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on Econometric Time Series |
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Issue of 2020‒10‒26
eleven papers chosen by Jaqueson K. Galimberti Auckland University of Technology |
| By: | Luidas Giraitis (Queen Mary University of London); George Kapetanios (King's College London); Massimiliano Marcellino (Bocconi University) |
| Abstract: | We develop non-parametric instrumental variable estimation and inferential theory for econometric models with possibly endogenous regressors whose coefficients can vary over time either deterministically or stochastically, and the time-varying and uniform versions of the standard Hausman exogeneity test. After deriving the asymptotic properties of the proposed procedures, we assess their finite sample performance by means of a set of Monte Carlo experiments, and illustrate their application by means of an empirical example on the Phillips curve. |
| Keywords: | Instrumental variables, Time-varying parameters, endogeneity, Hausman test, Non-parametric methods, Phillips curve. |
| JEL: | C14 C26 C51 |
| Date: | 2020–08–17 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:911&r=all |
| By: | Dimitrakopoulos, Stefanos; Tsionas, Mike G.; Aknouche, Abdelhakim |
| Abstract: | We propose a new model for transaction data that accounts jointly for the time duration between transactions and for the discreteness of the intraday stock price changes. Duration is assumed to follow a stochastic conditional duration model, while price discreteness is captured by an autoregressive moving average ordinal-response model with stochastic volatility and time-varying parameters. The proposed model also allows for endogeneity of the trade durations as well as for leverage and in-mean effects. In a purely Bayesian framework we conduct a forecasting exercise using multiple high-frequency transaction data sets and show that the proposed model produces better point and density forecasts than competing models. |
| Keywords: | Ordinal-response models, irregularly spaced data, stochastic conditional duration, time varying ARMA-SV model, Bayesian MCMC, model confidence set. |
| JEL: | C1 C11 C15 C4 C41 C5 C51 C53 C58 |
| Date: | 2020–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103250&r=all |
| By: | Jesus Otero (Universidad del Rosario); Theodore Panagiotidis (Department of Economics, University of Macedonia); Georgios Papapanagiotou (Department of Economics, University of Macedonia) |
| Abstract: | We perform Monte Carlo simulations to study the effect of increasing the frequency of observations and data span on the Johansen (1988, 1995) maximum likelihood cointegration testing approach, as well as on the bootstrap and wild bootstrap implementations of the method developed by Cavaliere et al. (2012, 2014). Considering systems with three and four variables, we find that when both the data span and the frequency vary, the power of the tests depend more on the sample length. We illustrate our findings by investigating the existence of long-run equilibrium relationships among four indicators prices of coffee. |
| Keywords: | Monte Carlo, Span, Power, Cointegration, Coffee prices. |
| JEL: | C13 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:mcd:mcddps:2020_05&r=all |
| By: | Yeguang Chi; Wenyan Hao |
| Abstract: | We test various volatility models using the Bitcoin spot price series. Our models include HIST, EMA ARCH, GARCH, and EGARCH, models. Both of our in-sample-fit and out-of-sample-forecast results suggest that GARCH and EGARCH models perform much better than other models. Moreover, the EGARCH model's asymmetric term is positive and insignificant, which suggests that Bitcoin prices lack the asymmetric volatility response to past returns. Finally, we formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option's implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.07402&r=all |
| By: | Yayi Yan; Jiti Gao; Bin peng |
| Abstract: | Multivariate time series analyses are widely encountered in practical studies, e.g., modelling policy transmission mechanism and measuring connectedness between economic agents. To better capture the dynamics, this paper proposes a class of multivariate dynamic models with time-varying coefficients, which have a general time-varying vector moving average (VMA) representation, and nest, for instance, time-varying vector autoregression (VAR), time–varying vector autoregression moving–average (VARMA), and so forth as special cases. The paper then develops a unified estimation method for the unknown quantities before an asymptotic theory for the proposed estimators is established. In the empirical study, we investigate the transmission mechanism of monetary policy using U.S. data, and uncover a fall in the volatilities of exogenous shocks. In addition, we find that (i) monetary policy shocks have less influence on inflation before and during the so-called Great Moderation, (ii) inflation is more anchored recently, and (iii) the long-run level of inflation is below, but quite close to the Federal Reserve’s target of two percent after the beginning of the Great Moderation period. |
