Abstract In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE) programs. We provide the following contributions to the literature: (i) for a robust measurement of policy effects, a new multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model is used. (ii) In addition to the measurement of the effects of QE, the effects of government conservatorship are also measured in this paper. (iii) Furthermore, the data period of the relevant literature is extended to the period of June 1998 to March 2020.
{"title":"Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions","authors":"Szabolcs Blazsek, V. Blazsek, Adam Kobor","doi":"10.1515/snde-2021-0066","DOIUrl":"https://doi.org/10.1515/snde-2021-0066","url":null,"abstract":"Abstract In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE) programs. We provide the following contributions to the literature: (i) for a robust measurement of policy effects, a new multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model is used. (ii) In addition to the measurement of the effects of QE, the effects of government conservatorship are also measured in this paper. (iii) Furthermore, the data period of the relevant literature is extended to the period of June 1998 to March 2020.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"27 1","pages":"237 - 264"},"PeriodicalIF":0.8,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44812136","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 Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation with non-Gaussian perturbations in the observation equation, and the estimation of parameters is performed by maximizing the likelihood written in terms derived from a Kalman filter algorithm. We also present a procedure to calculate volatility and Value-at-Risks forecasts. The proposal is evaluated by means of Monte Carlo experiments and applied to real-life time series, where an illustration of market risk calculation is presented.
{"title":"Estimation and forecasting of long memory stochastic volatility models","authors":"Omar Abbara, M. Zevallos","doi":"10.1515/snde-2020-0106","DOIUrl":"https://doi.org/10.1515/snde-2020-0106","url":null,"abstract":"Abstract Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation with non-Gaussian perturbations in the observation equation, and the estimation of parameters is performed by maximizing the likelihood written in terms derived from a Kalman filter algorithm. We also present a procedure to calculate volatility and Value-at-Risks forecasts. The proposal is evaluated by means of Monte Carlo experiments and applied to real-life time series, where an illustration of market risk calculation is presented.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"27 1","pages":"1 - 24"},"PeriodicalIF":0.8,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49294547","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 widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.
{"title":"A Gini estimator for regression with autocorrelated errors","authors":"Ndéné Ka, Stéphane Mussard","doi":"10.1515/snde-2020-0134","DOIUrl":"https://doi.org/10.1515/snde-2020-0134","url":null,"abstract":"Abstract The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"27 1","pages":"83 - 95"},"PeriodicalIF":0.8,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48294702","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 present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.
{"title":"Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility","authors":"Szabolcs Blazsek, A. Escribano, Adrián Licht","doi":"10.1515/snde-2021-0083","DOIUrl":"https://doi.org/10.1515/snde-2021-0083","url":null,"abstract":"Abstract We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46649325","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 Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %.
{"title":"Sequential Monte Carlo with model tempering","authors":"Marko Mlikota, F. Schorfheide","doi":"10.1515/snde-2022-0103","DOIUrl":"https://doi.org/10.1515/snde-2022-0103","url":null,"abstract":"Abstract Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45917085","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}
D. Živkov, Jelena Kovacevic, Biljana Stankov, Zoran Stefanović
Abstract This paper investigates the volatility spillover effect between the national stock and bond markets in the five East Asian emerging countries. We use wavelet signal decomposing technique, GARCH models with different distribution functions and quantile regression. We find that the spillover effect is much higher in more turbulent times, than in calm periods, whereby this effect is stronger from stocks to 10Y bonds, than vice-versa, and it applies for all the countries. Using wavelet signals, we determine that, in most cases, the volatility transmission is higher in short-term horizon, than in midterm and long-term. The effect is stronger in countries with the less developed financial markets (Thailand, Indonesia and Malaysia) than in countries with more developed financial markets (China and Korea), and this is particularly evident in direction from stock to bond markets. Wavelet coherence indicates low volatility correlation in short time-horizons and relatively high correlation in midterm and long-term, which applies for all selected countries. Wavelet cross-correlation indicates that volatility spillover shocks predominantly transmit from bond markets to stock market in more developed China and Korea, whereas volatility shocks from stock market spill over towards bond market in less developed Thailand and Indonesia in very short-time horizon (2–4 days).
