Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis

Baumöhl, Eduard and Lyócsa, Štefan (2017): Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis.

Abstract

We address the safe haven properties of gold relative to US stock market sector indices using the bivariate cross-quantilogram of Han et al. (2016). Splitting our sample into pre- and post-crisis periods, our results show that the safe haven properties of gold have a changing nature. Before and after the financial crisis, we find only limited quantile dependence and that gold can be considered a safe haven for most of the sectors, except Industrials. On a full sample (1999-2016), there are only three sectors – Healthcare, IT, and Telecommunication services – for which gold can be considered a safe haven.

• Item Type: MPRA Paper
• Language: English
• Keywords: stock market sectors; gold; safe haven; quantile dependence; cross-quantilogram
• Subjects: G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G10 - General; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217-229.

Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34(8), 1886-1898.

Baur, D.G., (2012). Financial contagion and the real economy. Journal of Banking & Finance 36(10), 2680-2692.

Ciner, C., Gurdgiev, C., & Lucey, B. M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. International Review of Financial Analysis, 29, 202-211.

Florackis, C., Kontonikas, A., & Kostakis, A., (2014). Stock market liquidity and macro-liquidity shocks: Evidence from the 2007–2009 financial crisis. Journal of International Money and Finance 44, 97-117.

Han, H., Linton, O., Oka, T., & Whang, Y. J. (2014). quantilogram: Quantilogram. R package version 0.1.

Han, H., Linton, O., Oka, T., & Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270.

Hayfield, T., & Racine, J. S. (2008). Nonparametric econometrics: The np package. Journal of statistical software, 27(5), 1-32.

Jiang, H., Su, J. J., Todorova, N., & Roca, E. (2016). Spillovers and Directional Predictability with a Cross‐Quantilogram Analysis: The Case of US and Chinese Agricultural Futures. Journal of Futures Markets, 36(12), 1231-1255.

Kontonikas, A., MacDonald, R., & Saggu, A., (2013). Stock market reaction to fed funds rate surprises: State dependence and the financial crisis. Journal of Banking & Finance 37, 4025-4037.

Linton, O., & Whang, Y. J. (2016). The quantilogram: With an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282.

Liu, C. S., Chang, M. S., Wu, X., & Chui, C. M. (2016). Hedges or safe havens—revisit the role of gold and USD against stock: a multivariate extended skew-t copula approach. Quantitative Finance, 16(11), 1763-1789.

O'Connor, F. A., Lucey, B. M., Batten, J. A., & Baur, D. G. (2015). The financial economics of gold—a survey. International Review of Financial Analysis, 41, 186-205.

Patton, A., Politis, D. N., & White, H. (2009). Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White, Econometric Reviews, 28(4), 372-375.

Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89, 1303-1313.

Politis, D. N., & White, H. (2004). Automatic Block-Length Selection for the Dependent Bootstrap. Econometric Reviews, 23(1), 53-70.