For many years the Stocks, Bonds, Bills & Inflation yearbook has served as the primary source for calibrating historical asset returns. However, uneasiness has grown about its depiction of corporate bond returns prior to the second World War. I document problems with the source data used in the SBBI and replace its flawed dataset with new observations of bond prices from 1926 to 1946 for a sample of several hundred large bonds listed on the NYSE and rated investment-grade. I find that the SBBI overstates corporate bond returns in the 1930s and accordingly, gives an unreliable estimate of the premium received for owning investment grade corporate bonds rather than government bonds during the prewar years. To extend the analysis I collected additional bond price data from 1946 to 1974 and find that the SBBI also overstates corporate bond returns in the 1960s. The problem again stems from a reliance on flawed yield series in place of observing bond prices. I combine the new data with existing data to examine the corporate bond premium from 1909 through 2019. Using ten-year rolling returns, over the past century I find the average premium earned on long maturity corporate bonds to be small, about 15 basis points annualized. For many of the ten-year rolls, the premium was instead a deficit: a bond investor would have done better owning only long government bonds. The small and fitful premium contrasts with the yield spread on investment-grade bonds, which was always positive and substantial throughout the period. Because the premium has been much more variable, the relative size of the yield spread does not seem to be predictive of whether a premium will subsequently be earned and how much. Results are interpreted in terms of the importance of regime change in financial history: sometimes corporate bonds outperform government bonds, sometimes they do not, just as sometimes stocks outperform bonds, and sometimes they do not, contra Siegel (2014). The idea of regime change challenges the notion that a mean computed over a longer rather than a shorter interval contributes any additional predictive power to the study of asset returns over human horizons.
{"title":"When Do Corporate Bond Investors Earn a Premium for Bearing Risk? A Test Spanning the Great Depression of the 1930s","authors":"Edward F. Mcquarrie","doi":"10.2139/ssrn.3740190","DOIUrl":"https://doi.org/10.2139/ssrn.3740190","url":null,"abstract":"For many years the Stocks, Bonds, Bills & Inflation yearbook has served as the primary source for calibrating historical asset returns. However, uneasiness has grown about its depiction of corporate bond returns prior to the second World War. I document problems with the source data used in the SBBI and replace its flawed dataset with new observations of bond prices from 1926 to 1946 for a sample of several hundred large bonds listed on the NYSE and rated investment-grade. I find that the SBBI overstates corporate bond returns in the 1930s and accordingly, gives an unreliable estimate of the premium received for owning investment grade corporate bonds rather than government bonds during the prewar years. To extend the analysis I collected additional bond price data from 1946 to 1974 and find that the SBBI also overstates corporate bond returns in the 1960s. The problem again stems from a reliance on flawed yield series in place of observing bond prices. I combine the new data with existing data to examine the corporate bond premium from 1909 through 2019. Using ten-year rolling returns, over the past century I find the average premium earned on long maturity corporate bonds to be small, about 15 basis points annualized. For many of the ten-year rolls, the premium was instead a deficit: a bond investor would have done better owning only long government bonds. The small and fitful premium contrasts with the yield spread on investment-grade bonds, which was always positive and substantial throughout the period. Because the premium has been much more variable, the relative size of the yield spread does not seem to be predictive of whether a premium will subsequently be earned and how much. Results are interpreted in terms of the importance of regime change in financial history: sometimes corporate bonds outperform government bonds, sometimes they do not, just as sometimes stocks outperform bonds, and sometimes they do not, contra Siegel (2014). The idea of regime change challenges the notion that a mean computed over a longer rather than a shorter interval contributes any additional predictive power to the study of asset returns over human horizons.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130882820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Extended examples of long-term, annually rebalanced portfolio performance using the modified risk parity (MRP) approach are presented. The analysis considers three distinct diversified portfolio holdings, comprising index funds, sector funds and a blended portfolio of index and sector funds. The analysis considers the time period 2000-mid November 2020, which includes drawdown from the March 2020 worldwide outbreak of coronavirus 2019 disease. Comparisons of MRP return performance versus competitive portfolios of index-following or balanced funds are presented. Results indicate that for sufficiently long holding periods, substantial out-performance of MRP allocation strategies relative to passive buy-and-hold benchmarks can be realized.
