Bayesian Dynamic Factor Models and Portfolio Allocation

By Omar Alejandro Aguilar, Matthew West. 2000

479 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/af40b7ac1dc2e5bb990e1e7a74d9071fc9e0fbba

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Citations in the corpus

Structural learning with time‐varying components: tracking the cross‐section of financial time series (2005, background): “Aguilar and West (2000) developed a class of dynamic linear factor models to capture both the multivariate stochastic volatility and the factor structure of financial time series.”
Multivariate Stochastic Volatility: A Review (2006, methodology): “(1999), Pitt and Shephard (1999a), and Aguilar and West (2000) have applied the MCMC procedure to estimate additive factor MSV models. Chan et al. (2005) estimated an asymmetric MSV model. Philipov and Glickman (2006) estimated a Wishart MSV model. Moreover, Yu and Meyer (2006) estimated and compare”
A Factor Analysis of Bond Risk Premia (2009, methodology): “For factor models, Kose, Otrok, and Whiteman (2003) uses an algorithm that involves 8 inversion of N matrices that are of dimension T ×T , which can be computationally demanding.”
Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data (2004, background): “Recent work from a Bayesian perspective has been done by Geweke and Zhou (1996), Press and Shigamesu (1997), Aguilar and West (2000), and Rowe (2002).”
Estimation methods for stochastic volatility models: a survey (2004, methodology): “Extensions to K factors and/or idiosyncratic errors following SV processes have been analysed by Kim et al. (1998), Pitt and Shephard (1999b) and Aguilar and West (2000).”
Asset Allocation With a High Dimensional Latent Factor Stochastic Volatility Model (2006, unknown): “Some recent examples are Lettau and Ludvigson (2001), Cremers (2002), Avramov (2002), and Goyal and Welch (2003). (2)For the (G)ARCH model, see the review by Bollerslev, Engle, and Nelson (1994) and references therein; for the stochastic volatility model, see Kim, Shephard, and Chib (1998), the revi”
Likelihood-based Analysis for Dynamic Factor Models (2008, background): “Examples of Bayesian approaches to dynamic factor models are Aguilar and West (2000) and Fiorentini, Sentana, and Shephard (2004) in the context of modelling volatility in time series.”
Comparisons of Four Methods for Estimating a Dynamic Factor Model (2008, methodology): “Recently, Bayesian methods have become more widely used for estimating the DAFS models in terms of its state space representation (Kim & Nelson, 1999, 2001; West, 2000).”
Sequential Monte Carlo Methods in Practice (2001, methodology): “Our experiences in this study mirror those using the straight auxiliary particle filtering method when the parameters are assumed to be fixed (Aguilar and West 2000).”
Multivariate stochastic volatility (2009, methodology): “(1999), Pitt and Shephard (1999a) and Aguilar and West (2000) proposed estimation methods based on single-move MCMC algorithms.”
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, background): “Another indication that r is inadequately large is the appearance of multimodalities in the posterior densities of the elements in A (Aguilar and West, 2010).”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, methodology): “(1994), Pitt and Shephard (1999), and Aguilar and West (2000) which were later picked up and extended by e.”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “Aguilar and West (2000) developed Bayes methods for estimating dynamic factor models with stochastic”
Multivariate Stochastic Volatility via Wishart Processes (2006, unknown): “Examples of multivariate SVOL models include models of Jacquier et al. (1995, 1999), Harvey et al. (1994), Mahieu and Schotman (1994), Aguilar and West (2000), and Chib et al. (2002).”
Markov chain Monte Carlo methods for stochastic volatility models (2002, background): “Another possible extension is to the generalized models of multivariate stochastic volatility of the type recently investigated in detail by Pitt and Shephard (1999b) and subsequently by Aguilar and West (2000).”
Factor Multivariate Stochastic Volatility via Wishart Processes (2006, methodology): “MSV models with factor structure have been proposed by Aguilar and West (2000), Chib et al. (2006), and Han (2002). The common feature of these models is that the covariance matrix of the factors is assumed diagonal and the factor variances each follow their independent stochastic process. When the ”