The flight-to-liquidity during the 2007-2009 financial crisis : an analysis of the gold to real estate ratio and the gold to art ratio

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The flight-to-liquidity during the 2007-2009 financial crisis

An analysis of the gold to real estate ratio and the gold to art ratio

Ethan Spanjaard

Abstract

In this thesis the “flight-to-liquidity” is investigated during the 2007-2009 financial crisis. An analysis is conducted on the basis of a created gold to real estate ratio as well as a gold to art ratio. Gold represents the safe liquid asset class and real estate and art the safe illiquid asset class. The results suggest a positive correlation between the gold to real estate as well as the gold to art ratio, whereas both ratios are negatively correlated with the U.S. stock market return. Although an increase in both ratios during the financial crisis is observed, there is not enough statistical evidence to claim this. This is mainly due to the lagging restoration of investors’ trust in the economy in the aftermath of a crisis. The overall conclusion supports the statement that a “flight-to-liquidity” occurred during the financial crisis.

University of Amsterdam Supervisor: Dorinth van Dijk Bachelor Thesis Economics and Finance

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Statement of Originality

This document is written by Ethan Spanjaard, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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1 Introduction

Economic prosperity is characterized by an upward moving economy, as well as an increased likelihood of profits being made by companies and investors. In addition, investors’ willingness to take risks, in order to earn a return on their investment, increases. By doing so, investors encourage each other to invest their capital and, therefore, contribute to the upward moving economy. However, when negative economic developments occur, investors react. When companies start to go bankrupt and people lose their jobs, investors change their preferences as well as their investment behaviour. When analysing investors, switching investment patterns away from risky investments towards safer and liquid investments, are often observed (Bodie, Kane, & Marcus, 2014).

The fact that there seems to be a ”flight-to-liquidity” of investors during times of high uncertainty provides motivation to address this topic. In this thesis the “flight-to-liquidity” during the recent financial crisis of 2007-2009 will be investigated by means of a regression analysis. This analysis is based on the regression model from the working paper of Huang (2015). While Huang (2015) analyses investors’ portfolio preferences towards gold and platinum as similar commodities, this thesis focuses on the investment behaviour towards liquid assets in comparison to illiquid assets.

An analysis will be carried out on the gold market as well as on the real estate and art market, by creating a gold to real estate and a gold to art ratio. The first question answered in this thesis, is whether the gold ratio price (index) increases during the financial crisis. The second question is whether the correlation between the gold to real estate ratio and the U.S. stock market return is negative. The final question to be answered in this thesis is whether a

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The “flight-to-liquidity” is a topic that is discussed frequently. However, the fact that new ratios are created, which have never been used in previous studies, makes this thesis unique. The gold market, combined with the real estate and the art market have to my knowledge never been subject to previous analyses. Therefore, this thesis contributes to the scientific environment by adding complementary information about investors’ portfolio preferences during the 2007-2009 financial crisis.

The structure of this thesis is as following: In the second chapter an overview of the “flight-to-quality” and the “flight-to-liquidity” will be presented and a distinction will be made between safe liquid and safe illiquid assets. Moreover, the relation between the “flight-to-quality” and the “flight-to-liquidity” will be discussed. The third chapter presents and describes the obtained data, the data manipulations as well as the regression analysis. This chapter ends with the presentation of the hypotheses. The empirical results will be discussed in the fourth chapter. In the following chapter, two robustness checks are performed. Finally, this thesis ends with a conclusion as well as implications for further research.

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2 Literature review

Four different topics are addressed in this literature review. First the “flight-to-quality” of investors during financial crises is discussed. This is followed by a discussion of the “flight-to-liquidity”. Furthermore, a distinction is made between safe liquid and safe illiquid assets. The chapter ends with the relation between the “flight-to-quality” and the “flight-to-liquidity”.

2.1 The “flight-to-quality”

During the recent financial crisis of 2007-2009 the terms to-quality” and “flight-to-liquidity” were used frequently throughout the news, referring to the risk-avoiding

behaviour of investors. In times of economic turmoil investors become more risk-averse, which can be observed from their investment patterns. Investors mostly try to hedge their portfolios against negative price swings. Often, as economic and financial uncertainty

increases, investments in safe assets experience a rise in popularity. This is due to the fact that safe assets bear less risk compared to risky assets. The movement of investors from riskier investments to safer investments, also know as the “flight-to-quality”, is a phenomenon often observed during times of uncertainty in financial markets (Bodie, Kane, & Marcus, 2014). Investors can invest in a set of safe assets, which can be distinguished in two groups: safe liquid assets and safe illiquid assets.

2.2 The “flight-to-liquidity”

Liquidity is a meaningful and common term used in all fields of Economics and

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while illiquid assets are relatively more difficult to instantly convert to cash. The underlying question to be analysed in this thesis is whether investors fled more to safe liquid assets than to safe illiquid assets in the financial crisis of 2007-2009. The “flight-to-liquidity”, defined as the movement of investors from illiquid assets to liquid assets (Bodie, Kane, & Marcus, 2014), is a crucial matter in understanding the behaviour of investors in times of economic crises.

