gold commodity trading risk hedging strategies

2. Review of lit

There is a big literature, prior to the 2007 Round Fiscal Crisis, documenting the general kinship between principal commodities, videlicet golden and oil ¹ , and a wide range of economics factors. The strength of the relationship would then provide the basis for effective portfolio composition. If a weak or independent association is found, a commodity is said to exhibit the properties of a risk-mitigating instrument. Christie-David et alii. (2000) use of goods and services intra-day information covering the period 1992 through 1995, and examine the responses of gold and silver price trends to every month macroeconomic-based tidings releases via a set of nonparametric tests. Specifically, the study finds that gold responds strongly to the release of the CPI, and also to a great extent, the releases of unemployment rate effects, GDP, and PPI. In accession, the study finds that gold responds sapless to the free of the federal deficit. In a similar study, Cai, Cheung, and Wong (2001) employ a regression of 5-little gold futures on 23 US economics announcements from 1994 to 1997. Specifically, a two-step GARCH estimation and a flexible Fourier function are conjunct to account for intra-mean solar day counte volatility in gold futures. Among the economic science announcements in hand, the subject area finds employment reports, GDP, CPI, and subjective income to exert the all but significant impact upon gold prices. Lawrence (2003) considers quarterly time serial data from 1975 to 2001 and applies a VAR system to test the relationship between gold and a comprehensive set of macroeconomic variables. The study finds gold to make up broadly independent of economic cycles throughout the specified point, suggesting that gold whitethorn serve as a multipurpose portfolio diversifier.

The fear of currency devaluation arguably increases the demand for gold. To that end, many scholars recognize the merit of using gold as a hedging instrument against exchange rate uncertainty. Capie et al. (2005) examine gold as a hedging instrument by means of assessing weekly data on the atomic number 79 price and sterling-dollar bill and yen-dollar bill commute rates. The information covers a 30-year time span, beginning 8 January 1971 and ending 20 February 2004. A collinear autoregressive distributed lag (ARDL) model is used. The results report a pessimistic relationship betwixt gold returns and both the dollar-yen too arsenic the buck-greatest exchange rates, suggesting that gold can be victimized as a hedging pawn against US dollar fluctuations, all the same, the strength of this relationship has shifted in the long-run primarily due to social, semipolitical, and economic events. Other studies sought-after to examine the impact of economic science shocks on gold prices. Tully and Lucey (2007) examine the relationship between monthly gold prices and a typeset of macroeconomic variables over the period 1984 to 2003, highlighting the 1987 and 2001 equity market downturns. The authors use an asymmetric power GARCH model (APGARCH) to excuse this relationship. The study concludes that the rate of the US dollar is the primary political economy variable which influences golden prices in their findings.

Over the past decade, a growing research interest has gravitated towards investigating volatility spillovers and transmission mechanisms between food for thought and vim commodities, financial securities, Au, and other precious metals, using a variety of different datasets and econometric models. Moreover, a multitude of research papers emphasized the grandness of differentiating between a "hedge", a "diversifier", and a "safety haven". In a leading subject field, Baur and Lucey (2010) define a hedge as an asset that is uncorrelated or negatively correlated with another asset or portfolio on average. In other words, a fudge does not continue the property of minimizing losings in periods of market Sturm und Drang since it English hawthorn demonstrate a positive correlation coefficient with other asset during so much periods, and a damaging correlation during not-crisis periods with a indirect correlation on average. ² A diversifier is defined every bit an asset that is positively, simply non absolutely, correlated with another asset Beaver State portfolio along average. Consanguine to a hedge, a diversifier does not have the characteristic of minimizing losings under extremely volatile market conditions since the direct correlation condition is expected to hold connected average. A safe haven is defined equally an asset that is either uncorrelated or negatively related to with another asset or portfolio on average, particularly during periods of market stress. Namely, the correlation coefficient may generally be positive or negative, but tends to be zero or negative specifically during turmoil periods. Thus, if the safe seaport plus is negatively correlated with another asset during turbulent periods, it is subsequently curtailing losings for the investor, namely because the price of the haven asset would comprise moving in the opposite charge of the other asset in question (i.e. when the price of the haven asset increases, the price of the some other asset decreases and vice versa). Further, Baur and Lucey (2010) prove the constant and time-varying dynamics between US, UK, and German line of descent-bond-gold returns to identify whether metallic is a hedge, diversifier, or a invulnerable haven. To that end, the authors carry out a GARCH-based regression with dummy variables for lower quantiles. The dataset comprises of each day MSCI old-hat and bond paper price indices as well as United States closing gold spot prices, starting 30 November 1995 and ending 30 November 2005. Stock and bond prices are in local currency (i.e. dollar, superlative, and euro) and gold price is converted when needed. ³ In the subsample analysis, a bearish market is identified between deuce optimistic markets. The study concludes that gold can make up used (on the average) as a hedging against stocks and as a innocuous haven during extremely volatile conditions all told of the three stock markets under probe. Even so, gold cannot function as a uninjured oasis against bonds in any of the markets selected. In addition, the bailiwick demonstrates via a portfolio analysis that the safe haven characteristic is short-lived. In other words, investors run to persist to gold for a limited period. More specifically, investors are expected to buy gold when standard returns are negative and sell it when market confidence is restored. It is widely accepted that gold occupies a distinctive investment position when compared with other precious metals, in this it can be used Eastern Samoa a tool to mitigate portfolio gamble. Sari, Hammoudeh, and Soytas (2010) examine the degree to which precious metal returns and the dollar mark-euro rate of exchange respond to oil price shocks. Specifically, the study analyzes the co-movements and information transmittance among the price trends of four precious metals (gold, silver, platinum, and Pd), the dollar-euro rally rate, and oil price. To examine the extent to which the variance of a variable can be explained by shocks to other variable, the authors use a VAR-founded generalized forecast error variance decomposition (FEVD) function. In simpler terms, a generalised FEVD function is regarded as an out-of-sample causality analysis. A generalized impulse response function (IRF) is then accustomed capture the direction of the dynamic responses of a shifting to changes in other variables. To test for cointegration (i.e. an equilibrium relationship), the authors employ the methods developed by Johansen (1991, 1995)) and Johansen and Juselius (1990), in addition to the bounds testing approach developed past Pesaran, Shin, and David Roland Smith (2001). Daily metre serial data are obtained from 1999 to 2007. In this historic period, cardinal major events are considered: the OPEC price band in 2000, the 9/11 attacks in 2001, and the Iraq War in 2003. The a posteriori findings indicate the front of a alcoholic asymmetrical relationship between embrocate and artful metals in the short-foot race. In other words, precious metallic prices are temporarily sensitive to a electrical shock in any of the prices of the other precious metals to boot to the convert order. Due to their limited supplies, gold, and less, argent, may beryllium exploited as hedging tools against short-term inflationary expectations, specifically in the event that the one dollar bill weakens against the euro. However, the cointegration 'tween oil colour and precious metals tends to weaken over the long-be given, consequently decreasing the endangerment-step-dow benefits of investment in wanted metals. In a analogous research paper, Hammoudeh, Yuan, McAleer, and Thompson (2010) investigate the conditional volatility and correlation coefficient dependency and interdependency among aureate, silver, platinum, and atomic number 46, whilst accounting for geopolitics inside a multivariate system. To canvass these interactions, the study employs multivariate GARCH models; specifically, VARMA-GARCH and DCC-GARCH. Daily time serial data are noninheritable for the 4 precious metals, the federal store value ⁴ , and the dollar-euro exchange rate, over the period 1999 through to 2007. In addition, the authors compete that since precious metals (gold in particular) are sensitive to geopolitical crisis episodes, a geopolitical blank unsettled is included to mark the beginning of the 2003 Iraq State of war. The authors believe this event to Be to a greater extent pervasive than the 9/11 attacks. The empirical findings demonstrate that all precious metals respond moderately to own news show, and to a lesser stage, to news spilled over from other metals in the short-run. This finding accentuates the significance of hedge in the short-run, however, the gains are limited when treasured metals are qualified against one some other. Happening the other hand, precious metals show solid excitability sensitivity to own prior shocks in the long-run. The strongest sensitivity is found for silver and the weakest is institute for gold. The learn finds spillover volatilities to be more pregnant than spillover shocks or news, inferring that these volatilities can be predicted. Furthermore, Precious metal returns tend to showing stronger sensitivity when the dollar-euro rate of exchange and northern monetary resource rate are considered. The study chiefly concludes that gold is the safest hedging tool against exchange rate volatility. Hoang (2011) examines the role of gold in the diversification of daniel Chester French portfolios over the period 2004 done to 2009, covering the 2007 Worldwide Financial Crisis. The study analyzes monthly data on French stocks, bonds, paper aureate, and physical gold, in an effort to examine the ascent in gilded prices in France during the chosen period. The generator follows the efficient frontier and portfolio diversification approach developed by Markowitz (1952, 1959)). The main findings suggest that the growth in gold prices is largely explained by the flimsy correlation coefficient that exists between atomic number 79 and stocks, and, to a small extent, 'tween gold and bonds, enticing investors to expand the role of aureate in the diversification of French portfolios. Put differently, the internalization of gilt in French portfolios significantly reduces their risk, and successively, significantly improves the performance of these portfolios. However, the study finds forcible gold to Be a more efficient portfolio diversifier than wallpaper gold.

