Sequential Monte Carlo for fractional stochastic volatility models
From MaRDI portal
Publication:4554435
DOI10.1080/14697688.2017.1327717zbMath1400.91652arXiv1508.02651OpenAlexW2964261112MaRDI QIDQ4554435
Alexandra Chronopoulou, Konstantinos V. Spiliopoulos
Publication date: 14 November 2018
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1508.02651
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Econometric estimation in long-range dependent volatility models: theory and practice
- Affine fractional stochastic volatility models
- Estimation and pricing under long-memory stochastic volatility
- Limit theorems for weighted samples with applications to sequential Monte Carlo methods
- Fractionally integrated generalized autoregressive conditional heteroskedasticity
- Central limit theorem for nonlinear filtering and interacting particle systems
- The detection and estimation of long memory in stochastic volatility
- Recursive Monte Carlo filters: algorithms and theoretical analysis
- Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
- Long memory in continuous-time stochastic volatility models
- Sequential Monte Carlo Methods in Practice
- Stochastic volatility and option pricing with long-memory in discrete and continuous time
- BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS
- Bayesian inference for partially observed stochastic differential equations driven by fractional Brownian motion
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- Asymptotic Statistics
- Arbitrage with Fractional Brownian Motion
- Correction to Black--Scholes Formula Due to Fractional Stochastic Volatility
- SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
This page was built for publication: Sequential Monte Carlo for fractional stochastic volatility models