{"title":"Bidirectional volatility transmission between stocks and bond in East Asia – The quantile estimates based on wavelets","authors":"D. Živkov, Jelena Kovacevic, Biljana Stankov, Zoran Stefanović","doi":"10.1515/snde-2020-0113","DOIUrl":"https://doi.org/10.1515/snde-2020-0113","url":null,"abstract":"Abstract This paper investigates the volatility spillover effect between the national stock and bond markets in the five East Asian emerging countries. We use wavelet signal decomposing technique, GARCH models with different distribution functions and quantile regression. We find that the spillover effect is much higher in more turbulent times, than in calm periods, whereby this effect is stronger from stocks to 10Y bonds, than vice-versa, and it applies for all the countries. Using wavelet signals, we determine that, in most cases, the volatility transmission is higher in short-term horizon, than in midterm and long-term. The effect is stronger in countries with the less developed financial markets (Thailand, Indonesia and Malaysia) than in countries with more developed financial markets (China and Korea), and this is particularly evident in direction from stock to bond markets. Wavelet coherence indicates low volatility correlation in short time-horizons and relatively high correlation in midterm and long-term, which applies for all selected countries. Wavelet cross-correlation indicates that volatility spillover shocks predominantly transmit from bond markets to stock market in more developed China and Korea, whereas volatility shocks from stock market spill over towards bond market in less developed Thailand and Indonesia in very short-time horizon (2–4 days).","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"27 1","pages":"49 - 65"},"PeriodicalIF":0.8,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48003739","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 study aggregate uncertainty and its linear and nonlinear impact on real and financial markets. By distinguishing between four general notions of aggregate uncertainty (good-expected, bad-expected, good-unexpected, bad-unexpected) within a simple, common framework, we show that it is bad-unexpected uncertainty shocks that generate a negative reaction of economic variables (such as investment and consumption) and asset prices. Our results help to elucidate the real, complex nature of uncertainty, which can be both a backward- or forward-looking expected or unexpected event, with markedly different consequences for the economy. We also document nonlinearities in the propagation of uncertainty to both real and financial markets, which calls for the close monitoring of the evolution of uncertainty so as to help mitigate the adverse effects of its occurrence.
{"title":"Expected, unexpected, good and bad aggregate uncertainty","authors":"Jorge M. Uribe, Helena Chuliá","doi":"10.1515/snde-2020-0127","DOIUrl":"https://doi.org/10.1515/snde-2020-0127","url":null,"abstract":"Abstract We study aggregate uncertainty and its linear and nonlinear impact on real and financial markets. By distinguishing between four general notions of aggregate uncertainty (good-expected, bad-expected, good-unexpected, bad-unexpected) within a simple, common framework, we show that it is bad-unexpected uncertainty shocks that generate a negative reaction of economic variables (such as investment and consumption) and asset prices. Our results help to elucidate the real, complex nature of uncertainty, which can be both a backward- or forward-looking expected or unexpected event, with markedly different consequences for the economy. We also document nonlinearities in the propagation of uncertainty to both real and financial markets, which calls for the close monitoring of the evolution of uncertainty so as to help mitigate the adverse effects of its occurrence.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"27 1","pages":"265 - 284"},"PeriodicalIF":0.8,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45371031","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 : 2022-02-01DOI: 10.1515/snde-2022-frontmatter1
{"title":"Frontmatter","authors":"","doi":"10.1515/snde-2022-frontmatter1","DOIUrl":"https://doi.org/10.1515/snde-2022-frontmatter1","url":null,"abstract":"","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"6 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81279949","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 : 2021-12-01DOI: 10.1515/snde-2021-frontmatter5
{"title":"Frontmatter","authors":"","doi":"10.1515/snde-2021-frontmatter5","DOIUrl":"https://doi.org/10.1515/snde-2021-frontmatter5","url":null,"abstract":"","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46976898","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 presents extensions to the family of nonparametric fractional variance ratio (FVR) unit root tests of Nielsen (2009. “A Powerful Test of the Autoregressive Unit Root Hypothesis Based on a Tuning Parameter Free Statistic.” Econometric Theory 25: 1515–44) under heavy tailed (infinite variance) innovations. In this regard, we first develop the asymptotic theory for these FVR tests under this setup. We show that the limiting distributions of the tests are free of serial correlation nuisance parameters, but depend on the tail index of the infinite variance process. Then, we compare the finite sample size and power performance of our FVR unit root tests with the well-known parametric ADF test under the impact of the heavy tailed shocks. Simulations demonstrate that under heavy tailed innovations, the nonparametric FVR tests have desirable size and power properties.
{"title":"A family of nonparametric unit root tests for processes driven by infinite variance innovations","authors":"K. C. Gogebakan","doi":"10.1515/snde-2021-0058","DOIUrl":"https://doi.org/10.1515/snde-2021-0058","url":null,"abstract":"Abstract This paper presents extensions to the family of nonparametric fractional variance ratio (FVR) unit root tests of Nielsen (2009. “A Powerful Test of the Autoregressive Unit Root Hypothesis Based on a Tuning Parameter Free Statistic.” Econometric Theory 25: 1515–44) under heavy tailed (infinite variance) innovations. In this regard, we first develop the asymptotic theory for these FVR tests under this setup. We show that the limiting distributions of the tests are free of serial correlation nuisance parameters, but depend on the tail index of the infinite variance process. Then, we compare the finite sample size and power performance of our FVR unit root tests with the well-known parametric ADF test under the impact of the heavy tailed shocks. Simulations demonstrate that under heavy tailed innovations, the nonparametric FVR tests have desirable size and power properties.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"26 1","pages":"705 - 721"},"PeriodicalIF":0.8,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48350510","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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