Furthermore, Kim and Dilts (2011) examine the causal relationship between the value of the US dollar and the prices of gold and oil, using unit of time data for the period January, 1970 through July, 2008. The authors commence aside producing Augmented Dickey-seat-Fuller (ADF) and Philippe-Perron (PP) unit origin tests to determine the order of integration of the variables. A VAR process is then practical in order to assess the evolution and interdependencies among the variables. To test for cointegration, the authors rely on Johansen's multivariate cointegration tests. The cointegrated variables are subsequently depicted by an error correction model (Electronic countermeasures) which separates the short-run dynamics and the long-unravel equilibrium condition of the variables. The presence of causality among the variables is captured using Farmer causality testing. Finally, the authors perform an IRF and FEVD analytic thinking to examine the impulsive relationships among the variables. The results confirm the presence of a importantly negative kinship between the rate of the dollar and the price of both commodities. This determination suggests that the price of some gold and oil increases as the treasure of the dollar sign decreases. Moreover, a significantly positive relationship is found to subsist between gold and oil prices, inferring that golden and oil May both serve atomic number 3 risk-alleviating tools against rising uncertainty in the value of the dollar bill. Specifically, investors tend to pursue safer commodities as the volatility in the terms of the one dollar bill increases, confirming the premise for a flight to quality. Similar results are echoed by Bhunia (2013), who investigates the long-run causal relationship among crude oil price, domestic gold price, and a natural selection of financial variables, namely interchange rates and stock price indices in India. Daily information are considered for the analysis, covering the period 2 January 1991 to 31 October 2012. The method framework includes an ADF building block settle test, a Volt-ampere-settled Johansen cointegration psychoanalysis, and a Husbandman causality test. The empirical findings suggest the comportment of a long-run equilibrium relationship among all of the underlying variables. Also, a significant bidirectional causality is institute to exist between gold and stock prices. The study explains that despite the occurrence of stellar international crises during the selected period (the 1997 Asian Financial Crisis, the 2007 Global Commercial enterprise Crisis, and the 2010 European Debt Crisis), gold prices continued to increase in India because of its safe harbor investiture status. When compared to inunct, amber continues to be a preferred oasis investment of choice in Bharat. The study postulates that increasing anele prices will increase production costs, thereby decreasing cash flows and, subsequently, oil stock price. Therefore, investing in gold increases its price and alleviates the fear of future loss. India remains the world's largest market for gold using up. Put together with China, both markets account for over 50% of global necessitate, according to the World Gold Council.

The importance of spillover effects between gold and new market variables in formulating best hedge strategies is generally swell-documented by a number of authors in the extant literature. In a worthy study, Mensi, Beljid, Boubaker, and Managi (2013) swear on a Volt-ampere-GARCH econometric framework to investigate constant conditional correlations and volatility spillovers crossways the SdanA;P 500 and commodity monetary value indices for energy, solid food, gold, and beverages. Daily closing returns are considered for the period 2000 finished 2011. This time frame is specified ready to evaluate the response of commodity market returns to the effects of three star crises: the events of 9/11, the 2003 Iraq War, and the 2007 Global Fiscal Crisis. The VAR-GARCH approach includes the multivariate (constant conditional correlation) CCC-GARCH in which correlations between system of rules shocks are counterfeit invariable. The continual provisionary correlations between equities and commodities are all formal but marginally greater than aught, inferring that gains can be ready-made aside investing in the Sdanamp;P 500 and trade good markets that are described by weaker correlation estimates. Also, there is evidence of a significant volatility transmission among the Sdanadenylic acid;P 500 and all of the commodity markets selected. These spillovers have markedly hyperbolic throughout the overall period, but to a greater extent particularly during the crisis episodes. Further, the study shows that the prior shocks and excitability of the Sdanamp;P 500 strongly, and positively, mold golden and oil colour return trends; more then than the opposite. The optimal portfolio weight and hedge ratio analysis highlights the short and long-terminal figure benefits of mostly including commodities to a stock-diversified portfolio ready to improve its overall performance. The results validate that metal is a relatively low-cost hedge against equities in comparison with other commodities, albeit not the cheapest choice. Furthermore, Gencer and Musoglu (2014) examine the bidirectional volatility transmittance mechanisms between gold, stocks, and bonds in Turkey, over the period June 2006 through to November 2022. Daily time serial data are analyzed on the basis of a quantity GARCH framing, developed as BEKK-GARCH by Engle and Kroner (1995). The authors describe the overall period as overly fickle collectible to the fact that it highlights the 2007 Global Financial Crisis, the 2010 European Debt Crisis, and the FED's monetary policy decisions in mid-2013. The results confirm the front of a significantly negative bidirectional shock transmission between gold and stocks, disposed the extremely negative correlation recovered 'tween the two variables throughout the total period, albeit much stronger during the 2007 Global Financial Crisis. This finding underlines the safe haven place of Au against stocks, as defined away Baur and Lucey (2010). Further, the results indicate a significant bidirectional volatility transmission that exists between gold and stocks, whereby antecedent golden restoration volatility negatively impacts current stock return volatility, yet, the affect is affirmatory from stocks to gold. This result is similar to that which has been reported by Mensi et al. (2013). Most likely, this is due to the shortcomings connected with the CCC-GARCH specification which fails to capture cross-market volatility spillover personal effects. The authors explain that the comovement of gilded and stocks is driven by the rising level of uncertainty in the securities market; gum olibanum, succeeding with the findings of Baur and Lucey (2010) and Coudert and Raymond (2011), Gencer and Musoglu (2014) classify aureate as a washy safe haven plus during the overall (extremely) volatile historical period. Furthermore, the study finds a unidirectional positive shock also as a bad unpredictability transmission system from atomic number 79 to bonds, namely because Turkish investors are traditionally inclined to buy gold when interest rates descent. Finally, optimal portfolio weights and hedge ratios are calculated for the gilt-blood line and gold-enslaved portfolio combinations. Gold is constitute to overbalance both stocks and bonds in the optimal portfolios. In addition, the gold-ancestry hedge ratio is found to be disinclined, outperforming the chromatic-Bond portfolio in this same nervure, which gives rise to the affirmation that gold optimizes portfolio efficiency.

In addition to the message of optimal portfolio composition, numerous scholars continue to investigate the time-varying dimensions of the kinship between aureate and other variables. Kumar (2014) employs a VAR-ADCC-BVGARCH model to examine the prototypal and second orders moment transmission between gold and a variety of commercial enterprise sectors in India (auto, finance, energy, religious service, pharmaceuticals, and commodities). Sir Thomas More explicitly, the read addresses replication and volatility spillover between gold and the Indian postindustrial sphere. Time period data are analyzed for the period 1999 finished to 2012. This period takes three senior crises into account: the 2000 Dotcom Bubble, the 2007 Global Financial Crisis, and the 2010 European Debt Crisis. The results establish evidence of a significant unidirectional retrovert spillover from metal to stocks, however, no evidence of volatility spillover is found to exist between the two markets. Specifically, the dynamic conditional correlations for each gold-stock yoke vary considerably between positive and negative estimates throughout the overall period. The harmful estimates are principally observed in the course of the aforementioned crises, underlining the variegation opportunities that might arise during such periods. The study besides examines optimum weights, hedge ratios, and hedging effectiveness for gold-stock portfolios and concludes that metal can provide better (forgetful-run) diversification advantages when compared with stock portfolios. Arouri, Lahiani, and Nguyen (2015) investigate return and excitableness spillovers between world gold prices and Chinese trite prices. The sample information consists of daily hackneyed returns and 3-month gold futures beginning 22 March 2004 and ending 31 Butt 2011. Accordingly, the authors rely on a ample countersink of multivariate GARCH corollaries, including CCC-, DCC-, BEKK-, diagonal BEKK-, and VAR-GARCH. The independent crisis considered in the study is that which occurred in 2007. The findings theme the existence of probative cross-market return and volatility effects between gold and stocks. Moreover, the experimental evidence shows that prior gold returns are critical in explaining the dynamics of conditional return and volatility of Chinese stocks, and should therefore be taken into account when prognostication the volatility of proximo stock returns. Specifically, the conditional volatility of gold returns is significantly unnatural by unexpected changes in stock returns. In else words, a shock to the Formosan stock grocery is indicative mood of an increase in the volatility of gold returns, irrespective of its sign. This issue corroborates that which has been presented by Gencer and Musoglu (2014). Nevertheless, there is no evidence to paint a picture that prior stock return volatility significantly affects chromatic return volatility, and evenly so, the impact of prior chromatic excitability upon the conditional volatility of Chinese stocks is statistically trifling. Furthermore, the constant conditional correlational statistics estimates show a relatively weak (positive) connection between gold and stock returns, indicating that gains could exist made by including both assets in a single portfolio. The optimal weights and hedge ratios confirm that adding gold to a stock-controlled portfolio enhances its total functioning and reduces its gold risk exposure at a relatively short hedging cost, in the main during turbulent periods. In particular, the bloodline investment risk is hedged most effectively via a short position in gold futures. Gokmenoglu and Fazlollahi (2015) watch a Sodbuster causality glide slope to look into the time-varying interactions among gilt and oil prices, in addition to chromatic and oil terms volatility indices, happening the Sdanamp;P 500 Indicator. Daily time series data are analyzed over the catamenia January 2022 through November 2022. The results indicate the presence of a epochal mutual yearn-hunt equilibrium crosswise the variables in interview, however, gold prices appear to exert the highest impact upon stock prices, both in the short-stalked-run as well arsenic in the long-bleed. Whilst administering a variant method feeler, the presence of this unidirectional spillover (from amber to stocks) substantiates the outcome posited by Kumar (2014). The authors debate that investors tend to be sensitive to changes in gold prices videlicet because gold continues to be a widely available alternative to stocks, and also because gold qualifies as a functional hedging asset against inflation. The short-run terms volatilities for both gold and oil appear to have no significant impact on the Sdanamp;P 500, which implies that capital flight is unlikely to take place in the short, therefore, investors are likely to pay up more attending to the price volatilities of gold and oil in the long-run. Moreover, the study underlines the short-term impacts of embrocate price changes happening the stock exchange out-of-pocket to the fact that push sector companies chronicle for 10% of the Sdanamp;P 500, the broth prices of which are shown to increase in reply to a lift in oil prices.

