|
on Econometric Time Series |
| By: | Onatski, A.; Wang, C. |
| Abstract: | This paper draws parallels between the Principal Components Analysis of factorless high-dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is collaborated by the standard panel information criteria. Furthermore, the Dickey-Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the non-stationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences. |
| Keywords: | Spurious regression, principal components, factor models, Karhunen-Loève expansion. |
| Date: | 2020–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2003&r=all |
| By: | Jan R. Magnus (Vrije Universiteit Amsterdam); Henk G.J. Pijls (University of Amsterdam); Enrique Sentana (CEMFI) |
| Abstract: | We derive closed-form expressions for the Jacobian of the matrix exponential function for both diagonalizable and defective matrices. The results are applied to two cases of interest in macroeconometrics: a continuous-time macro model and the parametrization of rotation matrices governing impulse response functions in structural vector autoregressions. |
| Keywords: | Matrix differential calculus, Orthogonal matrix, Continuous-time Markov chain, Ornstein-Uhlenbeck process |
| JEL: | C65 C32 C63 |
| Date: | 2020–06–20 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20200035&r=all |
| By: | Linton, O.; Seo, M.; Whang, Y-J. |
| Abstract: | We propose a test of the hypothesis of conditional stochastic dominance in the presence of many conditioning variables (whose dimension may grow to infinity as the sample size diverges). Our approach builds on a semiparametric location scale model in the sense that the conditional distribution of the outcome given the covariates is characterized by a nonparametric mean function and a nonparametric skedastic function with an independent innovation whose distribution is unknown. We propose to estimate the nonparametric mean and skedastic regression functions by the `1-penalized nonparametric series estimation with thresholding. Under the sparsity assumption, where the number of truly relevant series terms are relatively small (but their identities are unknown), we develop the estimation error bounds for the regression functions and series coefficients estimates allowing for the time series dependence. We derive the asymptotic distribution of the test statistic, which is not pivotal asymptotically, and introduce the smooth stationary bootstrap to approximate its sample distribution. We investigate the finite sample performance of the bootstrap critical values by a set of Monte Carlo simulations. Finally, our method is illustrated by an application to stochastic dominance among portfolio returns given all the past information. |
| Keywords: | Bootstrap, Empirical process, Home bias, LASSO, Power boosting, Sparsity |
| JEL: | C10 C12 C15 C15 |
| Date: | 2020–01–14 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2004&r=all |
| By: | Christoph Berninger; Almond St\"ocker; David R\"ugamer |
| Abstract: | Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs Sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short term predictions with long term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to the one of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.05750&r=all |
| By: | Heni Boubaker (International University of Rabat, BEAR LAB, Technopolis Rabat-Shore Rocade Rabat-Sale, Morocco); Giorgio Canarella (Department of Economics, Lee Business School, University of Nevada, Las Vegas; Las Vegas, Nevada); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Stephen M. Miller (Department of Economics, Lee Business School, University of Nevada, Las Vegas; Las Vegas, Nevada) |
| Abstract: | This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation (BP) and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily closing prices for the Dow Jones Industrial Average (DJIA) index over 01/01/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days. |
| Keywords: | Wavelet decomposition, WLLWNN, Neural network, ARFIMA, HYGARCH |
| JEL: | C45 C58 G17 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202056&r=all |
| By: | Elie Bouri (Holy Spirit University of Kaslik (USEK), USEK Business School, Jounieh, Lebanon); David Gabauer (Software Competence Center Hagenberg, Data Analysis Systems, Softwarepark 21, 4232 Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Aviral Kumar Tiwari (Rajagiri Business School, Rajagiri Valley Campus, Kochi, India) |
