Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.
{"title":"Causal relationships between cryptocurrencies: the effects of sampling interval and sample size","authors":"Nezire Köse, E. Ünal","doi":"10.1515/snde-2022-0054","DOIUrl":"https://doi.org/10.1515/snde-2022-0054","url":null,"abstract":"Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48803301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper introduces a panel threshold model with covariate-dependent and time-varying thresholds (PTCT), which extends the classical panel threshold model of Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 to a framework with multiple covariate-dependent and time-varying thresholds. Based on the within-group transformation and Markov chain Monte Carlo (MCMC) technique, we develop methods for estimation and inference for threshold parameters in the proposed panel threshold model. We also suggest test statistics for threshold effect, threshold constancy, and for determining the number of thresholds. Monte Carlo simulations indicate that the estimation, inference and testing procedures work well in finite samples. Empirically, using the same data as in Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 we revisit the cash flow/investment relationship and find quite different results.
{"title":"Panel threshold model with covariate-dependent thresholds and its application to the cash flow/investment relationship","authors":"Lixiong Yang","doi":"10.1515/snde-2022-0035","DOIUrl":"https://doi.org/10.1515/snde-2022-0035","url":null,"abstract":"Abstract This paper introduces a panel threshold model with covariate-dependent and time-varying thresholds (PTCT), which extends the classical panel threshold model of Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 to a framework with multiple covariate-dependent and time-varying thresholds. Based on the within-group transformation and Markov chain Monte Carlo (MCMC) technique, we develop methods for estimation and inference for threshold parameters in the proposed panel threshold model. We also suggest test statistics for threshold effect, threshold constancy, and for determining the number of thresholds. Monte Carlo simulations indicate that the estimation, inference and testing procedures work well in finite samples. Empirically, using the same data as in Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 we revisit the cash flow/investment relationship and find quite different results.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48945052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study investigates and uses multi-kernel Hawkes models to describe a high-frequency mid-price process. Each kernel represents a different responsive speed of market participants. Using the conditional Hessian, we examine whether the numerical optimizer effectively finds the global maximum of the log-likelihood function under complicated modeling. Empirical studies that use stock prices in the US equity market show the existence of multi-kernels classified as ultra-high-frequency (UHF), very-high-frequency (VHF), and high-frequency (HF). We estimate the conditional expectations of arrival times and the degree of contribution to the high-frequency activities for each kernel.
{"title":"Multi-kernel property in high-frequency price dynamics under Hawkes model","authors":"Kyungsub Lee","doi":"10.1515/snde-2022-0049","DOIUrl":"https://doi.org/10.1515/snde-2022-0049","url":null,"abstract":"Abstract This study investigates and uses multi-kernel Hawkes models to describe a high-frequency mid-price process. Each kernel represents a different responsive speed of market participants. Using the conditional Hessian, we examine whether the numerical optimizer effectively finds the global maximum of the log-likelihood function under complicated modeling. Empirical studies that use stock prices in the US equity market show the existence of multi-kernels classified as ultra-high-frequency (UHF), very-high-frequency (VHF), and high-frequency (HF). We estimate the conditional expectations of arrival times and the degree of contribution to the high-frequency activities for each kernel.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43869964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-01DOI: 10.1515/snde-2023-frontmatter1
{"title":"Frontmatter","authors":"","doi":"10.1515/snde-2023-frontmatter1","DOIUrl":"https://doi.org/10.1515/snde-2023-frontmatter1","url":null,"abstract":"","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134942338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin, and the lower tail dependence between Ethereum and Litecoin returns is the strongest. The GARCH-in-Mean model shows that the uncertainty effect on cryptocurrency returns is not statistically significant, while uncertainty has a negative and statistically significant effect on Bitcoin returns. The fact that there is no tail dependence between cryptocurrency and the interest rate or the effective exchange rate of U.S. dollar suggests that cryptocurrency could offer safe haven, defined as an asset that is uncorrelated with stocks and bonds.
摘要我们使用均值copula模型中的半参数GARCH来检验加密货币市场和金融市场之间的波动动力学和尾部依赖性。我们没有发现金融和加密货币市场之间有任何统计上显著的尾部依赖性，但我们发现比特币和股票回报之间的尾部依赖度较低。比特币、以太坊和莱特币之间存在较低的尾部依赖性，以太坊和比特币回报之间的较低尾部依赖性最强。GARCH in Mean模型表明，不确定性对加密货币回报的影响在统计上并不显著，而不确定性对比特币回报有负面和统计显著的影响。加密货币与美元利率或有效汇率之间不存在尾部依赖性，这一事实表明，加密货币可以提供避风港，被定义为与股票和债券无关的资产。
{"title":"Volatility and dependence in cryptocurrency and financial markets: a copula approach","authors":"Jinan Liu, Apostolos Serletis","doi":"10.1515/snde-2022-0029","DOIUrl":"https://doi.org/10.1515/snde-2022-0029","url":null,"abstract":"Abstract We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin, and the lower tail dependence between Ethereum and Litecoin returns is the strongest. The GARCH-in-Mean model shows that the uncertainty effect on cryptocurrency returns is not statistically significant, while uncertainty has a negative and statistically significant effect on Bitcoin returns. The fact that there is no tail dependence between cryptocurrency and the interest rate or the effective exchange rate of U.S. dollar suggests that cryptocurrency could offer safe haven, defined as an asset that is uncorrelated with stocks and bonds.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46891933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this paper, we develop a new version of Rao’s score (RS) statistic for testing a non-linear hypothesis under both distributional and local parametric misspecification. Our suggested test statistic is constructed through a size correction approach so that it becomes robust to both types of misspecification. We establish the asymptotic properties of the robust test statistic and provide several examples to illustrate its implementation. We also investigate the finite sample properties of our test along with some other well-known tests through simulations. Our simulation results demonstrate that the new test statistic has good finite sample properties in terms of empirical size and power.