Despite the fact that that previous research shows us that the “flight-to-quality” is accompanied with less risk as well as diversification benefits for both safe liquid and safe illiquid assets, the “flight-to-liquidity” overrules in times of economic distress (Beber, Brandt, & Kavajecz, 2009). According to the bond market analysis of Beber, Brandt and Kavajecz (2009), investors demand credit quality and liquidity at different times. They state that during increased market uncertainty, investors from low credit risk countries value liquidity more than credit quality, explained by the large capital flows in and out of the bond market. Moreover, their results suggest that liquidity explains a greater proportion of the sovereign yield spreads (Beber, Brandt, & Kavajecz, 2009). Moreover, the study by Ericsson & Renault (2006) shows that risk and illiquidity of assets are often intertwined, which explains investors demand for liquidity even more.

Another important subject is the cost of liquidity. Empirical findings show that liquidity costs are negatively correlated with stock market returns (Rösch & Kaserer, 2014). Other evidence in the article of Rösch and Kaserer (2014) highlights that stock market liquidity declines in times of financial uncertainty, pointing out the positive relation between market and liquidity risk. Furthermore, they conclude that liquidity costs increase when credit risk and or the default probability rise, which is more likely to occur during crises. This proves the “flight-to-liquidity” during times of crisis (Rösch & Kaserer, 2014).

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2.3 Safe liquid assets: gold

One of the most important and well-known safe liquid assets that investors flee to in times of financial distress is gold. Gold is a valuable asset that is used as consumption good for the jewellery industry and has an increasingly important role for investors as a safe haven during a financial crisis (Bauer & McDermott, 2010; Ciner, Gurdgiev, & Lucey, 2013), as a dollar hedge (Zagaglia & Marzo, 2013),as an inflation hedge (Blose, 2010) and as a portfolio diversifier (Hillier, Draper, & Faff, 2006).

The jewellery industry is volatile in the sense that it is sensitive to the current economic state. In an upward moving economy, the jewellery industry experiences a large increase in demand, due to the prosperous forecasts of the future. However, a downward moving

economy has a large impact on the jewellery industry. As the financial situation of households worsens and unemployment increases, the demand for jewellery as a luxury good declines. Hence, as gold is a precious metal that is widely used in the jewellery industry, the factors influencing the volatility in the jewellery industry have an effect on the gold price as well. Other determinants of the gold price were discussed and analysed in an article of Ismail, Yahya and Shabri (2009), in which they conducted a multiple linear regression in order to predict the future gold price. They found that the Commodity Research Bureau (CRB) future index, the €/$ exchange rate, the inflation rate, the money supply, the NYSE index, the S&P500, Treasury bills and the U.S. dollar index influences the future gold price.

In times of financial distress, gold has empirically proven to be a safe haven asset for the developed country stock markets, providing protection against the downside risks (Bauer & McDermott, 2010). In their article, Bauer and McDermott (2010) stated that the findings

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were strongest for extreme and short-lived shocks, thereby concluding that gold can be seen as a panic buy in the aftermath of such shocks. They distinguish between a weak safe haven, which is defined as an asset that does not co-move with other assets after a negative shock, and a strong safe haven, described as an asset that moves against other assets in times of economic turmoil, thus reducing overall losses. Looking at the major developed stock markets in the recent financial crisis, they identified gold as a strong safe haven in the peak of the crisis. Consequently, gold can be seen as a stabilizer of the financial system due to its role of reducing losses when it is most needed (Bauer & McDermott, 2010). In addition, Ciner, Gurdgiev and Lucey (2013) analysed the safe haven theory in the U.S. and U.K. by using quantile regressions. They emphasise that gold can be considered a safe haven when

experiencing negative exchange rate fluctuations and, thereby, point out the usefulness of gold as a monetary asset for governments.

Another characteristic of gold that should be taken into consideration, is its function of hedging the U.S. dollar (Zagaglia & Marzo, 2013). In their article, Zagaglia and Marzo (2013) investigate what the effect was of the recent financial turmoil on the relation between the U.S. dollar and the gold price. They found that the gold price reacts less to increasing market uncertainty compared to the U.S. dollar, which indicates that gold can be considered a safe financial asset and, therefore, can be added to a currency portfolio in order to hedge against the U.S. dollar.

Furthermore, gold prices do not change in reaction to changing expectations regarding future inflation (Blose, 2010). An increase in expected inflation causes a rise in the interest rate, also known as the Fisher effect. As Blose (2010) stated, this rise in the interest rate causes the cost of carry of gold, defined as the cost of storing a physical commodity over a period of time,

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to increase (Bodie, Kane, & Marcus, 2014). However, as the gold price itself does not change after changing inflation expectations, he notes that it is impossible for investors to set up a speculation strategy in the gold market based on future inflation forecasts. Moreover, he argues that by examining the price of gold, investors are unable to determine market inflation expectations. Therefore, Blose (2010) concludes that gold can be considered an inflation hedge.

Investors investing in equity portfolios recognize gold as a useful portfolio diversifier. In a study of Hillier, Draper and Faff (2006) the investment role of precious metals in financial markets, such as gold, silver and platinum, is investigated. With the use of daily data they examined whether these metals provide diversification benefits for equity portfolio, by looking at their correlation with stock index returns. The empirical findings prove that these metals indeed provide diversification benefits in investment portfolios due to the low

correlation with stock index returns. These findings are support the empirical investigation of Lawrence (2003), in which he found a low correlation between gold and the U.S. stock market and concluded that gold is a suitable portfolio diversifier. Next to that, financial portfolios containing precious metals seem to outperform portfolios without precious metals (Hillier, Draper, & Faff, 2006). Finally, in accordance to the papers discussed in this literature review, Hillier, Draper and Faff (2006) find evidence that precious metals react differently in times of extreme market volatility, providing an investor with hedging power.

2.4 Safe illiquid assets: real estate and art

Whether illiquid assets provide diversification benefits to investors is an issue that has been investigated by researchers in the past decades. As illiquid assets cannot be traded immediately, an investment in such assets becomes temporarily irreversible (Longstaff, 2009).

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Assets such as real estate and art are generally regarded as alternative assets, which can be categorized as illiquid assets.

In the article of Sirmans and Worzala (2003), a review of previous research that has been done on international direct real estate investment is presented to the reader. They concluded that the quality of the data available for research has improved and more

sophisticated analyses have been done over time. What most of the discussed research papers have in common is that the findings conclude that international direct real estate indeed provides investors with diversification benefits, due to the fact that the real estate market is not perfectly correlated with the stock markets (Sirmans & Worzala, 2003). Furthermore, they state that investors should not ignore real estate as an alternative asset when making portfolio allocation decisions, because it reduces overall portfolio risk. Moreover, real estate can be considered a safe investment, due to a low systematic risk compared to traditional financial assets as well as low standard deviations (Sirmans & Worzala, 2003). Finally, when

considering volatility, greater risk-adjusted returns compared to equities and bonds are earned with real estate investments (Hudson-Wilson, Fabozzi, & Gordon, 2003).

Investors always seek to obtain the highest return while minimizing the risk. In order to diversify the risk in their portfolios, investors are continually seeking for alternative assets to invest in. Art is one of those alternative illiquid assets included in portfolios (Campbell, 2008). According to the study of Campbell (2008), the art market, previously non-transparent, becomes more accessible for investors through data on the art market and indices. Although the entry level for direct art investments is still high, investors with enough funds available can still reap the diversification benefits of such an investment. This is due to the low correlation between art and other asset classes, such as stocks (Campbell, 2008). The

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assumption of art being a safe alternative investment is backed by the risk-return trade off analysis in the paper of Campbell (2008), in which art investments tend to have lower average annual standard deviations in comparison to U.S. equities. Considering real estate and art as being safe investments, considering the standard deviations, is relative, due to the fact that both illiquid assets are accompanied with high liquidity risk (Rösch & Kaserer, 2014). Therefore, a better Sharpe ratio1_{will be obtained for both assets, as liquidity risk is not}

captured in the standard deviation.

2.5 The relation between the “flight-to-quality” and the “flight-to-liquidity”

So far the “flight-to-quality” and the “flight-to-liquidity” have been discussed separately, but the link between the phenomena is important to examine. The study of Ericsson and Renault (2006) shows that when a quality” occurs, a

“flight-to-liquidity” appears at the same time. The main reason that explains this occurrence is that risk and illiquidity of assets are linked, as risky assets tend to be less liquid (Ericsson & Renault, 2006). This implies that even though an investment in either real estate or art is assumed to be safe, it still holds liquidity risk (Rösch & Kaserer, 2014). Investors seeking to hedge this

liquidity risk would typically prefer liquid investments, such as for example gold. Therefore, we can conclude that assets which are subject to the “flight-to-quality”, are also subject to the “flight-to-liquidity”.

Both concepts apply to the analysis performed in this thesis. As explained in the methodology chapter, a gold to real estate ratio as well as a gold to art ratio are created, in

1

_{In the book of Bodie, Kane and Marcus (2014), the Sharpe ratio is defined as the average return in}

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order to analyse the “flight-to-liquidity” during the 2007-2009 financial crisis. These ratios are based on the ratio presented in the working paper of Huang (2015).

The gold to real estate ratio as well as the gold to art ratio, in which gold represents the safe liquid asset class and real estate and art the safe illiquid asset class, can increase, decrease or stay constant over time. A constant ratio over time implies that investors’ portfolio preferences towards gold/real estate and gold/art have not changed. The movements of the ratios are the main focus of this thesis.

A decrease of the gold to real estate ratio during a crisis period, such as the financial crisis of 2007-2009, would suggest an increase of investments in real estate, in other words, a movement towards safe illiquid assets. This can be interpreted as a “flight-to-quality”, as investors’ main concern in this case is not liquidity, but the additional returns that can be earned with such an illiquid asset, which are considered more important. Consequently, a fall of the gold to art ratio would imply the same for art investments, where additional returns on art investments are preferred over the liquidity of an investment in gold.

A rise of the ratios during the financial crisis would indicate increased investments in gold, illustrating a movement of investors towards safe liquid assets. In this case investors seem to prefer liquidity of gold to the returns of illiquid real estate and/or art investments. For that reason, a rise of the ratios can be explained as a “flight-to-liquidity”.

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3 Methodology

In this chapter the obtained data, the data manipulation, as well as the correlations between the ratios will be presented. Besides, the regression analysis will be discussed, highlighting the model and the hypotheses. All analyses have been conducted using SPSS.

3.1 Data

3.1.1 Data sources

In order to perform the regression analysis of the gold to real estate ratio as well as the gold to art ratio on the U.S. stock market, several datasets are needed. Firstly, data regarding the U.S. stock market are required. For this purpose, monthly data of the Center for Research in Security Prices (CRSP) Value-Weighted Index are used, which represents nearly all of the U.S. investable equity market. The index-data are collected from the Wharton WRDS database. Secondly, the 3-month Treasury bill rate represents the risk-free rate in the regression model and is obtained from the DataStream database.

For the created liquid asset to illiquid asset ratios, data regarding the gold market, the real estate market and the art market are used. The daily fixing prices in U.S. dollars from the London Bullion Market (LBM) are used in the ratios for gold. In order to show whether

investors fled more to safe liquid than to safe illiquid assets, it is important that for the illiquid assets, real estate and art, data are used that contain direct investments in these asset classes. Therefore, quarterly data of the U.S. National Council of Real Estate Investment Fiduciaries (NCREIF) Property Index are used, which is gathered from the DataStream database. This index is most suitable, due to the fact that it measures investment performance of real estate

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properties acquired in the private market for investment purposes only. The Artprice Global Index is used as art index. This index is obtained from the website Artprice.com and contains quarterly data based on fine art and catalogued auctions records. A time frame from January 2001 until December 2015 is used for all datasets.2

3.1.2 Data manipulation

As mentioned above, the obtained datasets are not similar and need manipulation before performing the regression analysis. First, all datasets were transformed into quarterly datasets. For the gold price dataset consisting of daily data, the average of the daily prices within a quarter, i.e. three months, was taken. The same was done for the datasets consisting of monthly data. Next, before calculating the ratios, the real estate, art and risk-free rate datasets were indexed in order to obtain similar values for the analysis.

The regression functions (1) and (2) below, based on the regression function of Huang (2015), will be used to test the gold to real estate ratio as well as the gold to art ratio,

respectively, on the U.S. stock market.

! ! 𝑙𝑜𝑔𝑅!!!− ! !!! 𝑙𝑜𝑔𝑅!!!! = 𝛽!+ 𝛽!∗ 𝑙𝑜𝑔𝐺𝑅!+ 𝜖!!! (1) ! ! 𝑙𝑜𝑔𝑅!!!− ! !!! 𝑙𝑜𝑔𝑅!!!! = 𝛽!+ 𝛽!∗ 𝑙𝑜𝑔𝐺𝐴!+ 𝜖!!! (2)

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In both functions !_! !!!!𝑙𝑜𝑔𝑅!!!−𝑙𝑜𝑔𝑅!!!! is the dependent variable. This variable is

described as the sum of natural logarithms of the CRSP indexed values up to and including the quarter h,3_{multiplied by the factor}!

!, minus the natural logarithm of the indexed Treasury

bill rates at quarter h. In other words, this dependent variable can be described as the natural logarithm of the U.S. stock market excess return. A slight difference between the regression function in the paper of Huang (2015) and the functions used in this thesis, is the factor multiplied with the sum of natural logarithms in the dependent variable. As Huang (2015) analyses monthly datasets, he multiplies the sum of natural logarithms in the dependent variable by the factor !"_!. While in this thesis all data are transformed into quarterly data, the sum of natural logarithms in the dependent variable are multiplied by the factor !_!.

In function (1), 𝑙𝑜𝑔𝐺𝑅! is the independent variable, defined as the natural logarithm of

the indexed gold price divided by the real estate index. Consequently, 𝑙𝑜𝑔𝐺𝐴! is the

independent variable in function (2), defined as the natural logarithm of the indexed gold price, divided by the art index. In both functions 𝜖!!! is defined as the error term. Both

independent variables are so-called lagged variables, which means that these variables are always one period behind (t-1), compared to the period of the dependent variable (t).

Table 3-1 exhibits the descriptive statistics after manipulating the data and following the calculation steps presented above. Looking at the descriptive statistics, one can observe that 59 observations were the result of the manipulation process towards quarterly data, after the imposed lag of the independent variable.

3

_{h = the horizon. For example: if h=2, the dependent variable is the sum of natural logarithms of CRSP}

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Table 3-1: Descriptive statistics

N Mean St. Deviation Minimum Maximum

LReturn 59 13.35 0.17 13.15 13.82

LGR 59 0.42 0.34 -0.01 1.07

LGA 59 0.50 0.43 -0.14 1.24

Table 3-2 presents the description of the variables used in the analysis as well as the terms that will be used for convenience, throughout the remainder of this thesis.

Table 3-2: Description of the variables and terms used throughout thesis

Variable Definition Description Term

Dependent ₄

ℎ 𝑙𝑜𝑔𝑅!!!− !

!!!

𝑙𝑜𝑔𝑅!!!!

Natural logarithm of the U.S. stock market excess return LReturn

Independent 𝑙𝑜𝑔𝐺𝑅 Natural logarithm of the gold to real estate ratio LGR Independent 𝑙𝑜𝑔𝐺𝐴 Natural logarithm of the gold to art ratio LGA

3.1.3 Correlation between ratios

A co-movement between the ratios is conceivable. Firstly, both ratios consist of gold, which is considered a safe liquid asset. Secondly, the ratios consist of real estate an art, which are both considered safe illiquid assets. In Table 3-3, the correlations between the dependent and independent variables are presented.

Table 3-3: Correlations

Variable LReturn LGR LGA

LReturn 1

LGR -0.727 1

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Looking at Table 3-3, we find a positive correlation between the gold to real estate and the gold to art ratio of 0.915. Even though both ratios consist of safe illiquid assets, the

correlation is not 1 as a result of real estate and art being assets with different characteristics. The negative correlations between the dependent variable and the ratios will be discussed in the next chapter.

3.2 Regression analysis

In this thesis, a regression analysis is implemented, based on the model used in the working paper of Huang (2015). A gold to real estate ratio, as well as a gold to art ratio, are created in order to conduct the analysis on the U.S. stock market. The idea behind these created ratios is to test for negative correlations with the U.S. stock market in times of crisis. Furthermore, another issue to be investigated is whether investors fled to safe liquid assets rather than to safe illiquid assets during the recent financial crisis of 2007-2009. The ratios created in this thesis measure investors’ preferences towards gold, real estate and/or art, by showing the movement of the ratios over time. Therefore, three hypotheses will be examined.

I. The gold ratio price (index) increases during the financial crisis. Statistically: H0: µ!" ≤ µ!"# and H1: µ!" > µ!"#

II. There is a negative correlation between the gold to real estate ratio and the U.S. stock market return.

III. There is a negative correlation between the gold to art ratio and the U.S. stock market

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The first hypothesis is tested using an independent samples t-test, in order to check whether the movement of the ratios in- and outside of the crisis are significant. The data are divided into two groups, one group containing data in the crisis period from Q1 2007 – Q4 2009,4_{another group including the data outside of the crisis period. Furthermore, the second}

and third hypotheses are tested using linear regressions of LGR and LGA on LReturn,

respectively. The linear regressions are performed, in order to check whether the betas, i.e. the correlations between the dependent and the independent variable, are significant.

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4 Results

The empirical results regarding the presented hypotheses will be presented and discussed in this chapter.

4.1 Hypothesis I

H0: The gold ratio price (index) inside the crisis period will be smaller or equal to the gold ratio price (index) outside the crisis period (𝝁𝒊𝒏 ≤ 𝝁𝒐𝒖𝒕)

H1: The gold ratio price (index) inside the crisis period will be higher than the gold ratio price (index) outside the crisis period (𝝁𝒊𝒏> 𝝁𝒐𝒖𝒕)

An independent samples t-test is performed, in order to test the first hypothesis. First, the data are grouped into two groups. The first group, termed “in”, contains only data within the crisis period, i.e. from Q1 2007 until and including Q4 2009, which are 12 observations in total. The second group, named “out”, contains the data points outside of the crisis period, i.e. 47 observations. Table 4-1 displays the means and standard deviations of both groups, for both independent variables.

Table 4-1: Group Statistics

N Mean St. Deviation LGR out in 47 12 0.42 0.42 0.37 0.22 LGA out in 47 12 0.49 0.55 0.47 0.26

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Art is not an ordinary investment, and compared to stocks, there is not a common marketplace in which art is traded on a daily basis. Most art is traded by means of auctions and over-the-counter deals. In addition, real estate is also an illiquid asset. However, it is less difficult to sell a house or building, in comparison to a piece of art, as there is no common marketplace available as well as arts’ unique and elusive characteristic. The fact that trading art seems to be harder, is accompanied with additional liquidity risk. Ericsson and Renault (2006) mentioned in their study that most risky assets tend to less liquid. Although real estate and art are both illiquid assets, art seems to be more illiquid, and so, bears more liquidity risk as an investment.

For the independent samples t-test, equal variances are assumed for both periods. The t-value, the degrees of freedom (df) and the p-value of the test, for a confidence interval of 95%, are listed in Table 4-2.

Table 4-2: Independent samples t-test

t-value df p-value

LGR -0.035 57 0.972

LGA -0.418 57 0.678

Observing the gold to real estate ratio, a p-value of 0.972 is found, which is not significant for a t-value of -0.035 and 57 degrees of freedom. Likewise, the gold to art ratio is not significant, due to a p-value of 0.678, a t-value of -0.418 and 57 degrees of freedom. Hence, with the empirical results provided in Table 4-2, we are unable to state that the gold ratio price (index) increases during the financial crisis and, therefore, the null hypothesis is rejected.

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The reason for the rejection of the null hypothesis can be analysed by looking at the movements of the variables LReturn, LGR and LGA in the research period of this thesis. The movement of the ratios, LGR as well as LGA, is provided in Figure 4-1.

Figure 4-1: Movement of LGR and LGA in the period 2001-2015

When looking at Figure 4-1, we observe a clear co-movement between the two ratios. Analysing the crisis period from 2007 until 2009, we notice that both ratios increase. This rise indicates that investors tend to prefer gold investments compared to real estate as well as art investments, and thus, a ”flight-to-liquidity” occurs. As gold is empirically proven to be a safe haven during periods of financial distress, both ratios increasing seems to be a plausible observation (Bauer & McDermott, 2010).

Nevertheless, the empirical findings provided in Table 4-2, suggest that this upward trend is not significant during the financial crisis. The main reason causing these insignificant results is that both ratios keep increasing after the crisis period until approximately Q3 2011, which indicates a “delay effect” in both ratios. This “delay effect” is detected, due to the fact that investors’ trust in the economy needs to restore in the aftermath of the crisis. As trust in

-0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGR LGA

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the economy restores, investors dare to take more risk and, consequently, start investing more in illiquid assets (Ericsson & Renault, 2006).

4.2 Hypothesis II

There is a negative correlation between the gold to real estate ratio and the U.S. stock market return

A “flight-to-liquidity” is established by a movement of investors towards liquid assets (Bodie, Kane, & Marcus, 2014). In the performed analysis, this would indicate an increasing gold to real estate ratio as well as gold to art ratio, due to the liquid and illiquid characteristics of gold as well as real estate and art, respectively. Figure 4-1 supports this, and illustrates increasing ratios during, and in the aftermath of, the financial crisis.

Considering Table 3-3, the gold to real estate ratio is negatively correlated with the U.S. stock market return, with a correlation of -0.727. Table 4-3 demonstrates the regression

statistics of the linear regression of the gold to real estate ratio on the U.S. stock market return.

Table 4-3: Linear regression of LGR on LReturn

β t-value df p-value

LGR -0.727 -7.992 58 0.000

MSR = 0.916 df = 1 MSE = 0.014 df = 57

R!_{= 0.528} _{F = 63.87} _{p-value = 0.000}

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i.e. the 𝛽, is significant. The same conclusion can be drawn for an F (1,57) of 63.87 and a p-value below 0.001. Thus, enough statistical evidence was found to state that there is a negative correlation between the gold to real estate ratio and the U.S. market return.

The empirical findings are visualized in Figure 4-2, which illustrates the movement of LReturn, and Figure 4-3, combining the movements of the LGR and LReturn in the research period.

Figure 4-2: Movement of LReturn in the period 2001-2015

Figure 4-3: Movement of LGR and LReturn in the period 2001-2015

12.65 12.80 12.95 13.10 13.25 13.40 13.55 13.70 13.85 14.00 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LReturn 12.65 12.80 12.95 13.10 13.25 13.40 13.55 13.70 13.85 14.00 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGR LReturn

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From Figure 4-2 we identify a decrease of LReturn during the financial crisis, but this variable starts rising slowly in the period after the financial crisis. When combining this with the movement of the gold to real estate ratio in Figure 4-3, a clear negative correlation is illustrated between both variables during the crisis period. While the gold to real estate ratio increases, the U.S. stock market return decreases. The claim of gold being a portfolio

diversifier as well as a safe haven during times of economic distress is supported by these findings (Hillier, Draper, & Faff, 2006; Bauer & McDermott, 2010).

4.3 Hypothesis III

There is a negative correlation between the gold to art ratio and the U.S. stock market return The negative correlation between the gold to art ratio and the U.S. stock market return was first presented in Table 3-3. Depicting this correlation in Figure 4-4 helps analysing the changes of the variables during the research period.

Figure 4-4: Movement of LGA and LReturn in the period 2001-2015

12.65 12.80 12.95 13.10 13.25 13.40 13.55 13.70 13.85 14.00 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGA LReturn

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Figure 4-4 clearly shows a negative correlation between the gold to art ratio and the U.S. stock market return during the financial crisis.

