Nonlinear State-Space Econometrics for Trading Signals With Python: Particle Filters, SMC², and Rao-Blackwellization for Real-Time Trading Signals(Richman Computational Economics)

Level up your quant edge with a dense, practitioner-first playbook to design, estimate, and deploy nonlinear state-space models for live trading. From heavy-tailed returns and microstructure noise to high-dimensional factor SV and regime switching, you'll master particle methods, SMC², and Rao-Blackwellized filters-then implement them line-by-line in Python. What makes this the go-to resource Trading-first focus: Every model is motivated by alpha, risk, execution, and portfolio constraints. Real-time ready: Online filtering, fixed-lag smoothing, and latency-aware pipelines for production. Non-Gaussian by default: Robust heavy tails, jumps, count/intensity models, and discrete regimes. Scales with your universe: Factor SV, conditional independence, and GPU-friendly parallelism. Variance reduction that matters: Rao-Blackwellization, guided proposals, and tempered SMC for sharp likelihoods. How each chapter delivers value Theory: Clear, mathematically precise derivations tailored to financial use-cases. Checkpoint MCQs: Multiple-choice questions with solutions to cement understanding quickly. Full Python code: End-to-end, reproducible demos for filtering, smoothing, PMCMC, SMC², and RBPF. You will learn to Build robust nonlinear state-space models for alpha, volatility, liquidity, and execution costs. Engineer observation models for heavy tails, jumps, and microstructure distortions. Implement bootstrap/APF filters, guided proposals, and backward-simulation smoothers. Train via Particle EM, PMMH, Particle Gibbs/PGAS, and nested SMC². Collapse linear-Gaussian substructures with Rao-Blackwellization for speed and accuracy. Evaluate and select models with evidence estimates, prequential scoring, and DMA. Deploy GPU-accelerated pipelines with reproducibility and numerical stability. Who this is for Quant researchers and portfolio managers seeking deployable signal pipelines. Data scientists and ML engineers moving beyond static models to state-space systems. Grad students in econometrics/finance looking for a rigorous, hands-on guide. What you'll build in code RBPF for dynamic regression with stochastic volatility and heavy tails. SMC² for online parameter learning across multi-asset universes. PGAS for regime-switching and semi-Markov duration models. Tempered SMC for evidence estimation and model comparison. Real-time signal extractors with risk forecasts (VaR/ES) and transaction-cost-aware P&L. Stop guessing and start filtering-transform noisy data into actionable, risk-aware trading signals.