RETURN, VOLATILITY AND LIQUIDITY SPILLOVERS: THE CASE OF EQUITY AND COMMODITY MARKETS Master Thesis June 2007 Dalia Lasaite 1 MSc in Finance Supervised by: Prof. Michael Rockinger University of Lausanne 1 I am very grateful to my supervisor, Prof. Michael Rockinger, for his valuable support and patience. I would also like to thank Maria Khodorkovskaya, Mustafa Karaman, Ji Hyung Noh, Elzbieta Lukenskaite, and other fellow students for their valuable help, mind-blowing discussions, and entertainment. My sincerest gratitude goes to my family and Tomasz Sinicki, who supported me throughout the way. All the remaining errors are mine.
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Return, Volatility and Liquidity Spillovers: the Case of Equity and Commodity Markets
Dalia Lasaite - Return, Volatility and Liquidity Spillovers: the Case of Equity and Commodity Markets MSc in Finance thesis at HEC Lausanne
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RETURN, VOLATILITY AND LIQUIDITY SPILLOVERS:
THE CASE OF EQUITY AND COMMODITY MARKETS
Master Thesis
June 2007
Dalia Lasaite1
MSc in Finance
Supervised by:
Prof. Michael Rockinger
University of Lausanne
1 I am very grateful to my supervisor, Prof. Michael Rockinger, for his valuable support and patience. I would also like to thank Maria Khodorkovskaya, Mustafa Karaman, Ji Hyung Noh, Elzbieta Lukenskaite, and other fellow students for their valuable help, mind-blowing discussions, and entertainment. My sincerest gratitude goes to my family and Tomasz Sinicki, who supported me throughout the way. All the remaining errors are mine.
ABSTRACT
We investigate the interactions between equities and six commodity assets using vector
autoregression (VAR) and identification through heteroskedasticity (IH) techniques and find
evidence of spillovers within commodity markets, but also between equity, oil and precious
metals. Each commodity asset is mostly negatively auto-correlated in returns and positively in
volatility and liquidity, and the spillovers within commodity markets usually occur through the
latter channels. Spillovers between equities in commodities, however, are slightly different,
resulting in positive relationship in returns and liquidity between equities, precious metals and
oil. From equity investor point of view, precious metals and oil therefore are not the best
choice for diversification purposes, as they experience both return falls and liquidity squeezes
at the same time as the equity market does. Comparison of 1998-2001 and 2004-2007 periods
suggests increasing integration between metals, oil and equities, but the evidence is rather
weak. Inclusion of liquidity parameter versus return and volatility estimation does not alter the
picture dramatically, only slightly strengthening persistence in volatility. We were unable to
detect contemporaneous effects with the daily data either because of parameter instability, lack
of such interactions, or noise. The study has important implications for portfolio management
and diversification.
TABLE OF CONTENTS
1 INTRODUCTION................................................................................................................. 42 LITERATURE REVIEW .................................................................................................... 6
2.1 Market liquidity ........................................................................................................... 72.2 Liquidity spillovers ...................................................................................................... 92.3 Commodities as a portfolio investment ..................................................................... 10
3 METHODOLOGY ............................................................................................................. 113.1 Liquidity proxies........................................................................................................ 113.2 Vector Autoregression Model.................................................................................... 133.3 Identification through heteroskedasticity................................................................... 133.4 Implementation .......................................................................................................... 173.5 A note on methodology.............................................................................................. 173.6 Data ............................................................................................................................ 18
4 RESULTS ............................................................................................................................ 194.1 VAR: Single asset results........................................................................................... 19
Each asset has three data series for the whole sample period: log-return, volatility (absolute
return), and illiquidity parameter (relative bid-ask spread)3. The series are adjusted by
regressing them on weekday dummies and trend variables, in order to filter out the effects that
are not relevant to the study. Adjusted time series are used for all estimations. MATLAB codes
are available in the appendix4. The model is estimated with two lags.
3 Unfortunately, we were unable to retrieve liquidity parameters for oil; therefore only returns and volatility are used in the estimation4 Some MATLAB codes were taken from www.spatialeconometrics.com, to which we are grateful.
4 RESULTS
4.1 VAR: Single asset results
We first estimate the interplay between returns, volatility and illiquidity for single asset
markets. The results for each asset are discussed below.
4.1.1 Gold
Returns in gold are not persistent and do not spill over to volatility and illiquidity. Innovations
in illiquidity and volatility strongly Granger-cause each other, thereby confirming the intuition
that illiquidity periods tend to be succeeded by periods of high volatility, and vice versa.
Exclusion of illiquidity parameter gives similar results, the only persistent factor being
volatility.
4.1.2 Silver
Silver patterns are slightly distinct from other metals, for that returns are Granger-caused both
by lagged returns and volatility. In addition, returns, volatility and illiquidity play a role in
determining volatility. Higher lagged volatility results in higher volatility and higher returns,
while widening bid-ask spread results in higher volatility. In addition, model without illiquidity
parameter shows similar results, exhibiting substantial persistence in returns and volatility. The
results for gold and silver are summarized in the table below.
As seen from the table above, most spill overs occur through volatility, whereas illiquidity and
returns have slightly less persistent patterns. Illiquidity, however, shows much stronger
persistence than returns. Own returns tend to exhibit reversals and have negative relation to
their lags.
4.2.2 Commodities and equities
Inclusion of equities into the estimation does not change the interactions between the metals
dramatically, yet equities cause some interesting effects. Equity returns play a role in Granger-
causing copper, silver, and gold returns. Contrary to metal own lagged returns, when the
relation is negative (i.e. returns tend to reverse), equity returns have a positive influence on
metal returns, i.e. after a fall in stock prices, metals tend to fall as well. This effect is
particularly pronounced for the precious metals – gold and silver.
Table 5. Estimation results: Commodities and equitiesAsset Code Significantly Granger-caused by*:Copper Returns CR CR(-), AR, ER(+)Copper Volatility CV CL(+), ZV, SV(+)Copper Liquidity CL CV(+), CL(+), AV(-), ZL(+)Aluminium Returns AR AL(+)Aluminium Volatility AV AR(-), AV(+), AL(+)Aluminium Liquidity ALZinc Returns ZRZinc Volatility ZV CV(+),CL(+), SV(+)Zinc Liquidity ZL CV(+), CL(+), ZL(+)Silver Returns SR SR(-), ER(+)Silver Volatility SV SR(-), SV(+), SL(+), GR(+), GVSilver Liquidity SLGold Returns GR ER(+)Gold Volatility GV GV(+)Gold Liquidity GL GV(+), GL(+), EL(+)Oil Returns OR OV(-)Oil Volatility OV OV(+), EL(+)Equity Returns EREquity Volatility EV ER(-), EV(+)Equity Liqudity EL EL(+), GL(-)* Significant Granger-causality is set at the level of 1% or smaller
Equity volatility does not influence commodities at all, the only influence that it has is a lagged
positive effect on equity volatility, supporting the prior academic evidence on volatility
persistence. Equity liquidity, however, has a positive influence on gold liquidity and oil
volatility, thereby implying that after times of low liquidity in the equity markets, liqudity also
dries up in the gold market and volatility rises in the oil market. A speculative explanation
could be that since gold and oil markets are the most popular commodity investments5, a shock
in the equity markets is easily transmitted to these markets. Clearly, this effect is not
particularly desirable when considering diversification opportunities.
4.2.3 Time dependence
In order to capture the dynamic changes in the spillover patterns, we estimate the model for
two 3-year periods: 1998 – 2001 and 2004 – 2007. This allows to check whether the
interactions have changed over time. The results of the estimation point to strengthening
liquidity channel and increasing cross-autocorrelations among metals. Influence of equity
5 Crude oil and gold respectively have 93mn and 34.5mn futures contracts and 14.8mn and 2.9mn options contracts traded by financial market participants in 2005 (BIS survey (2007))
returns became more pronounced for copper liquidity and zinc returns, while equity liquidity
exerted impact on silver liquidity and oil volatility in the second period, but not in the first one.
Overall, there is a trend on more cross-relations and higher role of liquidity towards the second
sample, yet further research is needed in order to find a more conclusive result.