| Abstract: | In this paper, we first obtain a time-varying measure of volatility connectedness involving fifteen major cryptocurrencies based on a dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, and then analyze the role of investor sentiment in explaining the movement of the connectedness metric within a quantile-on-quantile framework. Our findings show that lower quantiles of investor happiness, built on Twitter feed data as a proxy for investor sentiment, is positively associated with the entire conditional distribution of connectedness, but the opposite is observed at higher values of investor happiness. In addition, when we look at the effect of sentiment on the common market volatility, we are able to deduce that as investors become exceedingly unhappy, overall market volatility increases and this is associated with high market connectedness. The heightened volatility possibly due to higher trading, seems to suggest that cryptocurrencies are used for hedging when investor sentiment is weak, with evidence in favor of this behavior being relatively stronger than the possible speculative motive associated with happy investors, as low total connectedness is coupled with high common volatility. Our results tend to suggest that, relatively more diversification opportunities are available when investors are happy rather than when sentiment is weak. |
| Keywords: | Cryptocurrency Market, DCC-GARCH, Volatility Connectedness, Investor Happiness, Quantile-on-Quantile Regression |
| JEL: | C22 C32 G10 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202059&r=all |
| By: | Semei Coronado (Independent Consultant. 16366 Avenida Venusto Unit C, San Diego, CA, 92128, U.S.); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Besma Hkiri (Department of Finance and Economics, College of Business, University of Jeddah, Jeddah, Saudi Arabia); Omar Rojas (Universidad Panamericana, Facultad de Ciencias Económicas y Empresariales, Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México) |
| Abstract: | In this paper, we analyze time-varying causality between the dollar-pound exchange rate and S&P 500 returns over the monthly period of September, 1791 to September, 2019. Based on a Dynamic Conditional Correlation-Multivariate Generalised Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) framework, we find that evidence of unidirectional causality between the two returns is in general weak, and primarily restricted to the period following the breakdown of the Bretton Woods agreement. However, instantaneous spillover across the returns of these two markets is quite strong, which in turn tends to suggest the existence of nonsynchronous trading and also high-frequency causal dependency, with the latter confirmed based on daily data covering January 3rd, 1900 to October 4th, 2019. Moreover, the underlying DCC reveals that there is actually portfolio diversification opportunities for investors. Finally, an analysis of the second moments reveal much stronger evidence of volatility spillover between these two assets, when compared to the return linkages. This result has important implications from the perspective of policy making aiming to reduce the impact of uncertainty on the real economy. |
| Keywords: | Time-varying Granger causality, currency and equity markets, returns and volatilities |
| JEL: | C32 F31 F31 G10 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202060&r=all |
| By: | Guglielmo Maria Caporale; Luis A. Gil-Alana; Laura Sauci |
| Abstract: | This paper analyses US sea level data using long memory and fractional integration methods. All series appear to exhibit orders of integration in the range (0, 1), which implies long-range dependence; further, significant positive time trends are found in the case of 29 stations located on the East Coast and the Gulf of Mexico, and negative ones in the case 4 stations on the North West Coast, but none for the remaining 8 on the West Coast. The highest degree of persistence is found for the West Coast and the lowest for the East Coast. |
| Keywords: | sea level, time trends, fractional integration |
| JEL: | C21 Q54 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8274&r=all |
| By: | Francis X. Diebold (University of Pennsylvania) |
| Abstract: | We study the real-time signals provided by the Aruoba-Diebold-Scotti Index of Business conditions (ADS) for tracking economic activity at high frequency. We start with exit from the Great Recession, comparing the evolution of real-time vintage beliefs to a “?nal” late-vintage chronology. We then consider entry into the Pandemic Recession, again tracking the evolution of real-time vintage beliefs. ADS swings widely as its underlying economic indicators swing widely, but the emerging ADS path as of this writing (late June) indicates a return to growth in May. The trajectory of the nascent recovery, however, is highly uncertain – particularly as COVID-19 spreads in the South and West – and could be revised or eliminated as new data arrive. |
| Keywords: | Aruboba-Dieold-Scotti index, ADS index, nowcasting, business cycle, recession, expansion, coincident indicator, real economic activity, forecasting, Big Data |
| JEL: | E32 E66 |
| Date: | 2020–06–26 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:20-023&r=all |