This presentation delves into the application of variational autoencoders (VAEs) and autoencoder market models (AEMMs) for modeling interest rates. It highlights how modern processors and cloud computing enable the adoption of more sophisticated machine learning models, whose performance is now comparable to fast numerical methods. The content emphasizes that neural networks outperform classical regression techniques like Principal Component Analysis (PCA) in capturing historical interest rate curve shapes and their evolution, and have the potential to surpass traditional methods such as the Nelson–Siegel (NS) and Nelson–Siegel–Svensson (NSS) bases. The presentation provides a detailed look at the architecture of VAEs and AEMMs, including the roles of encoders and decoders, the introduction of uncertainty, loss functions, optimization loops, and reconstruction/generation processes. It specifically covers VAEs for yield curves, including curve representation, training on historical data, currency encoding, and various latent space dimensionalities. Hands-on examples in Python, including VAEs for handwritten digits from the MNIST dataset and for yield curves, are also presented.

Features & Benefits

• Advanced Model Architectures: Explores Variational Autoencoders (VAEs) and Autoencoder Market Models (AEMMs) for sophisticated interest rate modeling.
• Performance Comparison: Compares the performance of VAEs and AEMMs against fast numerical methods and classical techniques like PCA, NS, and NSS bases.
• Practical Implementation: Provides hands-on examples in Python, demonstrating VAEs for both general datasets (MNIST) and specific financial applications (yield curves).
• Architectural Deep Dive: Details VAE architecture, including encoder/decoder roles, uncertainty introduction, loss functions, and optimization.