The way of return and excitability spillovers (i.e. transmission mechanisms) across metallic and early markets continues to be the capable of argue among academics and practitioners. Raza, Shahzad, Tiwari, and Shahbaz (2016) undertake a nonlinear ARDL approach to explore the stubby and long-term irregular impact of gold and oil prices, as well as their respective volatility indices, on rising stock markets (China, India, Brazil, Russia, Republic of South Africa, Mexico, Malaysia, Thailand, Chile, and Indonesia). Monthly data are nonheritable for the full stop Jan 2022 through to June 2022. The authors propose that the volatility indices for gold and oil are tradable securities that differ from their price indices, therefore, contrastive fruitful strategies may be exploited aside investors. The samara results indicate that gold prices deliver a importantly positive impact on fund prices, whereas gilded price volatility carries a significantly negative impact upon the emerging stock markets included in the study; corroborating the findings reported by Tully and Lucey (2007), Mensi et aliae. (2013), Baur and Lucey (2010), and many others. With value to oil prices, the findings suggest a significantly positive effect mainly on large BRICS line of descent markets, whereas oil price volatility has a short-run meaning burden on the stock prices of Brazil, India, and Kingdom of Thailand, with varying degrees. The results also confirm that the volatility indices for gold and embrocate exhibit a negative impact upon all of the future stock markets in question, both in the short-run likewise As in the long-ravel, suggesting that higher volatility in gold and oil prices may be understood as bad news for investors; thereby superior to a fall in their respective stock prices. In their concluding remarks, the authors infer that emerging market economies are more sensitive to indefinite economic conditions (i.e. bad news) in comparison with developed markets. In a related study, Arfaoui danamp; Ben Rejeb, 2017) evaluate the interdependencies among metallic prices, oil prices, the MSCI world stock food market index finger, and the broad trade-adjusted median of the unnaturalized exchange values of the US dollar against the currencies of a broad aggroup of John Roy Major US trading partners. To live up to this aim, the authors employment the simultaneous equations glide path put out forth by Imbs (2004). Unit of time data are obtained for the sample period covering Jan 1995 to October 2022. The principal findings break the comportment of pregnant interactions among the prices of all variables included in the bailiwick. More specifically, the results illustrate that oil price is importantly influenced by stock markets, gold, and trade-weighted US dollars. The authors further argue that changes in atomic number 79 prices are for the most part shaped by changes in oil prices, stock prices, and the value of the US dollar, and to a lesser extent, are dependent upon America oil gross imports and default on exchange premiu. The barter-leaden The States dollar rate of exchange is mainly determined by the prices of Au, oil, and stocks, and, is negatively impressed past the US CPI. The bailiwick underscores the unavoidable macrocosm of indirect personal effects, namely due to the presence of worldwide market interdependencies in improver to what the authors allude to in their concluding remarks as the "financialization" of good markets; so, maximizing the reliance on commodities, much as amber and oil, as hedging tools by investors.

At that place is a large consensus in the literature on the capitalisation of golden as a risk-mitigating cat's-paw. This claim is broadly substantiated by the negative, or weak, association between gold and other market variables (including stocks), particularly during adverse market conditions. However, a freshly literature branch has emerged in Holocene age, in which doubts are casted over the viability of this proposition; giving rise to many caveats associated with the relevance of gold in the process of best portfolio selection. Maghyereh et al. (2017) employ a DCC-GARCH framework to examine volatility spillover effects and intersect-hedging between gold, oil, and fairness prices in the Gulf Cooperation Council (GCC) markets. Categorically, the study questions the practicality of using gold and oil in hedging equity portfolios. Daily information are gathered for the period January 2004 through with May 2022 systematic to evaluate the dynamic correlations and hedge ratios for the variables in dispute. The authors make a distinction 'tween a put off and a condom harbour, adopting the same definitions as those proposed by Baur and Lucey (2010). The findings account the mien of significant spillover effects from oil to equities, delineating the excessive reliance of the local economies on the vegetable oil sector. In addition, the empirical results record zero testify of significant spillover effects from gold to stocks, which explicitly suggests that gilt damage volatility has zero significant impact upon equity-supported investment decisions. In addition, the discipline finds No evidence of pregnant spillover personal effects from stocks to either of the two commodities. According to the authors, this is largely explained by the comparatively small capitalization of the broth markets under investigation. At long last, the study outlines two main conclusions. First, with the exception of a few surges during periods of grocery store stress, the results demonstrate low dynamic correlations and hedge ratios, which renders some aureate and oil inexpensive, yet useless, hedge tools. Second, both commodities May be regarded Eastern Samoa weak safe havens, although at a substantial be. Similarly, Naser (2017) probes the effectiveness of investing in gold as a hedging legal document against inflation risks in the United States via a Granger causality testing framework. Each month data are analyzed for the period 1986 through to 2022. The important findings indicate that gold does not qualify as a useful hedge in the short-run to the same extent that it does so in the long-run. This is in contrast to the elementary argument launch in the literature, whereby gold is saved to be an effective hedge against securities industry-related risks in particular during turmoil periods. Balcilar, Ozdemir, Shahbaz, danampere; Gunes, 2018) examine the predictability of changes in gold prices based upon inflation for G7 markets. The authors depend on a nonparametric causality-in-quantiles methodology to quiz for causality in mean and variance. These tests detect nonlinearity and march the misspecification errors produced aside elongate Granger causality testing. This hybrid draw close combines the techniques proposed by Nishiyama, Hitomi, Kawasaki, and Jeong (2011) and Jeong, Härdle, and Song dynast (2012). Monthly data are analyzed for the period 1979 through 2022. The results propose that gold does not serve as a parry against inflation during periods when monetary value fluctuations in the gold market are either very high (i.e. churning periods) or very low (i.e. unruffled periods).

Information technology is noteworthy to mention that a number of earlier research document ingest brought into question the relevance of using gold as a risk-minimizing instrument against strange assets. These studies, however, are quite specific in number. This is mostly repayable to the differences in definitions and econometric methodologies adoptive. For instance, even though Baur and Lucey (2010) prove that aureate may serve as a fertile circumvent and haven plus against USA and European stocks during the 2007 Spherical Financial Crisis, there are few exceptions in their overall findings. Particularly, the sketch finds gold to be neither a hedge nor a safe haven against stocks in Australia, Canada, Japan, and the large BRIC markets. By the same token, Ciner, Gurdgiev, and Lucey (2013) explore the dynamic relationship betwixt aureate, oil, currency, stocks, and bonds in the US arsenic well as in the UK. The authors repose on the attack proposed by Baur and Lucey (2010). Specifically, the study applies a DCC-GARCH framework to analyze every day returns for the variables in question finished the period 1990 through to 2010. First, the study examines the clock time variation in provisory correlations to build whether these variables can be used As a hedge against one another. Second, the study investigates whether the dependencies 'tween the variables differ during extreme price shifts by utilizing quantile regressions. The empirical findings reason that gold can function as a oasis asset against a significant come by some exchange rates, however, the discipline finds that gold cannot be treated American Samoa a safe haven for UK stocks during turmoil periods. What is more, Atomic number 71 et aliae. (2014) employ a VAR-DCC-GARCH example to investigate the volatility spillover effects 'tween gold and stocks in the UK. In particular, the subject examines daily gold spot prices in the London Golden Market As well every bit daily stock prices in the FTSE 100 Index, covering the period 4 January 2000 to 31 December 2012. The empirical findings report evidence of a lasting bidirectional volatility spillover event between gold and stocks, albeit much significant from gold to stocks in the long-lasting-run. The results also demonstrate that the time-varying conditional correlation coefficient between the two assets becomes more significant when the price of gold increases. Although the conditional correlations observed during the 2007 Global Financial Crisis are mostly negative, the estimates are found to diverge well between cocksure and negative end-to-end the uncastrated sample period; suggesting that investors should pursue divergent strategies below different system circumstances. ⁵

In determining whether gold is an in force hedging instrument against other asset price fluctuations, earlier approaches have documented the sensitivity of gold prices to macroeconomic news show releases by means of employing a extensive drift of statistic tests, conciliatory Fourier functions, and ARDL examination frameworks. Moreover, the studies which get utilised a Granger causality advance are much more intimately connate capturing time-consuming-term causality trends betwixt two variables in a time series. These approaches, however, do non take into describe several critical aspects of hedging effectiveness such as asymmetry, dynamic conditional coefficient of correlation, and best portfolio weights. These factors constitute a pivotal portion in an investor's hedging strategy. To that extent, GARCH-founded models are far Sir Thomas More robust, particularly when accounting for frustrate-market return and unpredictability spillovers. In increase, selfsame few studies to date examine the dynamic relationship 'tween amber and stocks in the US market, particularly in the post-circular financial crisis period. This paper aims to fill these literature gaps by constructing a VAR-ADCC-BVGARCH model to investigate the dynamic human relationship betwixt gold and stocks in the post-global financial crisis era.

3. Methodology

The methodological analysis of this paper is disjointed into parts. The first part deals with capturing crossing-market return and volatility spillover effects. The second part relates to the issue of hedging effectiveness.

The returns are calculated as the log of the ratio of a given point's price to that of the previous period where: (1) $r_{t} = l n (\frac{p_{t}}{p_{t - 1}})$ (1)

Where $r_{t}$ is the return of an index at time ( $t$ ), $p_{t}$ is the price indicant at time ( $t$ ), and $p_{t - 1}$ is the price index of the previous period. Next, a VAR(1) process is applied to capture the spillover in mean returns. More specifically, the VAR(1) model accounts for changes in marketplace returns in addition to a commercialize's response to news show releases. So, the return for market $i$ at time $t$ is denoted past $r_{i, t}$ every bit follows: (2) $r_{i, t} = μ_{i 0} + \sum_{j = 1}^{2} μ_{i j} r_{j, t - 1} + ε_{i, t}, i, j = 1, 2$ (2)

So much that $E [ε_{i, t} | . ξ_{i, t - 1}] = 0$ , wherein $ξ_{i, t - 1}$ represents the entropy available at sentence $t - 1$ . The conditional mean return for each market is defined by its own antecedent returns to boot to cross-market antecedent returns. The moderate/lag human relationship between market returns is estimated via the coefficient $μ_{i j}$ where $i \neq j$ . A significant guess indicates that the current return in market $j$ can be used in forecasting the next hark back in market $i$ . The VAR model accounts for cross-market correlations in addition to autocorrelations in returns. In terms of modeling contrary to fact excitability and capturing unpredictability spillover effects, an ADCC-BVGARCH model is exploited. As proposed away Ling and McAleer (2003), the conditional variance is specified as VAR-GARCH(1,1), where: $ε_{i, t} = z_{i, t} \sqrt{h_{i, t}}$ (3) $h_{i, t} = ω_{i 0} + \sum_{j = 1}^{2} α_{i j} ε_{j, t - 1}^{2} + δ_{i} ε_{i, t - 1}^{2} D_{i, t - 1}^{-} + β_{i} h_{i, t - 1}, j = 1, 2$ (3)

The standardized residuals are denoted by $z_{i, t}$ and the conditional variance is denoted by $h_{i, t}$ . A dummy variable is denoted by $D_{i, t - 1}^{-}$ which is equal to 1 when $ε_{i, t - 1} danlt; 0$ and zero differently. The terminal figure $δ_{i} D_{i, t - 1}$ differently impacts the provisory variance, capturing good news when $ε_{i, t - 1} dangt; 0$ and icky news when $ε_{i, t - 1} danlt; 0$ . This procedure is valuable in evaluating the effects of large shocks among two markets.

The DCC model is a two-step procedure developed by Engle (2002). It is designed to capture the time-varying provisional correlation between two variables. It calculates the parameters of the GARCH model in the first tread, and it estimates the dynamic correlation in the second step. Thus, the DCC-GARCH model is dictated atomic number 3 follows: (4) $H_{t} = D_{t} P_{t} D_{t}$ (4)

The $2 \times 2$ conditional covariance matrix is defined by $H_{t}$ , the conditional correlation matrix is defined by $P_{t}$ , and a diagonal matrix with time-changing standard deviations is defined past $D_{t}$ , where: (5) $D_{t} = d i a g (\sqrt{h_{11},} \sqrt{h_{22}})$ (5)

And (6) $P_{t} = d i a g ({(Q_{t})}^{- 1 / 2}) Q_{t} d i a g ({(Q_{t})}^{- 1 / 2})$ (6)

As Kumar (2014) explains, $Q_{t}$ is a $(2 \times 2)$ symmetric positive definite matrix, where $Q_{t} = (q_{t}^{i j})$ , and is defined as follows: (7) $Q_{t} = (1 - θ_{1} - θ_{2}) \overset{ˉ}{Q} + θ_{1} Z_{t - 1} Z_{t - 1}^{'} + θ_{2} Q_{t - 1}$ (7)

Whereby $\overset{ˉ}{Q}$ denotes a $(2 \times 2)$ matrix of the unconditional correlation of the standardized residuals. The sum of the non-negative scalars, $θ_{1}$ and $θ_{2}$ , is assumed to be less than 1. The correlation coefficient estimates are provided as follows: (8) $ρ_{i, j, t} = \frac{q_{i, j, t}}{\sqrt{q_{i, j, t}}}$ (8)

It is Worth mentioning that a large body of literature on GARCH-founded specifications has evolved over the preceding few decades. Specifically, the models differ in footing of conditional volatility specifications atomic number 3 well as conditional variance-covariance intercellular substance specifications. For instance, the restricted BEKK approach formed by Engle and Kroner (1995) guarantees the positive definiteness of the covariance matrix in addition to the Three hundred. The DCC model developed past Engle (2002) eased the constancy criterion of the CCC model. Cappiello, Engle, and Sheppard (2006) outspread the DCC manikin to the ADCC model in order to account for (noninterchangeable) purchase effects in the specified correlation structure. As Katzke (2013) explains, the ADCC model nests both the DCC and the CCC, thus, the goodness of outfit 'tween the series can be compared using the log-likelihood estimates. Moreover, the results demonstrate that the ADCC model for the most part outperforms the some other two models based upon higher log-likeliness statistics too as lower Akaike Information Criterion (AIC) and Bayesian Data Measure (SBIC) estimates.

To test for hedging effectualness, it is necessary to establish a hedge work. This field adopts the optimal sideste ratio proposed by Kroner and Sultan (1993). The best hedge ratio is a risk minimizing hedge ratio between two assets ( $i$ and $j$ ) which is calculated using the estimates of the conditional disagreement and covariance based upon the minimization of the variance of the portfolio return, given as: (9) $δ_{i, j, t} = \frac{h_{i, j, t}}{h_{j, j, t}}$ (9)

The conditional variance of asset $j$ at clock time $t$ is defined by $h_{j, j, t}$ and the conditional covariance between plus $i$ and asset $j$ at time $t$ is denoted aside $h_{i, j, t}$ . Kumar (2014) explains that a long position in one dollar for asset $i$ can be qualified by a short position in $δ_{i, j, t}$ dollars of asset $j$ .

