We use a time-varying parameter dynamic factor model with stochastic volatility (DFM-TV-SV) estimated using Bayesian methods to disentangle the relative importance of the common component in FHFA house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We...

This paper explores the implications of time varying volatility for optimal monetary policy and the measurement of welfare costs. We show how macro-economic models with linear and quadratic state dependence in their variance structure can be used for the analysis of optimal policy within the...

This paper explores the implications of time varying volatility for optimal monetary policy and the measurement of welfare costs. We show how macroeconomic models with linear and quadratic state dependence in their variance structure can be used for the analysis of optimal policy within the...

Since the channel for agents' expectations matters for the effectiveness of monetary policies, it is crucial for policy-makers to assess the degree to which economic agents are boundedly rational and understand how the bounded rationality affects the monetary rules in stabilising the economy. We...

This paper introduces an extended multivariate EGARCH model that overcomes the zero-return problem and allows for negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite limitations, such as noninvertibility and unclear...

This paper introduces a novel model to analyse the impact of macroeconomic shocks on volatility spillovers within key financial markets, such as Stock, Bond, Gold and Crude Oil. By treating macroeconomic variables as external factors to financial market volatility, our study distinguishes...

Maximum Likelihood (ML) shows both lower power and higher bias in small sample Monte Carlo experiments than Indirect Inference (II) and IIís higher power comes from its use of the model-restricted distribution of the auxiliary model coeffi cients (Le et al. 2016). We show here that IIís higher...

Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is a form of indirect inference we term "informal" under which data features are "targeted" by the model -i.e. parameters are chosen so that model-simulated features...

A common practice in estimating parameters in DSGE models is to Önd a set that when simulated gets close to an average of certain data moments; the modelís simulated performance for other moments is then compared to the data for these as an informal test of the model. We call this procedure...

Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic...