Abstract

Graphical abstract

JEL classification

Keywords

1. Introduction

2. Methodology

3. The model and results

4. Robustness

5. Conclusion

Author contributions

Declaration of Competing Interest

Acknowledgments

Appendix A

References

Abstract

In this study, we examine the hedging relationship between gold and US sectoral stocks during the COVID-19 pandemic. We employ a multivariate volatility framework, which accounts for salient features of the series in the computation of optimal weights and optimal hedging ratios. We find evidence of hedging effectiveness between gold and sectoral stocks, albeit with lower performance, during the pandemic. Overall, including gold in a stock portfolio could provide a valuable asset class that can improve the risk-adjusted performance of stocks during the COVID-19 pandemic. In addition, we find that the estimated portfolio weights and hedge ratios are sensitive to structural breaks, and ignoring the breaks can lead to overestimation of the hedging effectiveness of gold for US sectoral stocks. Since the analysis involves sectoral stock data, we believe that any investor in the US stock market that seeks to maximize risk-adjusted returns is likely to find the results useful when making investment decisions during the pandemic.

1. Introduction

The literature on the response of financial markets, including the stock market, to the COVID-19 pandemic is fast growing, owing to its grave consequences. Several studies have examined the impact of the pandemic on financial markets (see for example, Ali et al., 2020; Al-Awadhi et al., 2020; Baig et al., 2020; Haroon and Rizvi, 2020a,b; He et al., 2020; Li et al., 2020; Narayan et al., 2020a,b; Mishra et al., 2020; Phan and Narayan, 2020; Prabheesh et al., 2020; Salisu and Akanni, 2020; Salisu et al., 2020a,b; Salisu and Sikiru, 2020; Salisu and Vo, 2020; Sharma, 2020; Zhang et al., 2020; Sikiru and Salisu, 2021; among others)1 and the evidence suggests a negative impact; consequently, the need to hedge against the risk associated with the pandemic becomes crucial.2 This is the motivation for our study. In response to this challenge, we consider gold to be a potential asset that can serve as a good hedge for stocks against the pandemic risk. The pioneering studies on the gold-stocks nexus (see for instance, McDonald and Solnik, 1977; Sherman, 1982; Jaffe, 1989; Chua et al., 1990) indeed support the inclusion of gold in the equities portfolio for the benefits of portfolio diversification and downside risk management.3,4 This continues to be emphasised in the recent literature with pieces of evidence of negative or low correlation of gold in the investment portfolio containing stocks (see for example, Joy, 2011; Reboredo, 2013; Beckmann et al., 2015; Bredin et al., 2015). This strand of the literature also finds support in the conceptual arguments, which suggest stock market hedging qualities of gold when its return is shown to be negative or uncorrelated with the stocks prices (or returns) (see Baur and Lucey, 2010; Baur and McDermott, 2010; Zagaglia and Marzo, 2013).5 In the class of traditional hedging assets, Shahzad et al. (2020), while not particularly considering the COVID-19 pandemic, have shown that gold offers more effective hedging benefits for G7 stock indices.

1- Introduction

2- Theoretical framework and methodology

3- Data and preliminary analysis

4- Presentation and discussion of results

5- Conclusion

References

Abstract

This study is a comparative analysis of inflation hedging properties of stocks, gold and real estates for the US. It is hypothesised that the assets have varying market characteristics and thus should respond differently to high inflation. The Fisher’s hypothesis for asset-inflation hedging is constructed both within the bivariate and multivariate modelling frameworks. Thereafter, some salient features typical of predictive models such as asymmetry, time-variation and structural breaks are incorporated in the estimation process for completeness. The results show that inflation hedging tendencies of assets are heterogeneous across the considered assets. The real estates and stocks prove to be good hedges against inflation, while gold investment defies Fisher’s hypothesis. Also, the results are sensitive to the decomposition of data for pre- and post-GFC periods, indicating that asset-inflation hedging relationship for the US is time-varying. The results are robust to alternative data frequencies.

Introduction

The aim of a rational investor is to maximise returns and reduce risk. However, inflation has been found to impede these objectives (Wang et al., 2011; Arnold and Auer, 2015; Yeap and Lean, 2017). The influence of inflation on financial matters has raised significant concern among academics, investors, and policymakers since 1970.2 For instance, Linter (1975) argues that fewer issues are more important than inflation’s effect on financial institutions, markets, and investment policies. Theoretical arguments show that inflation reduces purchasing power and the standard of living of economic agents in addition to causing a potential reduction in returns on investment assets (Anari and Kolari, 2002; Iacoviello, 2012; Case et al., 2012; Yeap and Lean, 2017; Christou et al., 2018). In an attempt to mitigate the detrimental effects of inflation, a strand of literature has designed ways to hedge against inflation based on the Fisher (1930) theory, which states that the expected nominal interest rate should move in sync with expected inflation. Fama and Schwert (1977) demonstrated that the Fisher thesis can be inferred for all classes of assets where nominal returns of the said asset should move in sync with inflation, indicating that the asset has a full (short-run) hedge against inflation risks (see Arnold and Auer, 2015). Hence, the dynamics of hedging against inflation require that the return on investments should be at least equal to the rate of inflation (see Fang et al., 2008; Obereiner and Kurzrock, 2012; Amonhaemanon et al., 2013; Hoang et al., 2016; Taderera and Akinsomi, 2020). This study seeks to identify and quantify the extent to which returns on selected financial assets could hedge against inflation. By extension, the study provides answers to the inquiry of whether different asset classifications react in the same manner or differently to inflation.

Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.

Introduction

The development of R&D activities follows respective trajectories regarding different domain features. These trajectories could be initiated or shifted by either findings from basic research or demands from customer markets. Along each technology developmental pathway, visible or invisible milestones, as readiness indicators, provide clues to identify further development opportunities. To delineate these opportunities, it is important to consider domain-specific progressive properties. In biomedical fields, scientific and technical innovation has facilitated the development of countless new therapies and medical devices. Yet, developmental trajectories of specific technologies are often uneven and challenging. Clinical translation of early discoveries “from bench to bedside” is often described as a slow and incremental process (with an average lag of 17 years) (Morris et al., 2011). This time lag can be as long as 30 years for specific topics, such as nano-enabled drug delivery (Wilhelm et al., 2016). Capturing research activities and other observable events in technology development can provide a better understanding of the ongoing biomedical R&D process and innovation prospects. Nanotechnologies hold much promise in biomedical research. Companies, agencies, and researchers are aware of the transformational potential of nanotechnology in biomedicine and continue to pursue its development. As a result, over the past decade, government support for nanotechnology-based research has increased dramatically all over the world. For example, the US National Institutes of Health (NIH) invests $450M per year into nanotechnology research and training, including $150M per year invested by the National Cancer Institute (NCI) alone (Dickherber et al., 2015).

All the industrially applied gold leaching methods (historical chlorine gas based leaching, dominating state-of-the-art cyanide gold leaching, processes at precious metals plants) suffer from the characteristics related to aggressive and even toxic leaching media and high chemical consumption. This study targets environmentally sound cyanide-free gold leaching in mild chloride media in terms of minimizing chemical consumption. In the current study, it was investigated whether providing instant gold recovery (carbon-in-chloride-leach, CICl) could allow high gold recovery in a mild and non-toxic leaching environment. The investigated leaching parameters were S/L ratio, T, type of oxidant i.e. [Cu2þ]/[Fe3þ] and [Cl]. The results showed that gold could be dissolved in exceptionally mild conditions, when an appropriate adsorption/reduction (activated carbon) site was provided immediately after leaching. It was found that impurity metals iron and copper originating from the gold ore (Fe 1.6% and Cu 0.05%), were advantageous self-initiating oxidants and 87% of gold could be dissolved in pure calcium chloride (2.8 M) solution. In addition, no bromide, which is a commonly added aggressive additive in modern cyanidefree processes, was required. The lowest chloride concentrations applied were comparable (0.6 M) or even milder (0.3 M) than those typical of seawater chloride concentrations, and could still result in gold recovery, 72% and 64%, respectively, with copper as oxidant. Conventionally gold extraction is assumed to require highly aggressive leaching media, high redox potentials, and high gold complex stability in the solution. The findings presented can provide a competitive environmental and economic edge and therefore new horizons for future cyanide-free gold chloride technologies, suggesting that in future, even seawater can act as the basis for cyanide free gold leaching.

Introduction

All the known industrial gold leaching methods suffer from the use of aggressive leaching media and high chemical consumption, the challenge becoming more evident with increasingly impure low-grade ores. Chlorine gas was applied as early as the 19th century for gold leaching, with Cl2 originating from hydrochloride acid on manganese oxide (Habashi, 2005; Marsden and House, 2006). In addition, bromide, bromine, cyanide, thiosulfate, and thiourea were known as early as the late 1800s and early 1900s (Marsden and House, 2006). The triumphal march of the currently dominating gold cyanide leaching process started from the deep Witwatersrand ore. With cyanide leaching, gold extraction could be increased from 55 ‒ 65%e90% (Marsden and House, 2006; Habashi, 2005). Later cyanide leaching has been operated in various modes – heap leaching, CIP (carbon in pulp), CIL (carbon in leach), and RIP/RIL (resin in pulp/leach) (Marsden and House, 2006). The environmental hazards and toxicity of cyanide have increased the interest in a new cyanide-free gold process (Aylmore, 2005). The typical challenges related to the new developmentstage cyanide-free gold processes are related to the high costs of chemicals, selectivity for gold leaching, and final gold recovery from the solutions (Hilson and Monhemius, 2006). The most promising alternatives to cyanide have been considered to be thiosulfate, thiourea, and halides (Hilson and Monhemius, 2006; Aylmore, 2005). Gold halide (iodine, bromide, and chloride) leaching has been observed to suffer from unstable gold complexes; therefore high halide concentrations and consequently high reagent consumption is typical for development-stage processes (Aylmore, 2005; Hilson and Monhemius, 2006; Marsden and House, 2006).

Abstract

In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market. Market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies, respectively. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset.

Introduction

The market for gold is one of the largest and most liquid in the world, surpassed only by the major currency pairs in terms of daily turnover.1 The price of gold, like that of many commodities, is conventionally quoted in USD. However, gold is not exclusively a US asset and so the return from an investment in gold, when calculated using the quoted USD price, conflates the change in the value of gold with the change in the value of the USD. To illustrate this point, consider the change in the gold price between 30 March 2015 and 18 May 2015. The USD price rose from 1185.79 USD per ounce to 1228.05 USD per ounce, an increase of 3.56 percent. But to a UK investor, the price of gold fell from 800.99 GBP per ounce to 783.00 GBP per ounce, a decrease of 2.25 percent. The discrepancy arises because the USD depreciated against the GBP by more than the price of gold increased in USD terms. In this paper, we develop a gold price index, which when used to compute returns, reflects changes in the intrinsic value of gold independently of concurrent changes in global exchange rates. As an illustration of the use the index, we are able to establish that over the period described above, the intrinsic value of gold decreased by 0.54%, and that the remaining changes in the price of gold in USD (+3.02%) and GBP (-1.31%) were due solely to exchange rate effects.

Domestic previous research methods for this problem are mainly grey prediction model and Markov chain combination method (Qin Sheng and Chen Yang(2010)), BP neural network model (Gu Mengjun and Zhang Zhihe 2007)) and Grey Markoff model(Qin Sheng and Chen Yang(2010)) and ARFIMA model(Lin Yu (2010)) etc. Also, long-term regression model was applied to analysis the factors influencing the gold price which can give good results about the impact on the gold price mechanism (Bai Yichi); and Qian Bingbing(2007 has selected a fuzzy time series model to determine the init ial parameters of fuzzy system, while the use of a Type-2 and Type-1 fuzzy system. Fuzzy system is a good choice for training and prediction. To some extent, these models are accurate predictors of the gold price trend. For the international part, there are a plethora of methodological approaches. As with all methodologies, each is confronted with its own pros and cons. The methods range from qualitative methods (the most commonly used methods are judge mental forecasting, intensity of-use concept and the Delphi method); cost and reserve-based methods; trend extrapolation and time-series methods (e.g. Gocht et al., 1988); causal or behavioral models; to the use of futures markets for price forecasting (e.g. Roche, 1995). In terms of trend extrapolation and time-series methods, they attempt to forecast by extrapolating from past trends of prices. In other words, they empirically evaluate trends. Time-series methods are superior to trend extrapolation in their rigor and sophistication. However, ARIMA model provides marginally better forecast results than lagged forward price model (Gillian Dooley, Helena Lanihan 2005). Recursive and rolling neural network models are applied to forecast one-step-ahead sign variations in gold price.(Antonino Parisi, Franco Parisi, David Diaz 2008); A new method considers price jumps or dips in the models which does not separate mean reverting rate with jump time to forecast the price. This type of model contains slightly modified assumptions from the classical models.(Shahriar shafiee, Erkan Topal 2010). The forecasting for platinum, silver and gold prices using trader positions is investigated in a VAR framework. Granger causality tests are conducted to determine whether a relation between trader positions and market prices exists.(Takvor H.Mutafoglu a, EkinTokat b,n, HakkiA.Tokat c 2012).

مقاله سال 2017

حسگرِ رنگ سنجی در این عمل در جهت شناسایی دقیقِ فعالیت آلفای گليكوسيدی و غربالگری آلفای بازدارندهء (AGIS) با استفاده از تغییرِ نانوذراتِ طلا (AuNPs) توسعه یافته است. دریافت استراتژی مبتنی بر واکنش کاتالیزورِ سه گانه ای است که توسط آلفای گليكوسيدی ایجاد شده است. در حضور آلفای گليكوسيدی، تجمع AuNPs بخاطر اکسیداسیون سیستئین به سیستین در سیستم ممنوع گشته است. با این حال، با اضافه نمودن AGIS، سیستئین مجموعه ای از رخداد AuNPs را ایجاد نموده است. بنابراین، روش جدیدی برای شناسایی دقیقِ فعالیت آلفای گليكوسيدی و غربالگری AGIS با برآوردِ جذب UV-VIS و یا از لحاظ تمایز دیداری توسعه داده شده است. ارتباط مناسب خطی در طیف وسیعی از ارائه داده شده است. محدودهء شناسایی در جهت برای آزمایش آلفای گليكوسيدی یافت شده است که در آن یکی از ذرات، میزان کمتری را به نسبت گزارشات دیگر بیان داشته است. مقادیر IC50 از چهار نوع بازدارنده با روشی مطابق با گزارشات دیگر مشاهده شده است. استفاده از AuNPs تغییر نیافته در این عمل از عملکرد تعدیل یافتهء زمان بر و پیچیده جلوگیری بعمل می آورد. همچنین این روش رنگ سنجیِ ساده و کارآمد می تواند در آزمایشات سایر آنزیم ها توسعه یابد.

1. مقدمه
دیابت بطور قابل توجهی به یکی از مهمترین بیماری های مزمن و رنج آور در سراسر جهان تبدیل شده است. دیابت شامل سه نوع اصلی می باشد: نوع 1، نوع 2 و دیابت بارداری. در سطح جهانی، دیابت نوع 2 یکی از رایج ترین نوع دیابت ها در میان این سه دسته است. این تقریبا برای 90٪ از تمام بیماران دیابتی در نظر گرفته شده است. علاوه بر این، دیابت نوع 2 با افزایش خطر ابتلا به بیماریهای قلبی عروقی و عصبی همراه است. بازدارندهء آلفای گلیکوسیدی (AGIS) یکی از هدف های اصلی در درمان اولیه دیابت و پیشگیری از آن می باشد؛ AGIS نقش بسیار مهمی را در کنترل قند خون بیمار پس از صرف غذا و نگه داشتن سطح قند خون در محدوده نسبتا طبیعی ایفا می کند. بنابراین، اهمیت قابل توجهی را در توسعه روش های دقیق برای تعیین فعالیت های گلیکوسیدی و بازدارندهء آن در داروهای خوراکی جدید و موثر در دیابت نوع 2 برقرار می سازد.

مقاله سال 2016

The dependence structure of financial markets is a key and debatable issue for financial economists, regulators and investors. It is the useful and important information for some financial activities, such as international portfolio’s diversification and risk management, derivative pricing, and market integration (Buccheri, Marmi, & Mantegna, 2013). For example, how to select financial assets in the international portfolio diversification is the crucial step, which needs to examine the dependence of financial asset returns and check whether they have the dependence hierarchical or clustering structure. As such, a growing number of methods are developed to capture the dependence structure among different financial asset returns, while the dependence (or correlation) network analytical tool is one of the most popular and widely used approaches (see, e.g.,Brida, Gómez, & Risso, 2009; Brida & Risso, 2010; Fenn et al., 2012; Kwapien & Dro ´ zd˙ z, 2012; Kwapie ˙ n, Gworek, & Dro ´ zd˙ z, 2009; Man- ˙ tegna, 1999; Tumminello, Lillo, & Mantegna, 2010; Wang, Xie, Chen, & Chen, 2013a; Wang, Xie, Han, & Sun, 2012). Different approaches can be used to build the dependence network, such as the minimum spanning tree (MST) approach (Mantegna, 1999), the planar maximally filtered graph (PMFG) method (Tumminello, Aste, Matteo, & Mantegna, 2005), and the correlation threshold method (Boginski, Butenko, & Pardalos, 2005; Onnela, Kaski, & Kertész, 2004), which are designed to select or filter the information presented in the dependence (or correlation) matrix. In other words, before constructing the financial networks, one should build the dependence matrix of the financial asset returns by the dependence measure, while the common practice of the dependence measure is chosen as the Pearson’s correlation coefficient (PCC). Although PCC is easy to calculate and widely used, some questions are posed on the practice of using PCC as a universal dependence measure (see, e.g., Rachev, Fabozzi, & Menn, 2005; Wang et al., 2012; Zebende, 2011; Zhou & Gao, 2012).

There are at least two drawbacks for PCC as follows. (1) The theoretical assumption of PCC is that the joint distributions of time series obey Gaussian distribution. However, a large deal of evidence shows that the dependence between different financial asset returns is nonGaussian (see, e.g., Bae, 2003; Durante, Foscolo, Jaworski, & Wang, 2014; Wang, Xie, Zhang, Han, & Chen, 2014). A good instance is that the dependence of financial asset returns during a recession is larger than during a boom (Zhou & Gao, 2012). Besides, many studies report that the dependence of financial markets comes to a remarkable peak during the largest market shocks and financial crises (see, e.g., Aste, Shaw, & Di Matteo, 2010; Podobnik, Wang, Horvatic, Grosse, & Stanley, 2010; Wang, Xie, Chen, Yang, & Yang, 2013b). (2) PCC is defined to quantify the linear correlation for the whole range of sample. It ignores the fact that the real world data are characterized by a high level of heterogeneity (Wang et al., 2013b). Namely, it neglects the difference between extreme and commonplace observations. It is a fact that as the frequency of financial crisis doubled in recent years, the extreme returns of financial time series occurred increasingly. Therefore, PCC may lose effectiveness and be misleading if the investigated asset returns are heterogeneous and the extreme returns show different patterns of dependence from the remaining returns (Zhou & Gao, 2012). In a word, the previous dependence network methods cannot accurately detect the dependence structure of financial markets, especially during the financial crises (e.g., US sub-prime crisis, 2008 fi- nancial crisis, and European debt crisis).

To overcome the shortcomings of linear correlation functions (e.g., PCC), scholars resort to a powerfully and widely used tool—copulas proposed by Sklar’s (1959). Copulas are flexible and effective tools to measure dependence structure between two or more variables and model any type of multivariate distributions, which go beyond the linear correlation. Specifically, they allow for the tail dependence that represents the level of dependence among the tails of financial asset distributions (Sun, 2013; Zhou & Gao, 2012). In detail, tail dependence refers to the level of dependence in the lower and upper quadrant tails of a bivariate distribution, so it is a suitable measure of the dependence of extreme events. That is to say, tail dependence can be divided into two measures, namely the lower-tail dependence and upper-tail dependence, which are used respectively to investigate the joint extreme events in financial asset returns during the market downturns and market upturns (Hu, 2010). However, in the common practice of measuring dependence, people usually ignore the extreme returns that hide in the tails and the tail dependence among financial asset returns, which is dangerous for investment portfolios and other financial activities especially during the market downturns. So, more attentions should be paid to the tail dependence of financial markets. Unfortunately, to the best of our knowledge, very little of the existing research considers the tail dependence of the foreign exchange (FX) market from a network point of view.

مقاله سال 2016

 مجله تحقیقات بازاریابی – Journal of Business Research

“Creating the right mix of investments for effective use of limited resources while providing the maximum business benefit is the ultimate challenge for business organizations” (Gabas-Varini, 2003).

To attain a sustainable competitive advantage, companies require insight about their customers. By understanding customer needs and value, enterprises can increase the value of each customer relationship. Understanding the value of each customer relationship enables companies to segment customers into “portfolios of relationships” that would increase a company’s return on relationships (Johnson & Selnes, 2004; Kumar, 2010).

By segmenting customers into portfolios, an organization can better understand the relative importance each customer represents relative to total sales and profits. Such an understanding will assist companies not only in retaining valuable customers but also in creating additional value with these customers through relationship development. Consequently, the organization can methodically allocate resources and apply marketing strategies toward the retention and development of its most valued customers, better known as customer portfolio management (CPM). CPM analysis reveals the small number of strategically important customers that contribute to the current and future value of the company and should receive the company’s resources (Terho, 2008).