{"title":"Investing in the Year of Corona: The Modified Risk Parity Portfolios","authors":"A. Maewal, Joel R. Bock","doi":"10.2139/ssrn.3738846","DOIUrl":"https://doi.org/10.2139/ssrn.3738846","url":null,"abstract":"Extended examples of long-term, annually rebalanced portfolio performance using the modified risk parity (MRP) approach are presented. The analysis considers three distinct diversified portfolio holdings, comprising index funds, sector funds and a blended portfolio of index and sector funds. \u0000 \u0000The analysis considers the time period 2000-mid November 2020, which includes drawdown from the March 2020 worldwide outbreak of coronavirus 2019 disease. \u0000 \u0000Comparisons of MRP return performance versus competitive portfolios of index-following or balanced funds are presented. Results indicate that for sufficiently long holding periods, substantial out-performance of MRP allocation strategies relative to passive buy-and-hold benchmarks can be realized.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"27 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116376454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. Identifying commonalities in the financial structures of Fintech-adopting banks, we develop the "Fintech score." Analysis of on-balance sheet lending, securitization, brokered deposits and non-interest income demonstrates that more broker-like (dealer-like) banks have high (low) Fintech scores. Using Data Envelopment and Stochastic Cost Frontier Analyses, banks with higher Fintech scores are more operationally efficient and resilient in crises.
{"title":"Cutting Operational Costs by Integrating Fintech into Traditional Banking Firms","authors":"Linda Allen, Y. Shan, Yi Tang, Alev Yildirim","doi":"10.2139/ssrn.3703840","DOIUrl":"https://doi.org/10.2139/ssrn.3703840","url":null,"abstract":"Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. Identifying commonalities in the financial structures of Fintech-adopting banks, we develop the \"Fintech score.\" Analysis of on-balance sheet lending, securitization, brokered deposits and non-interest income demonstrates that more broker-like (dealer-like) banks have high (low) Fintech scores. Using Data Envelopment and Stochastic Cost Frontier Analyses, banks with higher Fintech scores are more operationally efficient and resilient in crises.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115607551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper extends the life-cycle model by allowing for a small risk of a personal disaster with permanent effects on labour income. We calibrate the model to long-term unemployment (LTU) in the US, where it is only partially insured and is known to entail scarring effects. Despite its low probability, such risk boosts early investment in the risk-free asset. Consequently, the optimal equity portfolio share is relatively flat over the life cycle, consistent with observed investment profiles. A negligible probability of LTU or full insurance against it result in both higher optimal risk taking and equity profiles that are downward sloping in age.
{"title":"Life-cycle Investing with Personal Disaster Risk","authors":"F. Bagliano, C. Fugazza, G. Nicodano","doi":"10.2139/ssrn.3710543","DOIUrl":"https://doi.org/10.2139/ssrn.3710543","url":null,"abstract":"This paper extends the life-cycle model by allowing for a small risk of a personal disaster with permanent effects on labour income. We calibrate the model to long-term unemployment (LTU) in the US, where it is only partially insured and is known to entail scarring effects. Despite its low probability, such risk boosts early investment in the risk-free asset. Consequently, the optimal equity portfolio share is relatively flat over the life cycle, consistent with observed investment profiles. A negligible probability of LTU or full insurance against it result in both higher optimal risk taking and equity profiles that are downward sloping in age.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130773503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter described tools, structures and instruments used by impact investors to effectively apply their theories of environmental and/or social change. Impact tools are actions that impact investors can take to create a portfolio that is aligned with the investor’s investment and impact goals. Each impact investment needs to be structured properly to optimize impact while fitting within the investor’s overarching impact investment goals and policies. Impact structures are a function of decisions made regarding the investor, intermediary and enterprise vehicles and the use of certain transactional tools that are intended to drive specific impact outcomes. The selection of the investment instrument that will be used by the investor or the intermediary to provide capital to the enterprise depends on a number of factors including the investment goals and risk tolerances of the investor/intermediary; the legal structure of the enterprise; the instruments that the enterprise have previously issued to other investors, since the relative priorities of different groups of investors with respect to return of capital and their rights to the assets of the enterprise as collateral for their investment must always be clear; the relative cost of the financing to the business and the existing owners; the risks associated with the instrument and the degree of flexibility associated with any payment obligations under the terms of the instrument.
