Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis

doi:10.1016/j.physa.2019.123631

Physica A: Statistical Mechanics and its Applications

Volume 541, 1 March 2020, 123631

https://doi.org/10.1016/j.physa.2019.123631 Get rights and content

Highlights

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We examine the dynamic relationship between precious metals and stock markets.
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We use a hybrid wavelet-based Dynamic Conditional Correlation (DCC) approach.
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DCC between the return series of precious metals and stock market vary with timescales.
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G7 and BRICS nations exhibit different dynamicity with precious metals.
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This study finds that silver offers better hedging capability than other precious metals.

Abstract

This study examines the dynamic relationship between precious metals and stock markets of major developed (G7) and emerging (BRICS) nations. We use a hybrid wavelet-based Dynamic Conditional Correlation (DCC) approach, which allows us to investigate the dynamic relationship between precious metals and stock markets in time and frequency domain. Our results suggest that DCC between the return series of precious metals and stock market varies with timescales in terms of dynamicity, persistence and strength of relationship. G7 and BRICS nations exhibit different dynamicity with precious metals over the study period (2000 to 2017). The dynamics between precious metals and G7 stock markets show similar patterns, which depicts a clustering behaviour, however, same is not true for BRICS nations. In contrast to existing literature, this study found that silver offers better hedging capability than other precious metals for both short and long-run. To construct a two-asset optimal portfolio of precious metal and stock index, palladium emerges as the most favourable option for both short and long-run.

Introduction

The relationship between stock markets and precious metals has been of keen interest among researchers and practitioners alike. The primary interest of previous studies has been to explain investor’s tendency to invest in other asset classes to diversify the risks associated with stock markets. Moreover, the economic turmoil and crisis of last decades have prompted investors to diversify the portfolio with alternative investment instruments like precious metals [1], [2], [3], [4], [5], [6].

The interactions between different trading classes are considered to be an important factor in the resultant market dynamics [7]. The dynamics between stock markets and precious metals could impact the investor’s investment behaviour, however, this behaviour seems to work differently in developed and developing markets [1]. Besides, investment horizon (long-run and short-run) also reported to play an important role in portfolio diversification strategies [8]. Furthermore, as argued by Baruník et al. [9] such heterogeneity in market behaviour as well as interactions among different asset classes could result in dynamic relationship that may go unnoticed. Therefore, the objective of this study is to investigate the dynamic relationship between international stock markets (G7 and BRICS) with precious metals (gold, silver, platinum and palladium). More specifically, this paper investigates the dynamic relationship between stock markets and precious metals over different investment horizons.

The heterogeneity of market participants and investment choices emphasize the importance of analysing the time dependent co-movements. Moreover, The information transmission and spillover effect between different markets may also vary over different time horizons [10]. Active participants like financial institutions exploit the short-term movements in the markets, while passive investors like individual investors, insurance companies and non-financial firms’ targets long-term performances of the investments. The preference of investment can thus be attributed to risk type of investors. Gold has emerged as a hedging instrument against abrupt movements in stock markets. Usage of gold as a hedging instrument has led to an increased interest in other precious metals, which serves a dual role of investment assets as well as industrial commodities [11].

Understanding the dynamic relationship between precious metals and stock markets is an important aspect for portfolio design and hedging strategies. However, these interactions have been examined to a limited extent. For example, Baur and Lucey [12] analyse the time varying relationship between stock returns, bond returns and gold returns from 1995 to 2005 and reported that gold acts as a safe haven for stock markets of Germany, United Kingdom and United states. Authors also suggest that the safe haven property of gold is usually short lived and is predominant during economic shocks. Jain and Biswal [13] in their study analyse the dynamic relationship between crude oil prices, gold prices, exchange rate and stock market for 2006–2015 in the case of India. Authors report the existence of dynamic relationship between gold prices and stock index. Interestingly, they argue that the fall in gold prices result in a fall in the stock market index in India.

Financialization of commodity markets has directed the interests of investors towards precious metals as an alternative investment instrument. As a result, it calls for further research on the co-movements between precious metals and stock markets. Moreover, analysing the dynamic co-movements of assets for different investment horizons could provide additional information and help market participants to predict price changes and policy makers to take financial stability measures [9]. To get the additional insights regarding the co-movements of precious metal prices and stock market indices, this study adopts the combination of two techniques first suggested by Lehkonen and Heimonen [14]. The wavelet-based dynamic conditional correlation analysis depicts the precious metals and stock markets interferences at different time horizons. The strength of the wavelet analysis to decipher hidden information has led to the increased usage of wavelet analysis to get deeper understanding of the underlying phenomenon in time–frequency domain. For instance, Baruník et al. [9], Mensi et al. [15], Reboredo and Rivera-Castro [16], Vacha and Barunik [17], Das et al. [18], [19] and Das and Kumar [20] have employed wavelet analysis under different theoretical setups.

This study employs the multiresolution wavelet analysis to disentangle the precious metals and stock market dynamics at different time-horizons. Then, the results of wavelet analysis are used as an input in DCC–GARCH (dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity) process. Due to its capability to capture the dynamic correlation, DCC has been used by number of authors in the past, for example, Basher and Sadorsky [21], Creti et al. [22], Jones and Olson [23], Kim et al. [24] and Klein [25]. The combination of the above-mentioned approaches thus provides an opportunity for an in-depth examination of the dynamic correlation among the selected series. Moreover, the combination of these two techniques enable us to make inferences about the group of developed and emerging markets behaviour with respect to precious metals. Additionally, these techniques also allow to uncover the appropriate time-horizons to exploit the portfolio diversification benefits.