Succeeding, the optimal portfolio weights are estimated using the method proposed by Kroner and Ng (1998), which are calculated connected the basis of minimizing the risk of the portfolio without affecting the expected return, such that: (10) $w_{i, j, t} = \frac{h_{j, j, t} - h_{i, j, t}}{h_{i, i, t} - 2 h_{i, j, t} + h_{j, j, t}}$ (10)

And (11) $w_{i, j, t} = \{\begin{matrix} 0, i f w_{i, j, t} danlt; 0 \\ w_{i, j, t}, i f 0 \leq w_{i, j, t} \leq 1 \\ 1, i f w_{i, j, t} dangt; 1 \end{matrix}$ (11)

Investors could reduce their risk exposure against turbulent movements in asset $j$ by holding asset $i$ , whereby $w_{i, j, t}$ is calculated as the proportion assigned to the beginning plus, in one-dollar portfolio consisting of two assets ( $i$ and $j$ ), at time $t$ . Conversely, the proportion appointed to the second plus is premeditated As $1 - w_{i, j, t}$ .

Finally, the hedging effectiveness across the specified portfolio combinations is determined victimisation the method put up forward by Ku, Chen, and Chen (2007) and suggested by Kumar (2014), which can be conventional by evaluating the completed hedging errors American Samoa follows: (12) $H e d g i n g E f f e c t i v e n e s s = \frac{V a r i a n c e_{u n h e d g e d} - V a r i a n c e_{h e d g e d}}{V a r i a n c e_{u n h e d g e d}}$ (12)

The variance of returns for a portfolio comprised of chromatic and stocks (i.e. a weasel-worded portfolio) is defined by $V a r i a n c e_{h e d g e d}$ , whereas the variance of returns for a portfolio entirely comprised of stocks (i.e. an unhedged portfolio) is defined by $V a r i a n c e_{u n h e d g e d}$ . A comparatively higher hedging effectiveness estimate for a given portfolio is indicative of a propitious hedge strategy, based connected the significant amount of portfolio risk reduced. It should be noted that there are several methods through which hedge effectiveness can be produced, which mainly dissent in terms of how the circumvent ratio is estaimated. These methods can be divided into cardinal main groups, a static dodge ratio and a impulsive hedge ratio. Among the most illustrious methods are the ordinary method of least squares (OLS) method and the error correction exemplary (Electronic countermeasures). For the purposes of this paper, the results based upon the optimum (time-varying) elude ratio proposed by Kroner and Sultan (1993) are compared to those produced by the unchangeable OLS hedge ratio. A static hedge ratio assumes a continuant relationship between two underlying variables over a given period of clock, whereas a time-varying hedge ratio assumes that the variables of interest are described by a correlational statistics which changes over time. Since the intent of hedge is to efficaciously minimize the risk of a minded portfolio, it is therefore the highest degree of risk reduction (i.e. hedge effectiveness) which determines the transcendency of ace method over the other.

4. Data and descriptive statistics

4.1. Information

In terms of gold, the dataset consists of spot prices per ounce (in USD). The stock price indices considered in this survey include the Nasdaq Composite Index (National Association of Securities Dealers Automated Quotations) in plus to the end prices of the Dow Jones Industrial Average Index (DJIA) and the Sdanamp;P 500 Index (Sdanamp;P500). For entirely variables considered, each day time series data are obtained from the Bloomberg database, spanning an 11-yr period from 1 January 2007 to 31 December 2022. Thus, a total of 2,870 daily observations per variable are sampled. It is worth mentioning that trinity US market indices are included in this report in order to accounting for differences in market size and capitalization vis-à-vis hedge effectiveness against metallic. Figures 1 and 2 below illustrate the daily price indices and the each day log returns, respectively, for the markets in question. The price indices for the stock markets shew a sharp decrease in value from year-last 2007 through to mid-year 2009. However, the price indicant for gold exhibits a different trend. A sharp increase in rate can observed between year-end 2007 and earliest 2008, followed by a steady decrease until early on 2009, and an overall increase from thereon after, reaching its highest value by year-end 2011. In terms of log returns, the heaviest excitableness clump for wholly markets can be seen from class-end 2007 to mid-2009.

4.2. Descriptive statistics

Table 1 presents the synchronal statistics for the regular price index changes inside the four markets under probe, that is, the logarithm of price indices is used to estimate stock returns.

Prorogue 1. Synchronous statistics—returns

The highest average miserly and mesial daily return can be determined for the Nasdaq Index over the 11-year period. Moreover, the highest degree of uncertainty (i.e. excitability or standard deviation) is besides determined for the Nasdaq Index, followed by the Sdanamp;P 500 Index, gold, and the Dow Jones Industrial Average Index, respectively. All daily returns are leptokurtic and negatively inclined. As expected, the Jarque-Bera test results reassert that the null hypothesis of normality is disapproved for all markets. The Ljung-Loge $Q$ -test results show that the null hypothesis of no autocorrelation in the get-go 36 lags is rejected for all markets at the 5% significance level. Settled on 10 lags, the Lagrange multiplier (ARCH-LM) run results betoken the presence of contingent heteroskedasticity altogether return serial. The unconditional correlations 'tween gold and stock returns are relatively low; ranging 'tween a minimum of 1% and a uttermost of 3%. The highest correlation with gold is discovered for the Sdanamp;P 500 Index, followed aside the NASDAQ Index and the Dow Jones Developed Average Index, severally.

5. Results and discussion

5.1. Unit root test

The Nanogram and Perron (2001) unit ascendant test is applied in regularise to pass judgment the order of integration in metallic and stock returns. Kumar (2014) and many others argue that the conventional unit root tests, such as ADF and Philips and Perron (PP), are likely to yield biased results as they abide from finite sample power and size problems. The results in Table 2 indicate the presence of a unit root at level data for all the markets in question, however, all give serial are stationary at the first remainder. This substance that all return serial publication are integrated at I (1)

Table 2. Ng-Perron unit root test

5.2. The main role model

Table 3 demonstrates the upper limit likelihood estimates as well as the spot-diagnostic assay results on the standardized residuals produced by the principal VAR(1)-ADCC-BVGARCH(1,1) fashion mode for the metal-stock pairs under scrutiny (equations 2–7). Supported the mean equation, the results show that none of the current blood line returns are significantly affected by the lagged gold returns at the 5% level (indicated by $μ_{12}$ ). This finding implies that on that point is no grounds of significant return spillover from the gold market to the stock markets under probe. In other speech, prior gold returns cannot be used in forecasting current stock returns. However, the results exhibit that underway gold returns are importantly affected by lagged stock prices (indicated by $μ_{21}$ ). This outcome suggests the presence of a (key) blackbal return spillover from stocks to gold, inferring that prior stock returns can help in predicting current gold returns.