{"title":"Impact Investment Tools, Structures and Instruments","authors":"Alan S. Gutterman","doi":"10.2139/ssrn.3823843","DOIUrl":"https://doi.org/10.2139/ssrn.3823843","url":null,"abstract":"This chapter described tools, structures and instruments used by impact investors to effectively apply their theories of environmental and/or social change. Impact tools are actions that impact investors can take to create a portfolio that is aligned with the investor’s investment and impact goals. Each impact investment needs to be structured properly to optimize impact while fitting within the investor’s overarching impact investment goals and policies. Impact structures are a function of decisions made regarding the investor, intermediary and enterprise vehicles and the use of certain transactional tools that are intended to drive specific impact outcomes. The selection of the investment instrument that will be used by the investor or the intermediary to provide capital to the enterprise depends on a number of factors including the investment goals and risk tolerances of the investor/intermediary; the legal structure of the enterprise; the instruments that the enterprise have previously issued to other investors, since the relative priorities of different groups of investors with respect to return of capital and their rights to the assets of the enterprise as collateral for their investment must always be clear; the relative cost of the financing to the business and the existing owners; the risks associated with the instrument and the degree of flexibility associated with any payment obligations under the terms of the instrument.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent changes in Australian legislation that limit the value of how artworks that can be considered as assets in retirement funds have had an impact on the Australian Aboriginal Art market. In this paper we estimate the impact of these changes on the price index based on prices paid for 15,845 works by over 200 artists at art auctions from 1986 to 2019.
Using an OLS and a quantile regression approach, we estimate hedonic price models for various segments of the Australian Aboriginal art market. These models are used to estimate price indices in order to investigate if the changes in Australian laws concerning the sale and use of art assets has influenced the potential returns for different segments of the market.
{"title":"Investment in Australian Aboriginal Art","authors":"J. Lye, J. Hirschberg","doi":"10.2139/ssrn.3743883","DOIUrl":"https://doi.org/10.2139/ssrn.3743883","url":null,"abstract":"Recent changes in Australian legislation that limit the value of how artworks that can be considered as assets in retirement funds have had an impact on the Australian Aboriginal Art market. In this paper we estimate the impact of these changes on the price index based on prices paid for 15,845 works by over 200 artists at art auctions from 1986 to 2019.<br><br>Using an OLS and a quantile regression approach, we estimate hedonic price models for various segments of the Australian Aboriginal art market. These models are used to estimate price indices in order to investigate if the changes in Australian laws concerning the sale and use of art assets has influenced the potential returns for different segments of the market.<br>","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132401551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Benhamou, D. Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity , in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving , robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment , (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.
{"title":"Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning","authors":"E. Benhamou, D. Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay","doi":"10.2139/ssrn.3702112","DOIUrl":"https://doi.org/10.2139/ssrn.3702112","url":null,"abstract":"While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity , in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving , robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment , (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133132870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate whether climate transition risk is reflected in the financial performance and cross-section pricing of publicly-traded European and US firms. Using a firm-level carbon risk score (CRS) that assesses the vulnerability of a firm’s value to transition to a low-carbon economy, we find that firms with the lowest transition risk exposures perform better financially, and that European firms are more sensitive to transition risks than US firms. We also find that stocks with low exposure to transition risk offer greater returns to investors, consistent with the fact that stock prices of firms do not adequately reflect underlying climate transition risk. Relative financial performance of less vulnerable firms and underreaction effects to transition risk decreased after COP21.
