On Quantum and Quantum-Inspired Maximum Likelihood Estimation and Filtering of Stochastic Volatility Models

Learn about quantum and quantum-inspired approaches to maximum likelihood estimation and filtering techniques for stochastic volatility models in this 56-minute conference talk by Eric Ghysels from the University of North Carolina. Explore how quantum algorithms and formalisms can be applied to financial modeling, specifically focusing on the estimation and filtering of stochastic volatility models that are fundamental to modern finance. Discover the intersection of quantum computing methodologies with traditional econometric techniques, examining how quantum-inspired algorithms might offer computational advantages for complex financial modeling problems. Gain insights into cutting-edge research that bridges quantum computing theory with practical applications in financial mathematics and econometrics.