Over 85% of all foreign exchange (FX) transactions involve the US dollar, whereas the United States accounts for a much smaller fraction of global economic activity. My paper attributes the dominance of the US dollar in FX trading to strategic avoidance of price impact. Utilising a model of FX trading, I derive three conditions for dollar dominance. I then empirically test these conditions using a globally representative FX trade data set and provide evidence that is consistent with my model. I find that US dollar currency pairs enjoy a low-price-impact advantage, which favours their use as a vehicle currency to indirectly exchange two non-dollar currencies. Using a novel identification strategy, I show that up to 36-40% of the daily volume in dollar currency pairs are due to vehicle currency trading.
{"title":"Dollar Dominance in FX Trading","authors":"Fabricius Somogyi","doi":"10.2139/ssrn.3882546","DOIUrl":"https://doi.org/10.2139/ssrn.3882546","url":null,"abstract":"Over 85% of all foreign exchange (FX) transactions involve the US dollar, whereas the United States accounts for a much smaller fraction of global economic activity. My paper attributes the dominance of the US dollar in FX trading to strategic avoidance of price impact. Utilising a model of FX trading, I derive three conditions for dollar dominance. I then empirically test these conditions using a globally representative FX trade data set and provide evidence that is consistent with my model. I find that US dollar currency pairs enjoy a low-price-impact advantage, which favours their use as a vehicle currency to indirectly exchange two non-dollar currencies. Using a novel identification strategy, I show that up to 36-40% of the daily volume in dollar currency pairs are due to vehicle currency trading.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132528619","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}
I. Filippou, A. Gozluklu, My T. Nguyen, Ganesh Viswanath-Natraj
Using textual analysis, we identify the set of Trump tweets that contain information on macroeconomic policy, trade, or exchange rate content. We then analyze the effects of Trump tweets on the intraday trading activity of foreign exchange markets, such as trading volume, volatility, and FX spot returns. We find that Trump tweets reduce speculative trading, with a corresponding decline in trading volume and volatility, and induce a bias reflecting Trump’s (optimistic) views on the U.S. economy. We rationalize these results within a model of Trump tweets revealing economic content as a public signal that reduces disagreement among speculators.
{"title":"The Information Content of Trump Tweets and the Currency Market","authors":"I. Filippou, A. Gozluklu, My T. Nguyen, Ganesh Viswanath-Natraj","doi":"10.2139/ssrn.3754991","DOIUrl":"https://doi.org/10.2139/ssrn.3754991","url":null,"abstract":"Using textual analysis, we identify the set of Trump tweets that contain information on macroeconomic policy, trade, or exchange rate content. We then analyze the effects of Trump tweets on the intraday trading activity of foreign exchange markets, such as trading volume, volatility, and FX spot returns. We find that Trump tweets reduce speculative trading, with a corresponding decline in trading volume and volatility, and induce a bias reflecting Trump’s (optimistic) views on the U.S. economy. We rationalize these results within a model of Trump tweets revealing economic content as a public signal that reduces disagreement among speculators.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123919673","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}
Marcel Fratzscher, Tobias Heidland, Lukas Menkhoff, Lucio Sarno, Maik Schmeling
We construct a novel database of monthly foreign exchange interventions for 49 countries over up to 22 years. We build on a text classification approach that extracts information about interventions from news articles and calibrate our procedure to data about actual interventions. Our new dataset allows us to document stylized facts about the use of foreign exchange interventions for countries that neither publish their data nor make them available to researchers. Moreover, we show that foreign exchange interventions are used in a complementary way with capital controls and macroprudential regulation.
{"title":"Foreign Exchange Intervention: A New Database","authors":"Marcel Fratzscher, Tobias Heidland, Lukas Menkhoff, Lucio Sarno, Maik Schmeling","doi":"10.2139/ssrn.3735833","DOIUrl":"https://doi.org/10.2139/ssrn.3735833","url":null,"abstract":"We construct a novel database of monthly foreign exchange interventions for 49 countries over up to 22 years. We build on a text classification approach that extracts information about interventions from news articles and calibrate our procedure to data about actual interventions. Our new dataset allows us to document stylized facts about the use of foreign exchange interventions for countries that neither publish their data nor make them available to researchers. Moreover, we show that foreign exchange interventions are used in a complementary way with capital controls and macroprudential regulation.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122414658","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}
Mansour Abdelrhim, Abdullah M. Elsayed, Mahmoud Mohamed, Mahmoud Farouh
The global financial and economic crises remain a controversial topic among the categories of investors, there are those who can see and seize investment opportunities, and there are some individuals who are proficient in investing in difficult economic conditions and can create opportunities even in the most difficult crises. With the emergence of the corona virus in China, it quickly became a global pandemic and caused tremendous damage to many global financial and economic markets.