To the best of our knowledge, present study is the first to analyse the heterogeneity in correlations at different frequencies of precious metals and stock markets and therefore brings new insights to the existing body of literature. Our contribution to the literature is three-fold. First, this paper investigates the short-run and long-run dynamics between precious metals and stock markets. Due to the highly volatile properties of stock market and precious metal prices, any investigation is not complete without scrutinizing the time varying feature of these markets. It may happen that precious metals and stock markets do not exhibit any relationship in a short-run however, may show a strong relationship in a long-run. Segregating the data into various time scales (short run and long run) may also suggest the time periods to exploit the properties of financial assets. Second, this paper also investigates the appropriate hedging strategies. While literature suggests that gold provides a good hedge against adverse movements in stock exchanges however, is it true for other precious metals? Does investment in stock market can be used as a hedge against adverse movements in precious metals coupled with different time horizons? Third, this paper also considers the two-asset optimal portfolio in the case of stock market and precious metals over different time horizons.

Timescale analysis reveals some interesting dynamics between precious metals and stock markets. First, the dynamic correlation varies with timescales both in terms of dynamicity and strength of relationship. Second, developed and emerging markets exhibit different dynamic patterns over the study period. Both at returns level and higher timescales, developed countries follow up very closely to each other, which justify their clustering, but it is not true for BRICS nations. It suggests diversification benefits in the case of emerging nations over the long-time horizon. Third, on an average our results suggest that precious metals provide economical hedging against long positions in stock markets and not vice versa. Among precious metals, silver offers better hedging capability than other precious metals for both short and long-run. Fourth, in case of emerging markets portfolio of stock and precious metal should consist of higher levels of precious metals. However, to construct an optimal portfolio of stock markets and precious metals, palladium emerges as the most favourable option for both short and long run.

Rest of the paper is organized as follows. Section 2 describes the methodology employed for the study. Section 3 explains the data whereas Section 4 provides empirical results. The hedging strategies are discussed in Section 5. The Section 6 discusses the portfolio weights. Finally, Section 7 concludes.

Section snippets

Estimation methodology

This section explains the methodology employed for studying the dynamic relationship between stock markets and precious metals.

Data

The study considers a period from January 01, 2000 to March 31, 2017 (899 observations), which is approximately symmetric to the global financial crisis episode of 2008. This study employed weekly data to avoid the problem of non-synchronous daily trading across the international stock and precious metals markets.¹ The national equity index²

Empirical results

Empirical analysis was carried out in two steps. First, wavelet-based technique was employed to decompose the stock indices and precious metals returns series over different time horizons. In the second step, DCC–GARCH was used to investigate the dynamic correlation between decomposed series at respective frequencies and time horizons. For example, $d_{1}$ of gold was analysed with $d_{1}$ of Canadian stock index and $d_{6}$ of silver was considered with $d_{6}$ of Brazilian stock index. Similar pairing process

Hedging

Kroner and Sultan [36] argues that hedge ratios may be constructed by using the conditional volatility estimates. An asset $(x)$ on which a long-position is taken may be hedged with a second asset $(y)$ by taking a short position. Thus, the hedge ratio between the two assets may be represented as: $β_{x y, t} = \frac{h_{x y, t}}{h_{y y, t}}$ $β_{x y, t}$ is the hedge ratio between asset $x$ and $y$ at time $t$ , $h_{x y, t}$ is the conditional covariance between asset $x$ and $y$ at time $t$ , $h_{y y, t}$ is the conditional variance of asset $y$ . Table 4

Portfolio weights

Kroner and Ng [37] suggest that DCC GARCH (multiple GARCH model) volatility estimates can be used to construct optimal portfolio weights. $w_{x y, t} = \frac{h_{y y, t} - h_{x y, t}}{h_{x x, t} - 2 h_{x y, t} + h_{y y, t}}$ $w_{x y, t} = \{\begin{matrix} 0, i f w_{x y, t} < 0 \\ w_{x y, t}, i f 0 \leq w_{x y, t} \leq 1 \\ 1, i f w_{x y, t} > 1 \end{matrix}$ $w_{x y, t}$ is the weight of asset $x$ in a one-dollar portfolio of $x$ and $y$ , at time $t$ . $h_{x y, t}$ is the conditional covariance between asset $x$ and $y$ at time $t$ , $h_{y y, t}$ is the conditional variance of asset $y$ . The weight of asset $y$ at time $t$ is ${1 - w}_{x y, t}$ . Table 5 reports the summary statistics

Conclusion

This study investigates the dynamic relationship between precious metals and major stock market indices (G7 and BRICS) over different time horizons from 2000 to 2017 to capture the potential effects of market type on the co-movement dynamics. This study also investigates the hedging characteristic of precious metals and stock markets. Further, optimal portfolios were also constructed to examine the ideal weights of precious metal and stock market index in case of two asset portfolio. Results

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Authors are thankful to the editor Dr. Eugene Stanley and anonymous reviewers to their helpful comments and suggestions.

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Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis

Highlights

Abstract

Introduction

Section snippets

Estimation methodology

Data

Empirical results

Hedging

Portfolio weights

Conclusion

Declaration of Competing Interest

Acknowledgements

J. Bank. Financ.

Int. Rev. Financ. Anal.

Energy Econ.

Econ. Model.

Int. Rev. Econ. Finance

Energy Econ.

Resour. Policy

J. Empir. Financ.

Econ. Model.

Energy Econ.

Physica A

Econom. Lett.

Energy Econ.

Energy Policy

Econom. Lett.

J. Bank. Financ.

Econ. Model.

Rev. Financ. Econ.

Do precious metals shine? An investment perspective

Financ. Anal. J.

Do global factors impact BRICS stock markets? A quantile regression approach

Emerg. Mark. Rev.