Rösch and Kaserer (2014) note in their paper that stock market liquidity declines in times of financial uncertainty, which clarifies the decreasing stock market return, as well as the “flight-to-liquidity”, for instance to gold. Furthermore, the study of Beber, Brandt and

Kavajecz (2009) point out that during increased market uncertainty, investors from low credit risk countries value liquidity more than credit quality. This again, could be seen as an

explanation for the rising gold to art ratio during the 2007-2009 crisis.

Whether this observed negative correlation is significant, is tested by means of a linear regression. The regression statistics are listed in Table 4-4.

Table 4-4: Linear regression of LGA on LReturn

β t-value df p-value

LGA -0.733 -8.130 58 0.000

MSR = 0.931 df = 1 MSE = 0.014 df = 57

R!_{= 0.537} _{F = 66.10} _{p-value = 0.000}

Analysis of the regression coefficients tells us that the negative correlation between LGA and LReturn is statistically significant. A 𝛽 of -0.733, a t-value of -8.130 as well as a p-value below

0.001 were found. In addition, a 𝑅!_{of 0.537 was found, which means that 53.7% of the}

variance is explained by the model. Finally, for F (1,57), a value of 66.10 as well as a p-value below 0.001 was found. Therefore, with enough statistical evidence, we can state that there exists a negative correlation between the gold to art ratio and the U.S. stock market return.

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5 Robustness checks

The two robustness checks performed will be discussed briefly in this chapter. First, the results of alternative indices will be examined. The results of random sampling will be considered afterwards.

5.1 Alternative indices

In the executed analysis, the CRSP index was used as market index for the dependent variable. The main results suggested significant negative correlations of -0.727 for the gold to real estate ratio as well as -0.733 for the gold to art ratio, with the U.S. stock market return. In this section the results for two alternative indices, the Dow Jones Industrial Average as well as the NASDAQ Composite, are analysed. These alternatives are chosen for the reason that they are among the most followed U.S. stock market indices. Whereas the CRSP index represents the total U.S. stock market, the Dow Jones index is based on the 30 largest U.S. based publicly owned companies traded on the New York Stock Exchange (NYSE) as well as the NASDAQ. The NASDAQ index represents all stocks that trade on the NASDAQ stock market. The descriptive statistics including the alternative indices are presented in Table 5-1.

Table 5-1: Descriptive statistics including Dow Jones and NASDAQ

N Mean St. Deviation Minimum Maximum

LReturn 59 13.35 0.17 13.15 13.82

LReturnDJ 59 13.19 0.19 12.97 13.81

LReturnNASDAQ 59 12.58 0.29 12.30 13.53

LGR 59 0.42 0.34 -0.01 1.07

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As can be seen in the descriptive statistics, new dependent variables are created using the Dow Jones as well as the NASDAQ as market return indices. The correlations between the variables listed in Table 5-1, are demonstrated in Table 5-2.

Table 5-2: Correlations including Dow Jones and NASDAQ

Variable LReturn LReturnDJ LReturnNASDAQ LGR LGA

LReturn 1

LReturnDJ 0.902 1

LReturnNASDAQ 0.533 0.824 1

LGR -0.727 -0.615 -0.174 1

LGA -0.733 -0.508 0.021 0.915 1

Comparing the correlations of the dependent variables with LGR as well as LGA, one observes that the dependent variable LReturn, which consists of the CRSP index, has the strongest negative correlations. This seems credible, due to the fact that the CRSP index represents the whole U.S. stock market. In addition, the correlations found for the Dow Jones, are also negative, but to a smaller extent. As the correlation between the CRSP and the Dow Jones is high, the correlations with LGR as well as LGA are negative as well. The fact that these correlations are less negative compared to the CRSP index, can be explained by the fact that the Dow Jones only represents the 30 largest companies traded on the NYSE and

NASDAQ. The correlations for the NASDAQ, however, are slightly negative for LGR and positive for LGA. The reason for this is a weaker correlation between the CRSP and the NASDAQ. Finally, the high positive correlation between the Dow Jones and the NASDAQ is due to their composition, where they represent a part of the NASDAQ stock market as well as

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the whole NASDAQ stock market, respectively. Therefore, it may be concluded that, the CRSP index is the most suitable market index for the performed analysis, in other words, the results are robust to the chosen stock market index in this thesis. The correlations in Table 5-2 are visualized in the Appendix in Figure A, B and C.

5.2 Random sampling from original sample

A second robustness check is conducted on the difference between the correlations of the original sample as well as for the randomly selected sample. Table 5-2 shows the

correlations found for the entire sample as well as the randomly selected sample. The differences between the correlations presented, are tested using a Z-test.

Table 5-2: Correlations with LReturn for the entire sample (n=59) and the randomly selected sample (n=27)

Variable LReturn obs n=59 n=27

LReturn 1 1

LGR -0.727 -0.784 LGA -0.733 -0.707

Starting with LGR, the Z-score for the difference in correlations is 0.55. Accordingly, a p-value of 0.291 was found for this difference. For LGA, a Z-score of -0.22 as well as a p-value of 0.413 were found.

