Nonlinear State-Space Econometrics for Trading Signals With Python: Particle Filters, SMC², and Rao–Blackwellization for Real-Time Trading Signals (Richman Computational Economics) - Softcover
Book 28 of 29: Richman Computational Economics
Synopsis
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.
"synopsis" may belong to another edition of this title.
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Nonlinear State-Space Econometrics for Trading Signals With Python: Particle Filters, SMC², and Rao-Blackwellization for Real-Time Trading Signals (Richman Computational Economics)
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Nonlinear State-Space Econometrics for Trading Signals With Python (Paperback)
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Paperback. Condition: new. Paperback. 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 resourceTrading-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 valueTheory: 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 toBuild 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 forQuant 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 codeRBPF 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798264520723
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