"Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution Models"

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student's t-error distribution is described where we first consider an asymmetric heavy-tailed error and leverage effects. An efficient Markov chain Monte Carlo estimation method is described that exploits a normal variance-mean mixture representation of the error distribution with an inverse gamma distribution as the mixing distribution. The proposed method is illustrated using simulated data, daily S&P500 and TOPIX stock returns. The models for stock returns are compared based on the marginal likelihood in the empirical study. There is strong evidence in the stock returns high leverage and an asymmetric heavy-tailed distribution. Furthermore, a prior sensitivity analysis is conducted whether the results obtained are robust with respect to the choice of the priors.

Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model with the Ordering of Variables for the Japanese Economy and Monetary Policy

This paper applies the time-varying parameter vector autoregressive model to the Japanese economy. The both parameters and volatilities, which are assumed to follow a random-walk process, are estimated using a Bayesian method with MCMC. The recursive structure is assumed for identification and the reversible jump MCMC is used for the ordering of variables. The empirical result reveals the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008 and provides evidence that the order of variables may change by the introduction of zero interest rate policy.Bayesian inference, Monetary policy, Reversible jump Markov chain Monte Carlo, Stochastic volatility, Time-varying parameter VAR

Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.Comment: 27 pages, 13 figures, link to external web site for supplementary animated figure

Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications

This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR models with stochastic volatility. As an example of empirical application, the TVP-VAR model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic variables.Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregression, Stochastic volatility, Time-varying parameter

Stochastic Volatility Model with Leverage and Asymmetrically Heavy-tailed Error Using GH Skew Student's t-distribution

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student's t-error distribution is described where we first consider an asymmetric heavy-tailness as well as leverage effects. An efficient Markov chain Monte Carlo estimation method is described exploiting a normal variance-mean mixture representation of the error distribution with an inverse gamma distribution as a mixing distribution. The proposed method is illustrated using simulated data, daily TOPIX and S&P500 stock returns. The model comparison for stock returns is conducted based on the marginal likelihood in the empirical study. The strong evidence of the leverage and asymmetric heavy-tailness is found in the stock returns. Further, the prior sensitivity analysis is conducted to investigate whether obtained results are robust with respect to the choice of the priors.generalized hyperbolic skew Student's t-distribution, Markov chain Monte Carlo, Mixing distribution, State space model, Stochastic volatility, Stock returns

"Leverage, heavy-tails and correlated jumps in stochastic volatility models"

This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps. We illustrate our method using simulated data and analyze daily stock returns data on S&P500 index and TOPIX. Model comparisons are conducted based on the marginal likelihood for various SV models including the superposition model.

The Evolution of Loan Rate Stickiness Across the Euro Area

To investigate the banking sector integration across euro area countries in terms of loan interest rate stickiness, we estimate structural loan rate curves for 12 euro area countries using time-varying regressions with stochastic volatility. Our results show that the loan rates are sticky to a policy interest rate in all countries for all loan maturities, the degree of stickiness differs across the countries, and the degree of difference is more prominent for longer loan maturities. For short-term loans, the loan rate stickiness decreases and for intermediate- and long-term loans the loan rate stickiness converge to average levels during the sample periods. Banking integration in the euro area is not yet complete, but the degree of heterogeneity in the loan rate stickiness decreases.banking integration, sticky loan interest rate, Bayesian analysis, time-varying regression, Markov chain Monte Carlo

Monetary Policy Transmission under Zero Interest Rates: An Extended Time-Varying Parameter Vector Autoregression Approach

This paper attempts to explore monetary policy transmission under zero interest rates by explicitly incorporating the zero lower bound (ZLB) of nominal interest rates into the time-varying parameter structural vector autoregression model with stochastic volatility (TVP- VAR-ZLB). Nominal interest rates are modeled as a censored variable with Tobit-type non-linearity and incorporated into the TVP-VAR framework. For estimation, an efficient Markov chain Monte Carlo (MCMC) method is constructed in the context of Bayesian inference. The model is applied to the Japanese macroeconomic data including the periods of the zero interest rates policy and the quantitative easing policy. The empirical results show that a dynamic relationship between monetary policy and macroeconomic variables is well detected through changes in medium-term interest rates, and not policy interest rates under the ZLB, although other macroeconomic dynamics are reasonably traced without considering the ZLB in an explicit manner.Monetary policy, Zero lower bound of nominal interest rates, Markov chain Monte Carlo, Time-varying parameter vector autoregression with stochastic volatility