTM-TVPVAR methods and verify the achievements of the new methodology. Consequently, new approach identify Bitcoin (BTC), gold, and oil markets as the primary sources of volatility, since clean energy market is determined to be the only significant destination of risk. Finally, prediction accuracy and the reliability of the incorporated model are validated by performance metrics and the achievements of the new approach are verified by bootstrapping test results.