Identifying Uncertainty Shocks Using the Price of Gold

Piffer, Michele; Podstawski, Maximilian

doi:10.1111/ecoj.12545

Cited by 107 publications

(67 citation statements)

References 59 publications

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“…Developing instruments to minimise the risk of getting spurious results from endogeneity‐prone regressions is clearly an important avenue for future research. For a first attempt in this sense, see Piffer and Podstawski ().…”

Section: Discussionmentioning

confidence: 99%

Yield Curve and Financial Uncertainty: Evidence Based on US Data

Castelnuovo

2019

Australian Economic Review

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How do short-and long-term interest rates respond to a jump in financial uncertainty? We address this question by conducting a local projections analysis with US monthly data, period: 1962-2018. The state-of-the-art financial uncertainty measure proposed by Ludvigson, Ma and Ng (2019) is found to predict movements in interest rates at different maturities. In particular, an increase in financial uncertainty is found to trigger a negative and significant response of both short-and long-term interest rates. The response of the short end of the yield curve (i.e., of short-term interest rates) is found to be stronger than that of the long end (i.e., of longterm ones). In other words, a financial uncertainty shock causes a temporary steepening of the yield curve. This result is consistent, among other interpretations, with medium-term expectations of a recovery in real activity after a financial uncertainty shock.

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Section: Discussionmentioning

confidence: 99%

Yield Curve and Financial Uncertainty: Evidence Based on US Data

Castelnuovo

2019

Australian Economic Review

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“…respond to the great uncertainty by rebalancing their investments toward the safe asset, or because those who hold such an asset are less willing to sell it (Caballero and Krishnamurthy, 2008;Piffer and Podstawski, 2017…”

Section: 3measuring the Response Of Gold Returns To The Uncertaintmentioning

confidence: 99%

Measuring the response of gold prices to uncertainty: An analysis beyond the mean

2018

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This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. To capture the core uncertainty effects on gold returns, a dynamic factor model is used. This technique allows summarizing the impact of six different indexes (namely economic, macroeconomic, microeconomic, monetary policy, financial and political uncertainties) within one aggregate measure of uncertainty. In doing so, we show that the gold's role as a hedge and safe haven cannot be assumed to hold at all times. This ability seems to be sensitive to the gold's various market states (bearish, normal or bullish) and to whether the uncertainty is low, middle or high. Interestingly, we find a positive and strong relationship between gold returns and the uncertainty composite indicator when the uncertainty attains its highest level and under normal gold market scenario. This suggests that holding a diversified portfolio composed of gold could help protecting against exposure to uncertain risks. Acknowledgement: The authors would like to thank the editor Sushanta Mallick and the two anonymous Reviewers for helpful and insightful comments and suggestions on an earlier version of this article. Highlights We examine the response of gold returns to different uncertainty indicators. We use a quantile-on-quantile regression model.  We develop an uncertainty composite indicator based on six uncertainty factors. The hedge and safe haven ability of gold is conditional on gold market states. The gold's role as a hedge and safe haven depends on the nuances of uncertainty.

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“…The data contained in z t are eight monthly US data series from Bloom (2009), using the transformations as in Piffer and Podstawski (2017). The vector of observational series,…”

Section: Data Specification and Resultsmentioning

confidence: 99%

Proxy VAR Models in a Data-Rich Environment

Bruns

2019

SSRN Journal

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Structural VAR models require two ingredients: (i) Informational sufficiency, and (ii) a valid identification strategy. These conditions are unlikely to be met by small-scale recursively identified VAR models. I propose a Bayesian Proxy Factor-Augmented VAR (BP-FAVAR) to combine a large information set with an identification scheme based on an external instrument. In an application to monetary policy shocks I find that augmenting a standard small-scale Proxy VAR by factors from a large set of financial variables changes the model dynamics and delivers price responses which are more in line with economic theory. A second application shows that an exogenous increase in uncertainty affects disaggregated investment series more negatively than consumption series.JEL classification: C38, E60

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Identifying Uncertainty Shocks Using the Price of Gold

Cited by 107 publications

References 59 publications

Yield Curve and Financial Uncertainty: Evidence Based on US Data

Yield Curve and Financial Uncertainty: Evidence Based on US Data

Measuring the response of gold prices to uncertainty: An analysis beyond the mean

Proxy VAR Models in a Data-Rich Environment

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