{"title":"Climate Transition Risk, Profitability and Stock Prices","authors":"J. Reboredo, A. Ugolini","doi":"10.2139/ssrn.3847687","DOIUrl":"https://doi.org/10.2139/ssrn.3847687","url":null,"abstract":"We investigate whether climate transition risk is reflected in the financial performance and cross-section pricing of publicly-traded European and US firms. Using a firm-level carbon risk score (CRS) that assesses the vulnerability of a firm’s value to transition to a low-carbon economy, we find that firms with the lowest transition risk exposures perform better financially, and that European firms are more sensitive to transition risks than US firms. We also find that stocks with low exposure to transition risk offer greater returns to investors, consistent with the fact that stock prices of firms do not adequately reflect underlying climate transition risk. Relative financial performance of less vulnerable firms and underreaction effects to transition risk decreased after COP21.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"932 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133383041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose- The outbreak of the COVID-19 pandemic is making the global economy succumb under pressure. The effect of the pandemic is notable especially in the context of developing countries like India. The situation is questioning the sustenance of many organizations and has made investors sceptical about their income allocation. Hence, the shift in the global markets calls upon for new criteria to help address future ambiguity. Design/ Methodology/ Approach- The paper proposes a hybrid multi-criteria decision making (MCDM) model for portfolio selection, under fuzziness. The paper draws upon the methodologies of the fuzzy analytic hierarchy process (FAHP) for obtaining the criterion weightage based on inputs obtained from industry peers. The study considers five criteria namely risk, return, liquidity, environmental, and governmental factors. Thus, five types of portfolios offered by an investment firm are ranked based on these criteria using the technique for order preferences by similarity to ideal solution (TOPSIS) method. Findings- The data analysis and results inferred, further support the proposed model. The findings also align with the previous studies undertaken and substantiate the inputs obtained from industry professionals. Also, the discussions are based on the research findings along with implications for multiple stakeholders- prospective investors and academicians. Originality- As part of the study two distinct variables namely environmental and governmental variables were considered in accordance with the future uncertainty of the markets due to pandemic, which have not been used in previous studies.
{"title":"A Fuzzy MCDM Model for Post-COVID Portfolio Selection","authors":"Heman Amarjeet Paur","doi":"10.2139/ssrn.3888568","DOIUrl":"https://doi.org/10.2139/ssrn.3888568","url":null,"abstract":"Purpose- The outbreak of the COVID-19 pandemic is making the global economy succumb under pressure. The effect of the pandemic is notable especially in the context of developing countries like India. The situation is questioning the sustenance of many organizations and has made investors sceptical about their income allocation. Hence, the shift in the global markets calls upon for new criteria to help address future ambiguity. Design/ Methodology/ Approach- The paper proposes a hybrid multi-criteria decision making (MCDM) model for portfolio selection, under fuzziness. The paper draws upon the methodologies of the fuzzy analytic hierarchy process (FAHP) for obtaining the criterion weightage based on inputs obtained from industry peers. The study considers five criteria namely risk, return, liquidity, environmental, and governmental factors. Thus, five types of portfolios offered by an investment firm are ranked based on these criteria using the technique for order preferences by similarity to ideal solution (TOPSIS) method. Findings- The data analysis and results inferred, further support the proposed model. The findings also align with the previous studies undertaken and substantiate the inputs obtained from industry professionals. Also, the discussions are based on the research findings along with implications for multiple stakeholders- prospective investors and academicians. Originality- As part of the study two distinct variables namely environmental and governmental variables were considered in accordance with the future uncertainty of the markets due to pandemic, which have not been used in previous studies.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114396728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Literature suggests assets become more correlated during economic downturns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocur-rencies provide a diversification for equities is still an unsettled issue. Additionally , the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures , including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We find co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors differentiating themselves as safe havens.
{"title":"Diversifying with Cryptocurrencies during COVID-19","authors":"John W. Goodell, Stéphane Goutte","doi":"10.2139/ssrn.3631971","DOIUrl":"https://doi.org/10.2139/ssrn.3631971","url":null,"abstract":"Literature suggests assets become more correlated during economic downturns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocur-rencies provide a diversification for equities is still an unsettled issue. Additionally , the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures , including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We find co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors differentiating themselves as safe havens.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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