This paper aims to shed light on investment opportunities in the global markets in light of the spread of the corona virus around the world, by studying the effect of the corona virus on the returns of the cryptocurrency currency and global metals markets traded in the US dollar, where the research period was determined based on the spread of the virus at a level The world, from the date of 25/3/2020 to 25/6/2020, and the best cryptocurrency currencies were chosen in terms of market value and trading during the research period and were Bitcoin, Ethereum and Tether, and the best metals in terms of popularity and trading were gold, silver and copper. Corona virus was measured by indicators of virus spread, which are the number of daily cases, cumulative cases and the number of daily deaths and cumulative deaths, at the level of 213 countries around the world, and the dependent variable represented in the cryptocurrency and metal markets was measured by the daily returns of the investment opportunities that were chosen in each market.
The research results showed that the cryptocurrency currency markets are affected by the spread of the corona virus and the independent variable was the most influential (Total Deaths) variable on all investment opportunities in the cryptocurrency market. The Total Deaths variable had more influence on the Gold market, and Total Cases variable had more influence on both Silver and Copper in the metals market.
The results also showed that there were no statistically significant differences between the average return on investment for the cryptocurrency currency markets and the metal markets, where the significance of the test reached (0.889), which is greater than the level of significance of 5%, due to the convergence of the average levels of the markets during the period of coronary virus spread throughout the world.
The best investment opportunities according to the return on investment index during the research period were, for cryptocurrency currency markets, the return on investment on Ethereum was (72.02%), then Bitcoin (38.98%), then Tether (0.23%), and in relation to the metal markets, the return was on the investment for Silver was (42.16%), then Copper (20.75%), then Gold (8.54%).
{"title":"Investment Opportunities in The Time Of (COVID-19) Spread: The Case of Cryptocurrencies and Metals Markets","authors":"Mansour Abdelrhim, Abdullah M. Elsayed, Mahmoud Mohamed, Mahmoud Farouh","doi":"10.2139/ssrn.3640333","DOIUrl":"https://doi.org/10.2139/ssrn.3640333","url":null,"abstract":"The global financial and economic crises remain a controversial topic among the categories of investors, there are those who can see and seize investment opportunities, and there are some individuals who are proficient in investing in difficult economic conditions and can create opportunities even in the most difficult crises. With the emergence of the corona virus in China, it quickly became a global pandemic and caused tremendous damage to many global financial and economic markets.<br><br>This paper aims to shed light on investment opportunities in the global markets in light of the spread of the corona virus around the world, by studying the effect of the corona virus on the returns of the cryptocurrency currency and global metals markets traded in the US dollar, where the research period was determined based on the spread of the virus at a level The world, from the date of 25/3/2020 to 25/6/2020, and the best cryptocurrency currencies were chosen in terms of market value and trading during the research period and were Bitcoin, Ethereum and Tether, and the best metals in terms of popularity and trading were gold, silver and copper. Corona virus was measured by indicators of virus spread, which are the number of daily cases, cumulative cases and the number of daily deaths and cumulative deaths, at the level of 213 countries around the world, and the dependent variable represented in the cryptocurrency and metal markets was measured by the daily returns of the investment opportunities that were chosen in each market.<br><br>The research results showed that the cryptocurrency currency markets are affected by the spread of the corona virus and the independent variable was the most influential (Total Deaths) variable on all investment opportunities in the cryptocurrency market. The Total Deaths variable had more influence on the Gold market, and Total Cases variable had more influence on both Silver and Copper in the metals market.<br><br>The results also showed that there were no statistically significant differences between the average return on investment for the cryptocurrency currency markets and the metal markets, where the significance of the test reached (0.889), which is greater than the level of significance of 5%, due to the convergence of the average levels of the markets during the period of coronary virus spread throughout the world.<br><br>The best investment opportunities according to the return on investment index during the research period were, for cryptocurrency currency markets, the return on investment on Ethereum was (72.02%), then Bitcoin (38.98%), then Tether (0.23%), and in relation to the metal markets, the return was on the investment for Silver was (42.16%), then Copper (20.75%), then Gold (8.54%).","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067497","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}
Cryptocurrencies employ different consensus protocols to verify transactions. While the “proof-of-work” consensus protocol is the most energy-consuming protocol, “proof-of-stake” and the hybrid of these two consensus protocols, which consume considerably less energy, have also been introduced. We employ portfolio analysis to explore whether energy is a fundamental economic factor affecting cryptocurrency prices. Surprisingly, our results suggest that, on average, cryptocurrencies employing proof-of-work consensus protocols do not generate returns that are significantly different from those that incorporate proof-of-stake consensus protocols. Even more surprising is that our results show that cryptocurrencies that incorporate the hybrid version of these consensus protocols generate significantly higher average returns than the other groups. A possible explanation for this phenomenon may be that the cryptocurrency market is still driven by the trust factor rather than the energy factor.
