A melting pot - Gold price forecasts under model and parameter uncertainty

被引：30

作者：

Baur, Dirk G. ^{[1
]}

Beckmann, Joscha ^{[2
,3
,4
]}

Czudaj, Robert ^{[5
,6
,7
]}

机构：

[1] Univ Western Australia, Sch Business, 35 Stirling Highway, Crawley, WA 6009, Australia

[2] Ruhr Univ Bochum, Chair Int Econ, D-44801 Bochum, Germany

[3] Univ Duisburg Essen, Dept Econ, Chair Macroecon, D-45117 Essen, Germany

[4] Kiel Inst World Econ, Hindenburgufer 66, D-24105 Kiel, Germany

[5] Tech Univ Chemnitz, Dept Econ, Chair Empir Econ, D-09126 Chemnitz, Germany

[6] Univ Duisburg Essen, Dept Econ, Chair Econometr, D-45117 Essen, Germany

[7] Univ Appl Sci, FOM Hsch Oekon & Management, Herkulesstr 32, D-45127 Essen, Germany

来源：

INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS | 2016年 / 48卷

关键词：

Bayesian econometrics; Dynamic Model Averaging; Forecasting; Gold; EXCHANGE-RATE; PREDICTABILITY; PREDICTION; SELECTION; SAMPLE;

D O I：

10.1016/j.irfa.2016.10.010

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Gold is special as it is influenced by a wide range of factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility. Hence, forecasting the price of gold is a difficult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This model uncertainty in combination with parameter uncertainty is explicitly accounted for by Dynamic Model Averaging (DMA) which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and find that DMA (1) improves forecasts compared to other frameworks, (2) yields strong time-variation of gold price predictors and (3) favors parsimonious models. The results also show that typical in-sample features of gold such as its hedge property are weaker in an out-of-sample context. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：282 / 291

页数：10