Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models |
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Affiliation: | 1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China;2. School of Economics and Management, Southwest Jiaotong University, Chengdu, China;3. School of Finance, Yunnan University of Finance and Economics, Kunming, China |
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Abstract: | We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers. |
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Keywords: | Bayesian forecasting Benchmark neutral portfolio Dynamic factor models Latent threshold dynamic models Multivariate stochastic volatility Portfolio optimization Sparse time-varying loadings |
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