{"title":"Blockchain Consensus Protocols, Energy Consumption and Cryptocurrency Prices","authors":"Niranjan Sapkota, Klaus Grobys","doi":"10.2139/ssrn.3403983","DOIUrl":"https://doi.org/10.2139/ssrn.3403983","url":null,"abstract":"Cryptocurrencies employ different consensus protocols to verify transactions. While the “proof-of-work” consensus protocol is the most energy-consuming protocol, “proof-of-stake” and the hybrid of these two consensus protocols, which consume considerably less energy, have also been introduced. We employ portfolio analysis to explore whether energy is a fundamental economic factor affecting cryptocurrency prices. Surprisingly, our results suggest that, on average, cryptocurrencies employing proof-of-work consensus protocols do not generate returns that are significantly different from those that incorporate proof-of-stake consensus protocols. Even more surprising is that our results show that cryptocurrencies that incorporate the hybrid version of these consensus protocols generate significantly higher average returns than the other groups. A possible explanation for this phenomenon may be that the cryptocurrency market is still driven by the trust factor rather than the energy factor.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123896130","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}
At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower.
{"title":"The Cross-Section of Cryptocurrency Returns","authors":"Nicola Borri, K. Shakhnov","doi":"10.2139/ssrn.3241485","DOIUrl":"https://doi.org/10.2139/ssrn.3241485","url":null,"abstract":"\u0000 At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132163192","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 contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).
{"title":"Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas","authors":"Toan Luu Duc Huynh","doi":"10.3390/JRFM12020052","DOIUrl":"https://doi.org/10.3390/JRFM12020052","url":null,"abstract":"This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741435","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 examines how trading in the FX market carries the information that drives movements in currency prices over minutes, days and weeks; and now those movements are connected to interest rates. The paper first presents a model of FX trading in a Limit Order Book (LOB) that identifies how information from outside the market is reflected in FX prices and trading patterns. I then empirically examine this transmission process with the aid of a structural VAR estimated on 13 years of LOB trading data for the EURUSD, the world's most heavily traded currency pair. The VAR estimates reveal several new findings: first, they show that shocks from outside the LOB affect FX prices through both liquidity and information channel; and that the importance of these channels varies according to the source of the shock. Liquidity effects on FX prices are temporary, lasting between two and ten minutes, while information effects of shocks on prices are permanent. Second, the contemporaneous correlation between price changes and order flows varies across the shocks. Some shocks produce a positive correlation (as in standard trading models), while others produce a negative correlation. Third, the model estimates imply that intraday variations in FX prices are overwhelmingly driven by one type of shock, it accounts for 87% of hour-by-hour changes in the FX prices. The second part of the paper examines the connection between the shocks in the trading model and the macroeconomy. For this purpose, I use the VAR estimates to decompose intraday FX price changes and order flows into separate components driven by different shocks. I then aggregate these components into daily and weekly series. I find that one component of daily order flow is strongly correlated with changes in the long-term interest differentials between US and EUR rates. This suggests that the intraday shocks driving this order flow component carry news about future short-term interest rates which is embedded into FX prices. I find that intraday shocks carrying interest-rate information account for on average 56% of the variance in daily EURUSD depreciation rate between 2003 and 2015, but their variance contributions before 2007 and after 2011 are over 80%. These findings indicate that the EURUSD depreciation rate is relatively well-connected to macro fundamentals via a particular component of order flow. Finally, I show that flows embedding liquidity risk have forecasting power for daily and weekly EURUSD depreciation rates.