Clearly, the table above suggests that either the parameters are unstable or some assumptions
of the model are violated. Beta 2 in absolute value is almost always larger than Beta 1,
therefore it is useful to recall how are both parameters estimated:
)()(
),(),(')'(ˆ
11
11 lh
llhh
sVarsVar
csCovcsCovcwsw
6 High volatility sub-sample includes the days when VIX changes by more one standard deviation from the mean, and low volatility is defined when analogous change occurs one deviation below the mean.
),(),(
)()(')'(ˆ
21
22 llhh
lh
csCovcsCov
cVarcVarcwsw
Examination of the intermediate estimation results suggests that although high and low
variance regimes are quite similar, the shift in commodity variance is substantial, while the
shift in covariances is not. This basically implies that the shocks in one market do not change
the correlation between the two markets, thereby making proper identification impossible and
parameters unstable. Intuitively, this situation resembles the case when in an event study the
event shocks are overwhelmed by usual market activity or shocks from other markets, thereby
decreasing the impact of the shocks. Therefore, we are unable to claim the stability of
parameters, interrelation remains inconclusive, and alternative specifications of variance
regimes could be employed in further research.
5 CONCLUDING REMARKS
The study is aimed to investigate the interaction between equity and commodity markets.
Empirical results suggest that shocks in equity and commodity markets are responding to
shocks in one another to a certain extent. While intra-commodity market effects are mostly
transferred through volatility and liquidity, relationship between equity and commodity
markets is transmitted through return and liquidity channels. These effects are more
pronounced for precious metals and oil. These transmission channels seem to be leaning
towards more interaction in recent years as compared to 1998-2001, however, the results are
not clear-cut and require caution when interpreting. It also seems that liquidity channel is
playing increasingly larger role, but this result has to be cross-checked with other proxies for
liquidity.
Determining simultaneous interactions between the two markets did not yield any consistent
results. It could be the case that the transmission parameters are unstable with regard to the
variance in the stock market, or additional shocks prevented estimation the contemporaneous
influences. This may also imply that the markets do not interact contemporaneously, as the
change in commodity market variance does not cause major shifts in correlation patterns
between the two markets. Therefore, interactions may not be substantial on a daily time frame,
but prevail in the longer term.
Inclusion of liquidity parameter into the estimation alters the results only slightly. Volatility is
still very persistent, which is in line with previous results in the literature, and returns are
negatively auto-correlated for some metals. Equity returns have a positive relationship with
gold, silver and copper, and equity liquidity positively Granger-causes volatility in the oil and
gold markets. This undermines the effectiveness of diversification in those markets, therefore
an investor willing to diversify her equity holdings should rather choose aluminium or zinc
investments.
Implications for further research include, first of all, search for better data and better liquidity
proxies for commodities, since the reported bid-ask spread is quite a noisy indicator. Research
on spillovers and their relationship to the share of financial speculator participation in
commodity markets would also be very interesting, and the liquidity channel effects clearly
deserve further study. In addition, effective diversification could be estimated for investors
who face funds withdrawals as the performance falls below a threshold based on the historical
data. Somewhat speculatively, these results suggest that in order to reap diversification
benefits, a leveraged investor should invest into markets which are liquid, but also, in which
most investors have different portfolios and different trading motives than the investor, such as,
for instance, hedging. This would minimize the risk of liquidity squeezes that occur
endogenously. All these issues merit further research, which would usefully complement the
literature on portfolio management in frictionless markets with insights from a more realistic
perspective.
6 REFERENCES
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APPENDIX: MATLAB CODES
Implementation: VAR
close allclear allclc
CopperRaw = xlsread('C:\Documents and Settings\Dalia\Desktop\Master Thesis\alle.xls', 'Copper');
C = xlsread('C:\Documents and Settings\Dalia\Desktop\Master Thesis\all1.xls', 'Copper');Regimes = xlsread('C:\Documents and Settings\Dalia\Desktop\Master Thesis\all1.xls', 'Regimes');
S = xlsread('C:\Documents and Settings\Dalia\Desktop\Master Thesis\all1.xls', 'SP');%Int = xlsread('C:\Documents and Settings\Dalia\Desktop\Master Thesis\all1.xls', 'Interest');