The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t-distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.
{"title":"Forecasting the Risk of Cryptocurrencies: Comparison and Combination of GARCH and Stochastic Volatility Models","authors":"Jan Prüser","doi":"10.1515/jtse-2023-0039","DOIUrl":"https://doi.org/10.1515/jtse-2023-0039","url":null,"abstract":"The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding <jats:italic>t</jats:italic>-distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782183","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 develop a hybrid model of multivariate volatility that uses recurrent neural networks to capture the conditional variances of latent orthogonal factors in a GO-GARCH framework. Our approach seeks to balance model flexibility with ease of estimation and can be used to model conditional covariances of a large number of assets. The model performs favourably in comparison with relevant benchmark models in a minimum variance portfolio (MVP) scenario.
{"title":"Recurrent Neural Network GO-GARCH Model for Portfolio Selection","authors":"Martin Burda, Adrian K. Schroeder","doi":"10.1515/jtse-2023-0012","DOIUrl":"https://doi.org/10.1515/jtse-2023-0012","url":null,"abstract":"\u0000 We develop a hybrid model of multivariate volatility that uses recurrent neural networks to capture the conditional variances of latent orthogonal factors in a GO-GARCH framework. Our approach seeks to balance model flexibility with ease of estimation and can be used to model conditional covariances of a large number of assets. The model performs favourably in comparison with relevant benchmark models in a minimum variance portfolio (MVP) scenario.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644173","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 study exploits the non-Gaussianity for identification of a Bayesian SVAR model on newly unexplored monthly Indonesian data from 2007M1–2022M12, where we disentangle the commodity-related revenue from the total government revenues. Our main contribution is in labeling the statistically identified structural shocks as economic shocks by conducting a formal assessment of a set of proposed sign constraints. We simultaneously label a commodity price and three fiscal policy shocks, i.e. fiscal income tax, investment-spending, and consumption-spending shocks. Having evaluated their impacts, among the fiscal policy shocks, we find income tax shock the most impactful on output. Moreover, during the Covid crisis 2020–2021, the launched fiscal economic stimulus package (PEN program) positively contributed to the output. The recession of the Covid crisis could have deepened had the fiscal policymaker not responded at all. Albeit so, we should not overlook the contribution of the rising commodity prices to the output recovery. We also evaluate the commodity boom period in 2007–2009, the tax amnesty program in 2016–2017 and 2022, and the infrastructure spending boost in 2015. Our results suggest that output and retail sales would have been lower without the commodity price shock’s contribution during the commodity boom. Then, we find that tax amnesty and infrastructure spending boost policies contribute to higher retail sales.
{"title":"Commodity Price and Indonesian Fiscal Policy: An SVAR Analysis with Non-Gaussian Errors","authors":"Alfan Mansur","doi":"10.1515/jtse-2023-0037","DOIUrl":"https://doi.org/10.1515/jtse-2023-0037","url":null,"abstract":"This study exploits the non-Gaussianity for identification of a Bayesian SVAR model on newly unexplored monthly Indonesian data from 2007M1–2022M12, where we disentangle the commodity-related revenue from the total government revenues. Our main contribution is in labeling the statistically identified structural shocks as economic shocks by conducting a formal assessment of a set of proposed sign constraints. We simultaneously label a commodity price and three fiscal policy shocks, i.e. fiscal income tax, investment-spending, and consumption-spending shocks. Having evaluated their impacts, among the fiscal policy shocks, we find income tax shock the most impactful on output. Moreover, during the Covid crisis 2020–2021, the launched fiscal economic stimulus package (PEN program) positively contributed to the output. The recession of the Covid crisis could have deepened had the fiscal policymaker not responded at all. Albeit so, we should not overlook the contribution of the rising commodity prices to the output recovery. We also evaluate the commodity boom period in 2007–2009, the tax amnesty program in 2016–2017 and 2022, and the infrastructure spending boost in 2015. Our results suggest that output and retail sales would have been lower without the commodity price shock’s contribution during the commodity boom. Then, we find that tax amnesty and infrastructure spending boost policies contribute to higher retail sales.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567697","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139119031","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139120073","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139124508","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139124550","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139112953","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139113145","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}
Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.
{"title":"Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation","authors":"Yongdeng Xu","doi":"10.1515/jtse-2022-0018","DOIUrl":"https://doi.org/10.1515/jtse-2022-0018","url":null,"abstract":"Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.","PeriodicalId":42470,"journal":{"name":"Journal of Time Series Econometrics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139113316","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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