3-3 - factors driving univariate series at well-separated...

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MCMC Estimation of Multiscale Stochastic Volatility Models By German Molina Vega Capital Services Ltd., London, W1K 2TJ, United Kingdom Chuan-Hsiang Han Department of Quantitative Finance, National Tsing Hua University, Taiwan, ROC Jean-Pierre Fouque Department of Statistics and Applied Probability, South Hall 5504, University of California, Santa Barbara, CA 93106-3110, USA Abstract: In this paper we propose to use Monte Carlo Markov Chain methods to estimate the parameters of Stochastic Volatility Models with several factors varying at different time scales. The originality of our approach, in contrast with classical factor models is the identification of two
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Unformatted text preview: factors driving univariate series at well-separated time scales. This is tested with simulated data as well as foreign exchange data. Then we discuss the usage of scaled volatilities to derivatives pricing by Monte Carlo simulations with the feature of variance reduction. OUTLINE 1. INTRODUCTION 2. MULTISCALE MODELING AND MCMC ESTIMATION 2.1 Prior specification 2.2 Estimation 3. SIMULATION STUDY 4. EMPIRICAL APPLICATION: FX DATA 5. MONTE CARLO PRICING UNDER MULTISCALE VOLATILITY MODELS 5.1 Hedging Martingale as a Control 5.2 Linear and Nonlinear Control for Derivatives Pricing 6. CONCLUSION...
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This note was uploaded on 07/26/2011 for the course ECON 101 taught by Professor Markspenser during the Spring '11 term at Webster FL.

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