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on Econometric Time Series |
| By: | Huang, Wenxin (Shanghai Jiao Tong University); Jin, Sainan (School of Economics, Singapore Management University); Phillips, Peter C.B. (Yale University); Su, Liangjun (School of Economics, Singapore Management University) |
| Abstract: | This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an asymptotically non-negligible bias from correlations between the nonstationary regressors and unobserved stationary common factors and/or the equation errors. To remedy these shortcomings we provide three bias-correction procedures under which the estimators are re-centered about zero as both dimensions (N and T) of the panel tend to infinity. We establish a mixed normal limit theory for the estimators of the group-specific long-run coefficients, which permits inference using standard test statistics. Simulations suggest the good finite sample performance of the proposed method. An empirical application applies the methodology to study international R&D spillovers and the results offer a convincing explanation for the growth convergence puzzle through the heterogeneous impact of R&D spillovers. |
| Keywords: | Nonstationarity; Parameter heterogeneity; Latent group patterns; Penalized principal component; Cross-section dependence; Classifier Lasso; R&D spillovers |
| JEL: | C13 C33 C38 C51 F43 O32 O40 |
| Date: | 2020–03–24 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2020_007&r=all |
| By: | Yuan, Huiling; Zhou, Yong; Zhang, Zhiyuan; Cui, Xiangyu |
| Abstract: | Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-It\^{o}-OI model, we assume that the option-implied volatility can influence the security's future volatility, and the option-implied volatility is treated as an observable exogenous variable. In GARCH-It\^{o}-IV model, we assume that the option-implied volatility can not influence the security's volatility directly, and the relationship between the option-implied volatility and the security's volatility is constructed to extract useful information of the underlying security. After providing the quasi-maximum likelihood estimators for the parameters and establishing their asymptotic properties, we also conduct a series of simulation analysis and empirical analysis to compare the proposed models with other popular models in the literature. We find that when the sampling interval of the high-frequency data is 5 minutes, the GARCH-It\^{o}-OI model and GARCH-It\^{o}-IV model has better forecasting performance than other models. |
| Date: | 2020–03–27 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:vdsqf&r=all |
| By: | Irena Barja\v{s}i\'c; Nino Antulov-Fantulin |
| Abstract: | In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the problem, but none without shortcomings. We combine an approach that uses historical values of returns and their volatilities - GARCH family of models, with a so-called "Mixture of Distribution Hypothesis", which states that the dynamics of price returns are governed by the information flow about the market. Using time-series of Bitcoin-related tweets and volume of transactions as external information, we test for improvement in volatility prediction of several GARCH model variants on a minute level Bitcoin price time series. Statistical tests show that the simplest GARCH(1,1) reacts the best to the addition of external signal to model volatility process on out-of-sample data. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.00550&r=all |
| By: | Natalia Bailey; George Kapetanios; M. Hashem Pesaran |
| Abstract: | This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The proposed estimator is based on the number of statistically significant factor loadings, taking account of the multiple testing problem. We focus on the case where the factors are observed which is of primary interest in many applications in macroeconomics and finance. We also consider using cross section averages as a proxy in the case of unobserved common factors. We face a fundamental factor identification issue when there are more than one unobserved common factors. We investigate the small sample properties of the proposed estimator by means of Monte Carlo experiments under a variety of scenarios. In general, we find that the estimator, and the associated inference, perform well. The test is conservative under the null hypothesis, but, nevertheless, has excellent power properties, especially when the factor strength is sufficiently high. Application of the proposed estimation strategy to factor models of asset returns shows that out of 146 factors recently considered in the finance literature, only the market factor is truly strong, while all other factors are at best semi-strong, with their strength varying considerably over time. Similarly, we only find evidence of semi-strong factors in an updated version of the Stock and Watson (2012) macroeconomic dataset. |
| Keywords: | factor models, factor strength, measures of pervasiveness, cross-sectional dependence, market factor |
| JEL: | C38 E20 G20 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8146&r=all |
| By: | Yuan, Huiling; Zhou, Yong; Xu, Lu; Sun, Yulei; Cui, Xiangyu |
| Abstract: | Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature. |
| Date: | 2020–03–27 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:hkzdr&r=all |
| By: | Eraslan, Sercan; Nöller, Marvin |
| Abstract: | We follow the idea of exploiting cross-sectional information to improve recession probability forecasts by aggregating indicator-specific turning point predictions to obtain economy-wide recession probabilities. This stands in contrast to most of the relevant literature, which relies on an aggregated economic indicator to identify business cycle turning points. Using smooth transition regressions we compare the forecast performance of both approaches to business cycle dating in a comprehensive real-time forecasting exercise for recessions in the US. Moreover, we propose a novel smooth transition modelling framework which makes use of the interrelation between business and growth cycles to forecast recession probabilities. Our real-time out-of-sample forecast evaluation reveals that (i) using cross-sectional information is benficial to predicting recession probabilities, (ii) aggregating indicator-specific turning point forecasts clearly outperforms turning point predictions based on a single indicator and (iii) the proposed smooth transition framework is able to provide informative recession probability forecasts for up to three months in the US. |
| Keywords: | Business cycles,forecasting,recessions,STAR models,turning points |
| JEL: | C24 C53 E37 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:082020&r=all |
| By: | Arianna Agosto (Università di Pavia); Paolo Giudici (Università di Pavia) |
| Abstract: | We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19. The model is a Poisson autoregression, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are presented from the observed series of China, Iran, Italy and South Korea. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0185&r=all |
| By: | Wolf, Elias; Mokinski, Frieder; Schüler, Yves |
| Abstract: | We show that one should not use the one-sided Hodrick-Prescott filter (HP-1s) as the real-time version of the two-sided Hodrick-Prescott filter (HP-2s): First, in terms of the extracted cyclical component, HP-1s fails to remove low-frequency fluctuations to the same extent as HP-2s. Second, HP-1s dampens fluctuations at all frequencies - even those it is meant to extract. As a remedy, we propose two small adjustments to HP-1s, aligning its properties closely with HP-2s: (1) a lower value for the smoothing parameter and (2) a multiplicative rescaling of the extracted cyclical component. For example, for HP-2s with = 1,600 (value of smoothing parameter), the adjusted one-sided HP filter uses = 650 and rescales the extracted cyclical component by a factor of 1:1513. Using simulated and empirical data, we illustrate the relevance of the adjustments. For instance, financial cycles may appear 1.7 times more volatile than business cycles, where in fact volatilities differ only marginally. |
| Keywords: | Real-time analysis,detrending,business cycles,financial cycles |
| JEL: | C10 E32 E58 G01 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:112020&r=all |