Tactic Volatility

Tactic volatility, the unpredictable variation in the attributes of actions taken by adaptive systems, presents a significant challenge in fields ranging from financial modeling to self-adaptive software. Current research focuses on improving prediction accuracy of this volatility, employing techniques like evolved recurrent neural networks and novel neural network architectures with dedicated mean and variance components to forecast both reactive and proactive responses. These advancements aim to enhance the efficiency and resilience of self-adaptive systems and improve the accuracy of financial models, particularly those involving options pricing and target volatility strategies. The development of robust prediction models for tactic volatility has broad implications for optimizing system performance and managing risk across diverse applications.

Papers

November 27, 2023

From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks
Philippe Goulet Coulombe, Mikael Frenette, Karin Klieber
Volatility Forecasting Reactive Behavior Constrained Risk Hemisphere Neural Network Tactic Volatility

April 21, 2022

Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics
Aizaz Ul Haq, Niranjana Deshpande, AbdElRahman ElSaid, Travis Desell, Daniel E. Krutz
Neural Network Self Adaptive System APT Tactic Uncertainty Mitigation Tactic Volatility

December 3, 2021

Reinforcement learning for options on target volatility funds
Roberto Daluiso, Emanuele Nastasi, Andrea Pallavicini, Stefano Polo
Reinforcement Learning Market Volatility Continuous Option Mutual Fund Tactic Volatility

Tactic Volatility

Papers

From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks

Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics

Reinforcement learning for options on target volatility funds