MCMC Methods for Financial Econometrics

By Michael Johannes, Nicholas G. Polson. 2002

153 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c6372dadf834dcc2a8c7549bd352965efad1633e

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Citations in the corpus

Monetary Policy Shifts and the Term Structure (2011, background): “While there are several examples of these types of estimations for affine models (see, among others, Lamoureux and Witte, 2002; Johannes and Polson, 2005; Ang, Dong and Piazzesi, 2006; Dong, 2006), these cannot be directly employed to estimate the quadratic model because in an affine setting,…”
The dynamics of commodity prices (2013, unknown): “A three-factor model incorporating prices, interest rates and the convenience yield is constructed by Liu and Tang (2011) to capture the stochastic relationship between the convenience yield level and its volatility for industrial commodities. Tang (2012) develops another three-factor model with a s”
Analytical and Numerical Methods for Pricing Financial Derivatives (2011, methodology): “A comprehensive overview of Bayesian methods and Markov Chain Monte Carlo algorithms in finance can be found in [67] by Johannes and Poulson, which includes also a chapter on estimating the interest rate models.”
No-Arbitrage Taylor Rules (2007, unknown): “Drawing μ and Φ We follow Johannes and Polson (2005) and explicitly differentiate between {μ, Φ} and {μQ, ΦQ}.”
Estimating the Parameters of Stochastic Volatility Models Using Option Price Data (2015, background): “To circumvent the possibility of singularity it is necessary to assume a pricing error which is either additive in the sense that the difference between the observed and model option price is a zero mean Gaussian deviate or multiplicative in the sense that it is the difference between the logarithms”
CHAPTER 4 – Estimating Functions for Discretely Sampled Diffusion-Type Models (2010, methodology): “Bayesian estimators with the same asymptotic properties as the maximum likelihood estimator can be obtained by Markov chain Monte Carlo methods, see Elerian, Chib & Shephard (2001), Eraker (2001), Roberts & Stramer (2001), and Johannes & Polson (2003) .”
Resolution of Policy Uncertainty and Sudden Declines in Volatility (2017, unknown): “Note that we have applied properties of tempered distributions to simplify the calculations, all of which can be found in Kanwal (2004). The same technique has been applied by Amengual and Xiu (2012) for pricing VIX derivatives.”
Bayesian Tail Risk Interdependence Using Quantile Regression (2015, background): “As in Johannes and Polson (2009), equation (16) for yk,t is a measurement equation with time-varying coefficients, because yj,t is known and represents a time-varying factor loading.”
The information content of high-frequency data for estimating equity return models and forecasting risk (2010, methodology): “We first briefly describe the general principles of Markov chain Monte Carlo (MCMC) methods, with more detailed exposition in Chib and Greenberg (1996), Johannes and Polson (2002) and Jones (1998).”