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on Econometric Time Series |
| By: | Tim Bollerslev (Duke University, NBER and CREATES); Andrew J. Patton (Duke University); Rogier Quaedvlieg (Maastricht University) |
| Abstract: | We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models. |
| Keywords: | Common risks; realized covariances; forecasting; asset allocation; portfolio construction |
| JEL: | C32 C58 G11 G32 |
| Date: | 2016–04–05 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2016-10&r=ets |
| By: | Daniel Kosiorowski; Jerzy P. Rydlewski; Ma{\l}gorzata Snarska |
| Abstract: | Functional data analysis (FDA) is a part of modern multivariate statistics that analyses data providing information about curves, surfaces or anything else varying over a certain continuum. In economics and other practical applications we often have to deal with time series of functional data, where we cannot easily decide, whether they are to be considered as stationary or nonstationary. However the definition of nonstationary functional time series is a bit vogue. Quite a fundamental issue is that before we try to statistically model such data, we need to check whether these curves (suitably transformed, if needed) form a stationary functional time series. At present there are no adequate tests of stationarity for such functional data. We propose a novel statistic for detetecting nonstationarity in functional time series based on local Wilcoxon test. |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1604.03776&r=ets |
| By: | Holger Fink; Yulia Klimova; Claudia Czado; Jakob St\"ober |
| Abstract: | For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature, that correlations and higher moments between different indices tend to vary in time. However, to the best of our knowledge, no one has yet considered a global setup including both, equity and implied volatility indices of various continents, and allowing for a changing dependence structure. We aim to close this gap by applying Markov-switching $R$-vine models to investigate the existence of different, global dependence regimes. In particular, we identify times of "normal" and "abnormal" states within a data set consisting of North-American, European and Asian indices. Our results confirm the existence of joint points in time at which global regime switching takes place. |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1604.05598&r=ets |
| By: | Conrad, Christian; Kleen, Onno |
| Abstract: | We examine the statistical properties of multiplicative GARCH models. First, we show that in multiplicative models, returns have higher kurtosis and squared returns have a more persistent autocorrelation function than in the nested GARCH model. Second, we extend the results of Andersen and Bollerslev (1998) on the upper bound of the R2 in a Mincer-Zarnowitz regression to the case of a multiplicative GARCH model, using squared returns as a proxy for the true but unobservable conditional variance. Our theoretical results imply that multiplicative GARCH models provide an explanation for stylized facts that cannot be captured by classical GARCH modeling. |
| Keywords: | Forecast evaluation; GARCH-MIDAS; Mincer-Zarnowitz regression; volatility persistence; volatility component model; long-term volatility. |
| Date: | 2016–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:awi:wpaper:0613&r=ets |
| By: | Davide Pettenuzzo (Brandeis University); Gary Koop (NUniversity of Strathclyde); Dimitris Korobilis (University of Glasgow) |
| Abstract: | Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast better than either factor methods or large VAR methods involving prior shrinkage. |
| Keywords: | multivariate time series, random projection, forecasting |
| JEL: | C11 C32 C53 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:103&r=ets |
| By: | Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Edoardo Otranto |
| Abstract: | Volatility in financial markets alternates persistent turmoil and quiet periods. As a consequence, modelling realized volatility time series requires a specification in which these subperiods are adequately represented. Changes in regimes is a solution, but the question of whether transition between periods is abrupt or smooth remains open. In a recent work we have shown that modifications of the Asymmetric Multiplicative Error Models (AMEM) suitably capture the dynamics of the realized kernel volatility of the S&P500 index: a Markov Switching (MS–AMEM) extension with three regimes performs well in–sample, whereas a Smooth Transition (ST)–AMEM seems to have the best performance out–of–sample. In this paper we combine the two approaches, providing a new class of models with a set of parameters subject to abrupt changes in regime and another set subject to smooth transition changes. These models capture the possibility that regimes may overlap with one another (thus we label them fuzzy). We compare the performance of these models against the MS–AMEM and ST–AMEM, keeping the no–regime AMEM and HAR as benchmarks. The empirical application is carried out on the volatility of four US indices (S&P500, Russell 2000, Dow Jones 30, Nasdaq 100): the superiority of these more flexible models is established on the basis of several criteria and loss functions. |
| Keywords: | Volatility, Regime switching, Smooth transition, Forecasting, Turbulence, Multiplicative Error Models, MEM |
| JEL: | C58 C22 G01 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2016_02&r=ets |
| By: | Helmut Lütkepohl; Anna Staszewska-Bystrova; Peter Winker; |
| Abstract: | This paper proposes a new non-parametric method of constructing joint con- fidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive with the bootstrap-based Bonferroni and Wald confidence regions. The relative tightness of the HDR bands matched with their good coverage properties makes them attractive for applications. An application to corporate bond spreads for Germany highlights the potential for empirical work. |
| Keywords: | Impulse responses, joint confidence bands, highest density region, vector autoregressive process |
| JEL: | C32 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2016-017&r=ets |