{"title":"FX Trading and the Exchange Rate Disconnect Puzzle","authors":"Martin D. D. Evans","doi":"10.2139/ssrn.3278950","DOIUrl":"https://doi.org/10.2139/ssrn.3278950","url":null,"abstract":"This paper examines how trading in the FX market carries the information that drives movements in currency prices over minutes, days and weeks; and now those movements are connected to interest rates. The paper first presents a model of FX trading in a Limit Order Book (LOB) that identifies how information from outside the market is reflected in FX prices and trading patterns. I then empirically examine this transmission process with the aid of a structural VAR estimated on 13 years of LOB trading data for the EURUSD, the world's most heavily traded currency pair. The VAR estimates reveal several new findings: first, they show that shocks from outside the LOB affect FX prices through both liquidity and information channel; and that the importance of these channels varies according to the source of the shock. Liquidity effects on FX prices are temporary, lasting between two and ten minutes, while information effects of shocks on prices are permanent. Second, the contemporaneous correlation between price changes and order flows varies across the shocks. Some shocks produce a positive correlation (as in standard trading models), while others produce a negative correlation. Third, the model estimates imply that intraday variations in FX prices are overwhelmingly driven by one type of shock, it accounts for 87% of hour-by-hour changes in the FX prices. The second part of the paper examines the connection between the shocks in the trading model and the macroeconomy. For this purpose, I use the VAR estimates to decompose intraday FX price changes and order flows into separate components driven by different shocks. I then aggregate these components into daily and weekly series. I find that one component of daily order flow is strongly correlated with changes in the long-term interest differentials between US and EUR rates. This suggests that the intraday shocks driving this order flow component carry news about future short-term interest rates which is embedded into FX prices. I find that intraday shocks carrying interest-rate information account for on average 56% of the variance in daily EURUSD depreciation rate between 2003 and 2015, but their variance contributions before 2007 and after 2011 are over 80%. These findings indicate that the EURUSD depreciation rate is relatively well-connected to macro fundamentals via a particular component of order flow. Finally, I show that flows embedding liquidity risk have forecasting power for daily and weekly EURUSD depreciation rates.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113989401","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 article provides a new framework to evaluate the status of Renminbi internationalization. It proposes that the trading patterns of a currency in global foreign exchange market embody the currency’s position in the international monetary system. Based on foreign exchange trading data provided by CLS Group, the article constructs a ranking of major international currencies including Renminbi. It finds that Renminbi shares more similarities in foreign exchange trading patterns with the established global currencies like US dollar and Euro than with those regional currencies. The article also explores the policy implications that the new evaluation approach provides.
{"title":"Is Renminbi a (Truly) International Currency? An Evaluation Based on Offshore Foreign Exchange Market Trading Patterns","authors":"Lian Cheng, Junru Luo, Lin Liu","doi":"10.2139/ssrn.3263593","DOIUrl":"https://doi.org/10.2139/ssrn.3263593","url":null,"abstract":"This article provides a new framework to evaluate the status of Renminbi internationalization. It proposes that the trading patterns of a currency in global foreign exchange market embody the currency’s position in the international monetary system. Based on foreign exchange trading data provided by CLS Group, the article constructs a ranking of major international currencies including Renminbi. It finds that Renminbi shares more similarities in foreign exchange trading patterns with the established global currencies like US dollar and Euro than with those regional currencies. The article also explores the policy implications that the new evaluation approach provides.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117337129","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}
The substantial growth of the crytocurrency market since 2009 has merited suspicions of bubblelike dynamics attributed to the exceptional price growth and volatility exhibited across associated exchanges. The deep volatility and exponential rise in cryptocurrencies valuations strongly suggest that both long memory and price volatility spillovers should be present in these assets dynamics. To date, literature on the major cryptocurrencies price processes does not address jointly and comprehensively their fractal properties, long memory and wavelet analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies. Having performed both analyses, our overall results that Bitcoin prices show persistency. This trend has been reducing overtime. Assessing the period 2016 between 2017, Bitcoin is better described by a random walk while less mature cryptocurrencies such as Ethereum and Ripple present evidence of persistence behaviour, and may be better described as a random walk. We conclude that Bitcoin may be described as a ‘True Hurst Process’, where crowd behaviour and technical information tend to dominate the leading cryptocurrency’s price development.
{"title":"Fractal Dynamics and Wavelet Analysis: Deep Volatility Properties of Bitcoin, Ethereum and Ripple","authors":"Valerio Celeste, S. Corbet, Constantin Gurdgiev","doi":"10.2139/ssrn.3232913","DOIUrl":"https://doi.org/10.2139/ssrn.3232913","url":null,"abstract":"The substantial growth of the crytocurrency market since 2009 has merited suspicions of bubblelike dynamics attributed to the exceptional price growth and volatility exhibited across associated exchanges. The deep volatility and exponential rise in cryptocurrencies valuations strongly suggest that both long memory and price volatility spillovers should be present in these assets dynamics. To date, literature on the major cryptocurrencies price processes does not address jointly and comprehensively their fractal properties, long memory and wavelet analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies. Having performed both analyses, our overall results that Bitcoin prices show persistency. This trend has been reducing overtime. Assessing the period 2016 between 2017, Bitcoin is better described by a random walk while less mature cryptocurrencies such as Ethereum and Ripple present evidence of persistence behaviour, and may be better described as a random walk. We conclude that Bitcoin may be described as a ‘True Hurst Process’, where crowd behaviour and technical information tend to dominate the leading cryptocurrency’s price development.","PeriodicalId":445453,"journal":{"name":"ERN: Other Econometric Modeling: International Financial Markets - Foreign Exchange (Topic)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124473998","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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