Table 3. Maximum likeliness estimates of the VAR-ADCC-BVGARCH model

The dependant on variability equation relates to the issue of unpredictability persistence. The short-run dependence is captured aside the ARCH coefficient (denoted as $α_{i i}$ ). A important estimate suggests that preceding information shocks in unmatchable securities industry significantly regard the stage conditional volatility in another market. The results show that the existing conditional volatility for all stock returns can be explained past past shocks in gold returns at a established unwavering of meaning (indicated by $α_{12}$ ). Connected the opposite hand, thither is no evidence to suggest that prior shocks available returns bear a significant impact upon the current conditional volatility in gold returns at the 5% story (indicated by $α_{21}$ ). The long-lam volatility persistence is captured away the GARCH coefficient (denoted as $β_{i}$ ). This coefficient too deals with a commercialize's sensitiveness to its personal prior shocks. A of import estimate indicates that the instant volatility in a given market is sensitive to its own past volatility. It can be seen that the GARCH coefficient is highly significant for both gold and stocks, suggesting that the current volatility in the returns of each asset separate is importantly explained by its have historical return excitableness. Moreover, it can be determined that the GARCH coefficient estimates are considerably greater than the Patronizing coefficient estimates for all gold-stock pairs, confirming that the long-term unpredictability persistence in from each one securities industry is higher than that of its short-run persistence. In addition, this determination suggests that the estimated conditional volatility in gold and stock returns is probable to waver more sharply due to a probative impact of own tense volatility. In different words, the result of prior volatility is Sir Thomas More helpful in detecting rapid changes in the estimated conditional excitability series than that which nates be explained by the return shocks. To it end, information technology is useful for market participants and other practitioners to consider analyzing long-term (historical) volatility persistence to explain future information shocks in their investment funds strategies.

Furthermore, the empirical findings paint a picture none evidence of volatility spillover among the hand-picked gold-stock combinations. The presence of asymmetric volatility in a given serial publication is indicated aside $δ_{i}$ in the conditional variance par. The significant estimates of $δ_{1}$ indicate the existence of unsymmetric volatility in all stock returns. In counterpoint, the insignificant values of $δ_{2}$ rule in the presence of irregular excitableness in chromatic returns. The DCC model's estimated coefficients are indicated by $θ_{1}$ and $θ_{2}$ . Both coefficients are positive and statistically significant at the 1% level for all gold-stock pairs. In addition, the satisfaction of the $(θ_{1} + θ_{2}) danlt; 1$ touchstone accentuates the mean reverting nature of the dynamic conditional correlations 'tween gold and stock returns. The estimated values for the degrees of exemption are denoted by $v$ . The significant values suggest that the main mold is fit to detect the leptokurtic nature of the estimated residuals, based upon the Pupil's $t$ -distribution.

The Lagrange multiplier test statistic, based on 10 lags, is indicated by Condescending(10). The insignificant estimates infer that the main simulation is able to account for the heteroskedasticity in the takings serial publication. The significant sign predetermine test results are mainly determined for stock returns. That is to articulate, the essence of both positive and negative shocks observed in the stock exchange is significantly different from that which has been predicted by the main model. IT is worth noting that GARCH models cut the sign of the excess return and only account for the magnitude of an basic innovation. In accession, the results confirm the presence of disinclined size bias for all food market returns and confirming size bias for the Dow Jones Industrial Average and the Sdanamp;P 500 Index. However, the results of the joint examination reject the presence of both sign and size bias, positive that the main exemplary is able to account for asymmetry in the volatility process.

As indicated by Q(36), the estimates of the standardised residuals are monumental for all sprout returns, suggesting the mien of autocorrelation at the 1% level of implication. All the same, this effect is invalid when the standardized residuals are squared, as shown by Qs(36). Thus, information technology can be concluded that the abovementioned mock up is adequately specified. It is Worth mentioning that some N tests, including the modified Ljung-Boxwood mental test (1978), have been conducted. As expected, the results reassert not-normality in the standardized residuals for the series in hand. In other words, the null hypothesis of normality is importantly jilted at the 1% tier. Normality tests are likely to demonstrate non-normality in the standardized residuals as sample size increases. Burns (2002) explains that atomic number 3 the tails extend on the far side a $t$ with 10 degrees of exemption, the nothing distribution of the Ljung-Box test loses power. This is a major drawback of the Ljung-Package and other normality tests. Yet, this agency that it is very problematic to palliate this limitation.

5.3. Driving conditional correlation (DCC)

Figure 3 illustrates the time-varying dependent on correlational statistics estimates produced aside Equation (8) for all the chromatic-stock combinations relevant. Evening though highly negative conditional correlation estimates can Be ascertained during the 2007 Global Fiscal Crisis, a wide-cut-ranging (overconfident and dissenting) pas seul is institute to subsist throughout the overall period. This finding underlines the importance of examining the prospective benefits of combining both amber and stocks in the portfolio optimization process.

Figure 3. Dynamic conditional correlations.

5.4. The optimal hedge ratio

Figure 4 illustrates the optimal skirt ratio using Equality (9), arsenic suggested by Kroner and Sultan (1993), for whol the stock-gilt and gold-stock combinations in question.

Figure 4. Optimal hedge ratios.

The optimal hedge ratios are founded along the conditional variance/covariance estimates produced by the main model. Kumar (2014) explains that the risk of infection in pickings a unsound position in asset $i$ can represent offset aside taking a short position in asset $j$ . A widespread fluctuation of positive and negative estimates can be seen in the optimal hedge ratios for all the pairwise combinations throughout the entire sample historical period. This finding suggests that it is modality for investors to incessantly adjust their portfolios ready to in effect denigrate their risk vulnerability over time. The (higher) negative hedge ratios indicate that both assets, $i$ and $j$ are negatively correlated operating theatre, in other words, draw in opposite directions. During such (crisis) periods, investors can take a long location in plus $j$ to hedge against a short position in asset $i$ . Table 4 reports the descriptive statistics of the optimal hedge ratio for all possible asset combinations terminated the nominal sample period.

Table 4. Synchronic statistics—optimal hedge ratio

The average ratio is generally low for all the pairs in doubtfulness. The highest average stock-atomic number 79 hedge ratio (i.e. the nearly pricey hedge) is that which is indicated by Sdanamp;P500−dannbsp;Gold, followed by NASDAQ−Gold and DJIA−Gilt, severally. This means that a $1 long put across in the Sdanamp;P 500 Index can represent hedged against a 3 cent short positioning in gold. Moreover, a $1 long put down in the Nasdaq Index can be hedged with a 2 cent short set back in chromatic, and a $1 long put over in the Dow-Jones Industrial Average Industrial Ordinary Forefinger crapper be shorted against a one-half centime in gold. Considering the metallic-stock combinations, the cheapest hedging option is to learn a $1 long-wool position in gold against a 2 cent short position in the Dow daniel Jones Industrial Norm Index. It is slenderly more expensive to hedge gold against the Nasdaq Index, where a $1 long put on in golden is shorted with approximately 3 cents in stocks. By the same token, the most expensive put off is to take a $1 long position in Au with a 5 cent short view in the Sdanamp;P 500 Index.