| Keywords: | multivariate time series model, nonparametric kernel estimation, trending stationarity |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-39&r=all |
| By: | Bernd Funovits |
| Abstract: | This comment points out a serious flaw in the article "Gouri\'eroux, Monfort, Renne (2019): Identification and Estimation in Non-Fundamental Structural VARMA Models" with regard to mirroring complex-valued roots with Blaschke polynomial matrices. Moreover, the (non-) feasibility of the proposed method (if the handling of Blaschke transformation were not prohibitive) for cross-sectional dimensions greater than two and vector moving average (VMA) polynomial matrices of degree greater than one is discussed. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.02711&r=all |
| By: | Haroon Mumtaz (Queen Mary University of London) |
| Abstract: | In this note we present an updated algorithm to estimate the VAR with stochastic volatility proposed in Mumtaz (2018). The model is re-written so that some of the Metropolis Hastings steps are avoided. |
| Keywords: | VAR, Stochastic volatility in mean, error covariance |
| JEL: | C3 C11 E3 |
| Date: | 2020–07–05 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:908&r=all |
| By: | Cem Cakmakli (Koc University, Istanbul, Turkey); Hamza Demircan (Central Bank of the Republic of Turkey, Istanbul, Turkey) |
| Abstract: | We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of US real GDP. Specifically, we use the conventional dynamic factor model together with a stochastic volatility component as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of ‘ambiguity’, conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution. |
| Keywords: | Dynamic factor model; Stochastic volatility; Survey of Professional Forecasters; Disagreement; Predictive density evaluation; Bayesian inference. |
| JEL: | C32 C38 C53 E32 E37 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:koc:wpaper:2016&r=all |
| By: | Michele Leonardo Bianchi; Giovanni De Luca; Giorgia Rivieccio |
| Abstract: | In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR$^{\leq}$ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR$^{=}$ based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.10764&r=all |
| By: | Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr |
| Abstract: | We propose a ranking-based variable selection (RBVS) technique that identifies important variables influencing the response in high-dimensional data. RBVS uses subsampling to identify the covariates that appear nonspuriously at the top of a chosen variable ranking. We study the conditions under which such a set is unique, and show that it can be recovered successfully from the data by our procedure. Unlike many existing high-dimensional variable selection techniques, among all relevant variables, RBVS distinguishes between important and unimportant variables, and aims to recover only the important ones. Moreover, RBVS does not require model restrictions on the relationship between the response and the covariates, and, thus, is widely applicable in both parametric and nonparametric contexts. Lastly, we illustrate the good practical performance of the proposed technique by means of a comparative simulation study. The RBVS algorithm is implemented in rbvs, a publicly available R package. |
| Keywords: | variable screening; subset selection; bootstrap; stability selection. |
| JEL: | C1 |
| Date: | 2020–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:90233&r=all |
| By: | Eduardo Abi Jaber |
| Abstract: | Stochastic volatility models based on Gaussian processes, like fractional Brownian motion, are able to reproduce important stylized facts of financial markets such as rich autocorrelation structures, persistence and roughness of sample paths. This is made possible by virtue of the flexibility introduced in the choice of the covariance function of the Gaussian process. The price to pay is that, in general, such models are no longer Markovian nor semimartingales, which limits their practical use. We derive, in two different ways, an explicit analytic expression for the joint characteristic function of the log-price and its integrated variance in general Gaussian stochastic volatility models. Such analytic expression can be approximated by closed form matrix expressions stemming from Wishart distributions. This opens the door to fast approximation of the joint density and pricing of derivatives on both the stock and its realized variance using Fourier inversion techniques. In the context of rough volatility modeling, our results apply to the (rough) fractional Stein-Stein model and provide the first analytic formulae for option pricing known to date, generalizing that of Stein-Stein, Sch{\"o}bel-Zhu and a special case of Heston. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.10972&r=all |