{"title":"A new test for non-linear hypotheses under distributional and local parametric misspecification","authors":"A. Bera, Osman Doğan, Suleyman Taspinar","doi":"10.1515/snde-2022-0043","DOIUrl":"https://doi.org/10.1515/snde-2022-0043","url":null,"abstract":"Abstract In this paper, we develop a new version of Rao’s score (RS) statistic for testing a non-linear hypothesis under both distributional and local parametric misspecification. Our suggested test statistic is constructed through a size correction approach so that it becomes robust to both types of misspecification. We establish the asymptotic properties of the robust test statistic and provide several examples to illustrate its implementation. We also investigate the finite sample properties of our test along with some other well-known tests through simulations. Our simulation results demonstrate that the new test statistic has good finite sample properties in terms of empirical size and power.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47735299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In the context that the tails of security returns obey an asymmetric power-law distribution, this paper constructs two fractal statistical measures based on fractal theory: fractal expectation and fractal variance. Subsequently, a new momentum strategy is constructed by introducing the fractal measures into the momentum strategy as measures of returns and risks to optimize the selection criterion. Finally, the empirical results show that the new momentum strategy outperforms the traditional momentum strategy and the risk-adjusted momentum strategy, confirming the effectiveness of fractal expectation and fractal variance.
{"title":"Optimization study of momentum investment strategies under asymmetric power-law distribution of return rate","authors":"Xuan Wu, Kun Wang, Linlin Zhang, Chong Peng","doi":"10.1515/snde-2022-0020","DOIUrl":"https://doi.org/10.1515/snde-2022-0020","url":null,"abstract":"Abstract In the context that the tails of security returns obey an asymmetric power-law distribution, this paper constructs two fractal statistical measures based on fractal theory: fractal expectation and fractal variance. Subsequently, a new momentum strategy is constructed by introducing the fractal measures into the momentum strategy as measures of returns and risks to optimize the selection criterion. Finally, the empirical results show that the new momentum strategy outperforms the traditional momentum strategy and the risk-adjusted momentum strategy, confirming the effectiveness of fractal expectation and fractal variance.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48702323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper investigates macroeconomic uncertainty spillover effects across countries and their impact on real economic activity in different economic periods, i.e. pre-crisis and during the recent financial crisis. The analysis is initially carried out using Monte Carlo simulations and, subsequently, real data for four euro zone economies, namely Italy, France, Germany, and Spain. The Monte Carlo findings clearly indicate a need to account for spillover effects across countries when investigating the impact of aggregate uncertainty on economic variables. The empirical results provide clear-cut evidence of the existence of macroeconomic spillovers between the four euro countries, with some feedback from periphery economies, notably Italy, to the core economies during the financial crisis period. Further, the impact of uncertainty on real economic activity is dampened for the four euro countries when spillover effects are accounted for. Spillover effects among the four countries are also observed when US uncertainty is taken into account. Further, US macroeconomic uncertainty impacts negatively on the real economic activity of the four euro countries.
{"title":"Estimating uncertainty spillover effects across euro area using a regime dependent VAR model","authors":"Giovanni Angelini, Mauro Costantini, J. Easaw","doi":"10.1515/snde-2021-0107","DOIUrl":"https://doi.org/10.1515/snde-2021-0107","url":null,"abstract":"Abstract This paper investigates macroeconomic uncertainty spillover effects across countries and their impact on real economic activity in different economic periods, i.e. pre-crisis and during the recent financial crisis. The analysis is initially carried out using Monte Carlo simulations and, subsequently, real data for four euro zone economies, namely Italy, France, Germany, and Spain. The Monte Carlo findings clearly indicate a need to account for spillover effects across countries when investigating the impact of aggregate uncertainty on economic variables. The empirical results provide clear-cut evidence of the existence of macroeconomic spillovers between the four euro countries, with some feedback from periphery economies, notably Italy, to the core economies during the financial crisis period. Further, the impact of uncertainty on real economic activity is dampened for the four euro countries when spillover effects are accounted for. Spillover effects among the four countries are also observed when US uncertainty is taken into account. Further, US macroeconomic uncertainty impacts negatively on the real economic activity of the four euro countries.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49640734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and the real example show that the proposed method works well in identifying the order and estimating the parameters of the DT-AR model simultaneously.
{"title":"Bayesian inference for order determination of double threshold variables autoregressive models","authors":"Xiaobing Zheng, Qiang Xia, Rubing Liang","doi":"10.1515/snde-2020-0096","DOIUrl":"https://doi.org/10.1515/snde-2020-0096","url":null,"abstract":"Abstract The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and the real example show that the proposed method works well in identifying the order and estimating the parameters of the DT-AR model simultaneously.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49163135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor’s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.
{"title":"Anticipating extreme losses using score-driven shape filters","authors":"A. Ayala, Szabolcs Blazsek, A. Escribano","doi":"10.1515/snde-2021-0102","DOIUrl":"https://doi.org/10.1515/snde-2021-0102","url":null,"abstract":"Abstract We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor’s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"0 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42201680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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