5.5. Best portfolio weight unit allocation

Figure out 5 presents the sentence-changing best portfolio weights for both chromatic and stocks happening the basis of the conditional variance/covariance estimates produced by the important model, as proposed by Kroner and Ng (1998), using Equations (10) and (11).

Shape 5. Optimal portfolio weights.

The occurrence of sharp fluctuations for each optimal portfolio can constitute observed over the sample distribution historical period. This particular determination does not necessarily imply that investors are required to adjust their portfolio weights at each point. Doing and so may lead to an increase in dealing costs, as Kumar (2014) explains. Instead, investors whitethorn allocate a mean (optimal) system of weights for each asset in a portfolio over a certain period of clip, thereby adjusting the portfolio by purchasing the under-allocated component and merchandising the over-allocated portion consequently.

Hold over 5 shows the synchronal statistics relating to the best weights for each portfolio over the nominal sample point. The mean value (optimal) weights for all the asset pairs under investigation are quite up compared to those produced by Kumar (2014). The highest stool be observed for the DJIA−Gold portfolio, followed by the Sdanamp;P500−dannbsp;Gold and NASDAQ−Metal portfolios, severally. This means that for a $1 portfolio, close to 61 cents (on the average) are to be invested in the Dow Jones Industrial Average Forefinger and the left 39 cents are to glucinium invested with in the gold commercialise. Moreover, 58 cents are to comprise allocated to the Sdanamp;P 500 Index and the remaining 42 cents are to be allocated to gold in a $1 portfolio. Finally, for every 51 cents invested in the National Association of Securities Dealers Automated Quotations Index finger, 49 cents are to be apportioned to gold in a $1 portfolio.

Table 5. Synchronal statistics—best portfolio weights

5.6. Hedge effectiveness

Table 6 presents the hedging effectiveness of including gold in a stock-dominated portfolio based upon the optimal hedge ratio (Equation 12) in plus to the static OLS hedge ratio. The division of an unhedged portfolio indicates the level of risk exposure for a stock-dominated portfolio, whereas the variance of a hedged portfolio indicates the degree of risk for a gold-stock portfolio. Based along the optimum fudge ratio, the highest degree of take a chanc reduction subsequent to the inclusion of gold in an optimal portfolio can be determined for the Dow-Jones Industrial Average Industrial Average (66%), followed by the Sdanamp;P 500 Index (53%) and the Nasdaq Forefinger (13%). These results are significantly lower than those which are produced exploitation the still OLS hedge ratio. Specifically, the highest estimate of hedging effectiveness can be observed for the Dow-Jones Industrial Average Industrial Average (70%), followed by the Sdanadenylic acid;P 500 Index (68%) and the Nasdaq Index (32%). Along a star facie basis, these findings English hawthorn suggest that utilizing the simpler methodology (i.e. the static put off ratio) yields significantly higher hedge effectivity estimates when compared with the more complex methodology (i.e. the optimal hedge ratio). This determination is not amazing as quasi results are according by Lien (2005), Gupta and Singh (2009), Wibowo (2017), and others. Miffre (2004) explains that the static OLS hedge ratio imposes the condition of a constant joint distribution for changes in some variables, which could result in substandard hedge decisions peculiarly during periods of high market excitability. In addition, static hedge strategies fail to consider the time-varying adjustments which are necessary in the overall hedge process. Thus, the approach proposed away Kroner and Sultan (1993) is considered more reliable in that the metre-varied hedge ratios are estimated on the cornerston of contingent moments and thereby mitigating the drawbacks associated with the traditional methods in the hedging literature.

Table 6. Hedging effectiveness (HE)—best hedge ratio

That beingness said, the experiential results suggest that incorporating golden in a portfolio reduces its gross risk exposure in the US market, all the same, not to the extent that can equal determined in South Asia and the Middle East, where gold occupies a distinct sociocultural status. Moreover, information technology should be noted that the Dow Jones Industrial Average is comprised of 30 companies, whereas the Sdanamp;P 500 Index consists of 505 stocks and the Nasdaq Index hosts over 3,300 listings. In another actor's line, the hedging effectives of gold diminishes as stock market capitalisation increases. Some other probable explanation relates to the issue of market efficiency. Compared with the results reported by Kumar (2014) happening the Indian commercialize, the lower hedging effectiveness estimates in this study perhaps paint a picture that it is less likely for gold to be considered an effective hedging creature against a efficacious form buy in market, such as that in the US. Moreover, the increased number of alternatives to gold in the commodities grocery may volunteer greater effectiveness in a hedge scheme against stock price volatility. Nevertheless, these issues remain subject to succeeding research and methodological specification. Considering the comparatively high proportion of funds required to be invested in gold, a marginal amount of lay on the line is hedged away for a stock index delineate aside a large number of listed companies.

6. Conclusion

The central aim of this wallpaper is to examine the hedging effectiveness of gold against pedigree Leontyne Price fluctuations in the US market. Specifically, this study examines the energizing pairwise interaction between chromatic spot prices and US fairness prices in the post-2007 Orbicular Financial Crisis period by applying a Volt-ampere-ADCC-BVGARCH framework. Initially, the supreme likelihood estimates and post-characteristic test results are produced. The results show no evidence of significant return spillover from metal to stocks. However, the findings indicate the bearing of significantly dissident return spillover from stocks to gold, which way that past stock returns pot help in predicting current gold returns. Moreover, the empirical results manifest that the present conditional volatility of livestock returns can be explained by prior shocks in atomic number 79 returns. On the contrary, on that point is no reading that prior shocks in stock returns feature a significant impact upon the represent conditional excitability in Au returns. Additionally, this study finds that the present excitableness in the returns of from each one asset assortment is significantly affected by its own sometime return volatility. The results encourage point that the semipermanent volatility persistence in both gold and stocks is higher than that of its short-term continuity, inferring that the provisory excitableness in some plus classes is likely to fluctuate much sharply Eastern Samoa a solution of personal prior excitableness. Further, there is no evidence of significant volatility spillover among the pairwise combinations in query. The active probationary correlation estimates between metallic and stocks are particularly negative during the 2007 Global Financial Crisis, however, a all-inclusive-ranging variation of positive and negative values can be observed end-to-end the entire try geological period. The optimal hedge ratios indicate that it is relatively inexpensive to retain a unforesightful position in the gold grocery against a long position in the stock market, however, the optimal portfolio weights show that, on the average, a significant proportion of pecuniary resource need to be allocated to gold in an optimal portfolio with the objective of reducing its overall danger exposure. Finally, the hedge effectiveness estimates confirm that the benefits of including amber in a stock-dominated portfolio tend to diminish as stockpile market capitalisation increases. Put differently, very little danger is mitigated considering the high proportion of funds which need to be invested in gold against a securities market index characterized by a large number of listed companies. Therefore, it is recommended that investors look for unconventional commodities to in effect hedge against stocks in the US market. The key findings of this study are critical for insurance makers, portfolio managers, institutional investors, and other market participants in formulating best hedging strategies.