The findings suggest that the difference between the model and the randomly selected sample is not significant, taking both p-values into consideration. Stated differently, the correlations are not significantly different. Therefore, it may be concluded that the results are

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6 Conclusion

This thesis examines whether a “flight-to-liquidity” occurred during the 2007-2009 financial crisis, on the basis of an analysis of a gold to real estate ratio as well as a gold to art ratio. In order to analyse the “flight-to-liquidity” phenomenon, three hypotheses were tested. The first hypothesis to be tested, was whether the gold ratio price (index) increased during the financial crisis. Although observing an increase in both ratios during the financial crisis, the hypothesis was rejected. The explanation for this insignificancy can be linked to the lagging restoration of investors’ trust after a crisis period.

In addition, a positive correlation of 0.915 between the gold to real estate ratio as well as the gold to art ratio was found. This co-movement seemed reasonable, due to the fact that both ratios consist of gold and a safe illiquid asset. Moreover, whether there exists a negative correlation between the gold to real estate ratio and the U.S. stock market return was also subject to the analysis. The significant negative correlation found, with a value of -0.727, supports the idea of gold being a hideaway during times of economic turmoil. Finally, the negative correlation between the gold to art ratio and the U.S. stock market return was questioned in this thesis. The negative correlation of -0.733 was again significant, which also supports the idea of gold as a hideaway during financial crises.

Consistent with the literature, the results found in this thesis, suggest that there indeed was a “flight-to-liquidity” during the financial crisis of 2007-2009. It is important to mention that this claim is based on an analysis of the U.S. stock market as well as gold, real estate and art as the only liquid and illiquid investments.

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liquidity” occurred during the financial crisis in emerging markets. Next to that, future research can extend the number of liquid as well as illiquid assets to be analysed. The results have shown that the rise of the gold ratio price (index) during the financial crisis was not significant, for the reason that investors’ trust in the economy has to restore in the aftermath of a crisis. This lag in investors’ trust, in other words the “delay effect”, could be a source for future studies.

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Appendix

Figure A: Movement of LGR, LGA and LReturn in the period 2001-2015

Figure B: Movement of LGR, LGA and LReturnDJ in the period 2001-2015

Figure C: Movement of LGR, LGA and LReturnNASDAQ in the period 2001-2015

12.65 12.80 12.95 13.10 13.25 13.40 13.55 13.70 13.85 14.00 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGR LGA LReturn 12.40 12.60 12.80 13.00 13.20 13.40 13.60 13.80 14.00 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGR LGA LReturnDJ 11.60 11.80 12.00 12.20 12.40 12.60 12.80 13.00 13.20 13.40 13.60 13.80 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Q1 (2 00 1) Q2 ( 20 01 ) Q3 ( 20 01 ) Q4 ( 20 01 ) Q1 ( 20 02 ) Q2 ( 20 02 ) Q3 ( 20 02 ) Q4 ( 20 02 ) Q1 ( 20 03 ) Q2 ( 20 03 ) Q3 ( 20 03 ) Q4 ( 20 03 ) Q1 ( 20 04 ) Q2 ( 20 04 ) Q3 ( 20 04 ) Q4 ( 20 04 ) Q1 ( 20 05 ) Q2 ( 20 05 ) Q3 ( 20 05 ) Q4 ( 20 05 ) Q1 ( 20 06 ) Q2 ( 20 06 ) Q3 ( 20 06 ) Q4 ( 20 06 ) Q1 ( 20 07 ) Q2 ( 20 07 ) Q3 ( 20 07 ) Q4 ( 20 07 ) Q1 ( 20 08 ) Q2 ( 20 08 ) Q3 ( 20 08 ) Q4 ( 20 08 ) Q1 ( 20 09 ) Q2 ( 20 09 ) Q3 ( 20 09 ) Q4 ( 20 09 ) Q1 ( 20 10 ) Q2 ( 20 10 ) Q3 ( 20 10 ) Q4 ( 20 10 ) Q1 ( 20 11 ) Q2 ( 20 11 ) Q3 ( 20 11 ) Q4 ( 20 11 ) Q1 ( 20 12 ) Q2 ( 20 12 ) Q3 ( 20 12 ) Q4 ( 20 12 ) Q1 ( 20 13 ) Q2 ( 20 13 ) Q3 ( 20 13 ) Q4 ( 20 13 ) Q1 ( 20 14 ) Q2 ( 20 14 ) Q3 ( 20 14 ) Q4 ( 20 14 ) Q1 ( 20 15 ) Q2 ( 20 15 ) Q3 ( 20 15 ) Q4 ( 20 15 ) LGR LGA LReturnNASDAQ

The flight-to-liquidity during the 2007-2009 financial crisis : an analysis of the gold to real estate ratio and the gold to art ratio