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Erschienen in: Empirical Economics 1/2017

25.03.2016

On the influence of US monetary policy on crude oil price volatility

verfasst von: Alessandra Amendola, Vincenzo Candila, Antonio Scognamillo

Erschienen in: Empirical Economics | Ausgabe 1/2017

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Abstract

Forecasting oil prices is not straightforward, such that it is convenient to build a confidence interval around the forecasted prices. To this end, the principal ingredient for obtaining a reliable crude oil confidence interval is its volatility. Moreover, accurate crude oil volatility estimation has fundamental implications in terms of risk management, asset pricing and portfolio handling. Generally, current studies consider volatility models based on lagged crude oil price realizations and, at most, one additional macroeconomic variable as crude oil determinant. This paper aims to fill this gap, jointly considering not only traditional crude oil driving forces, such as the aggregate demand and oil supply, but also the monetary policy rate. Thus, this work aims to contribute to the debate concerning the potential impact of (lagged) US monetary policy as well as the other crude oil future price (COFP) determinants on daily COFP volatility. By means of the recently proposed generalized autoregressive conditional heteroskedasticity mixed data sampling model, different proxies of the US monetary policy alongside US industrial production (proxy of the US aggregate demand) and oil supply are included in the COFP volatility equation. Strong evidence that an expansionary (restrictive) variation in monetary policy anticipates a positive (negative) variation in COFP volatility is found. We also find that a negative (positive) variation of industrial production increases (decreases) COFP volatility. This means that volatility behaves counter-cyclically, according to the literature. Furthermore, the out-of-sample forecasting procedure shows that including these additional macroeconomic variables generally improves the forecasting performance.

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Fußnoten
1
The lagged-RV has been used as benchmark in Asgharian et al. (2014), for instance.
 
2
Engle et al. (2013) also propose a version of the MIDAS filter that allows for daily variation of the long-term \(\tau _t\), which would become \(\tau _{i,t}\) (rolling estimator). However, as pointed out by the authors, there are negligible differences between the long-term component obtained by using a fixed or a rolling estimator.
 
3
It is worth noting that in the GARCH-MIDAS framework the starting points of the parameters as well as the number of lags K quite affect the convergence of the BFGS algorithm (Shanno 1985). Nevertheless, our results are robust to small variations of K.
 
4
For instance: Engle and Rangel (2008), Engle et al. (2013), Asgharian et al. (2013) and Conrad et al. (2014), among others.
 
5
Note that there are no available expectations for OilP and EFFR.
 
6
For brevity, we present only the results for the restricted weighting scheme. The results when the unrestricted weighting scheme is used are available upon request.
 
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Metadaten
Titel
On the influence of US monetary policy on crude oil price volatility
verfasst von
Alessandra Amendola
Vincenzo Candila
Antonio Scognamillo
Publikationsdatum
25.03.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 1/2017
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-016-1069-5

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