The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model

Bhatt, Vipul; Kishor, N. Kundan; Ma, Jun

doi:10.1016/j.jedc.2017.06.008

Cited by 33 publications

(21 citation statements)

References 25 publications

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“…We demean each series before the estimation since the means of factors are not separately identifiable. Finally, following works such as Neely and Rapach (2011) and Bhatt et al (2017), we set p = q = 2 to keep the model parsimonious.…”

Section: Discussionmentioning

confidence: 99%

Time-varying spillovers between housing sentiment and housing market in the United States☆

André

Gabauer

Gupta

2021

Finance Research Letters

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This paper investigates spillovers between the housing sentiment index of Bork et al. (2020), common factors in US real housing returns and their volatility (derived from a time-varying dynamic factor model with stochastic volatility), GDP growth and real interest rates, using the time-varying parameter vector autoregressive version of the Diebold and Yılmaz (2012, 2014) methodology. We find that in contrast to spillovers from the common factor in housing returns, reverse spillovers are relatively weak. Net spillovers from the common factor of housing returns to housing sentiment and GDP increase durably after the Global Financial Crisis. This suggests that, while a shock to housing prices is likely to have a significant impact on housing sentiment and the economy, a purely exogenous shock to housing sentiment, may in itself have little impact on housing returns and volatility.

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Section: Discussionmentioning

confidence: 99%

Time-varying spillovers between housing sentiment and housing market in the United States☆

André

Gabauer

Gupta

2021

Finance Research Letters

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“…After the MCMC algorithm converges, the joint density of parameters and states can be numerically integrated to yield marginal distributions of parameters and states of interest. For further details on the estimation steps, readers are referred to the appendices of Bhatt, et al, (2017).…”

Section: Dynamic Factor Model With Time-varying Loadings and Stochastmentioning

confidence: 99%

Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty

Gupta

Risse

et al. 2018

Journal of Macroeconomics

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developed measures of partisan conflict, geopolitical risks and news-based VIX (NVIX). 5 These authors indicate that although in a general sense uncertainty is defined as the conditional volatility of an unforecastable disturbance, the empirical literature has usually relied on, which in turn can pick up fluctuations that are actually predictable and hence, can erroneously be attributable to uncertainty. Thus, it is important to distinguish between uncertainty in a series and its conditional volatility, i.e., properly measuring uncertainty would require one to remove the forecastable component of the considered series before computing the conditional volatility. In this sense, uncertainty in a series is not necessarily equivalent to the conditional volatility of the raw

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“…We extend the typical constant parameter Dynamic Factor Model (DFM) to a DFM with time‐varying stochastic volatility (denoted by DFM‐SV), following closely the work by Del Negro and Otrok () and Bhatt, Kishor and Ma (). The DFM‐SV decomposes the variations of each commodity return into three components: common market movement, sector‐specific movement and each individual commodity's idiosyncratic movement.…”

Section: A Dynamic Factor Model With Time‐varying Parameter and Stochmentioning

confidence: 99%

What Drives Commodity Returns? Market, Sector or Idiosyncratic Factors?

Vivian

Wohar

2019

Oxf Bull Econ Stat

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This paper examines the relationship between 43 commodity returns using a dynamic factor model with time varying stochastic volatility. The dynamic factor model decomposes each commodity return into a common (market), sector‐specific and commodity‐specific component. It enables the variance attributed to each component to be estimated at each point in time. We find the return variation explained by the common factor has increased substantially for the recent period and is statistically significant for the vast majority of commodities since 2004 (at each point in time) This phenomenon is the strongest for non‐perishable products. We link the amount of variation explained by the common factor to economic variables.

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The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model

Cited by 33 publications

References 25 publications

Time-varying spillovers between housing sentiment and housing market in the United States☆

Time-varying spillovers between housing sentiment and housing market in the United States☆

Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty

What Drives Commodity Returns? Market, Sector or Idiosyncratic Factors?

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