Volatility Persistence and Predictability of Squared Returns in GARCH(1,1) Models

Volatility persistence is a stylized statistical property of financial time-series data such as exchange rates and stock returns. The purpose of this letter is to investigate the relationship between volatility persistence and predictability of squared returns.GARCH Models, returns, time series, volatility persistence

A new proxy of the average volatility of a basket of returns: A Monte Carlo study

The volatility of returns plays a pivotal role in modern finance and an accurate evaluation of this parameter is crucial in portfolio and risk management decisions. Until quite recent it was common practice in the literature to use the squared return as proxy of volatility. However, as pointed out by several authors, this measure of volatility includes a large noisy component. In this paper we propose a procedure, based on a generalized dynamic factors model methodology, to obtain a more accurate estimate of volatility of a basket of returns.

Modes of climate variability and their relationships with interhemispheric temperature asymmetry: a Granger causality analysis

AbstractThe aim of this paper is to investigate the relationships among Interhemispheric Temperature Asymmetry (ITA) and the principal modes of natural variability: the Atlantic Multidecadal Oscillation (AMO), the Southern Oscillation Index (SOI), and the Pacific Decadal Oscillation (PDO). In particular, Granger causality tests are used to capture the linkages among these variables. Our analysis provides strong evidence that AMO causes ITA, the causal role of PDO is weak, and SOI seems to have no causal influence

Testing for non-causality by using the Autoregressive Metric

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications.AR metric, Bootstrap test, Granger non-causality, VAR

Evidence of recent causal decoupling between solar radiation and global temperature

The Sun has surely been a major external forcing to the climate system throughout the Holocene. Nevertheless, opposite trends in solar radiation and temperatures have been empirically identified in the last few decades. Here, by means of an inferential method—the Granger causality analysis—we analyze this situation and, for the first time, show that an evident causal decoupling between total solar irradiance and global temperature has appeared since the 1960s

Has natural variability a lagged influence on global temperature? A multi-horizon Granger causality analysis

At present, the role of natural variability in influencing climate behaviour is widely discussed. The generally accepted view is that atmosphere-ocean coupled circulation patterns are able to amplify or reduce temperature increase from interannual to multidecadal time ranges, leaving the principal driving role to anthropogenic forcings. In this framework, the influence of these circulation patterns is considered synchronous with global temperature changes. Here, we would like to investigate if there exists a lagged influence of these indices on temperature. In doing so, an extension of the Granger causality technique, which permits to test both direct and indirect causal influences, is applied. A lagged influence of natural variability is not evident in our analysis, if we except weak influences of some peculiar circulation indices in specific periods

Testing for non-causality by using the Autoregressive Metric

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications

Testing for non-causality by using the Autoregressive Metric

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications

Clarifying the Roles of Greenhouse Gases and ENSO in Recent Global Warming through Their Prediction Performance

Abstract It is well known that natural external forcings and decadal-to-millennial variability drove changes in the climate system throughout the Holocene. Regarding recent times, attribution studies have shown that greenhouse gases (GHGs) determined the trend of temperature (T) in the last half century, while circulation patterns contributed to modify its interannual, decadal, or multidecadal behavior over this period. Here temperature predictions based on vector autoregressive models (VARs) have been used to study the influence of GHGs and El Niño–Southern Oscillation (ENSO) on recent temperature behavior. It is found that in the last decades of steep temperature increase, ENSO shows just a very short-range influence on T, while GHGs are dominant for each forecast horizon. Conversely and quite surprisingly, in the previous quasi-stationary period the influences of GHGs and ENSO are comparable, even at longer range. Therefore, if the recent hiatus in global temperatures should persist into the near future, an enhancement of the role of ENSO can be expected. Finally, the predictive ability of GHGs is more evident in the Southern Hemisphere, where the temperature series is smoother

A Geometrical Characterisation Of Weakly Feedback Free Process

In this paper we provide a characterisation of a weakly feedback-free process using the geometrical concept of splitting subspace. c 1999 Published by Elsevier Science B.V. All rights reserved. Keywords: Feedback; Hilbert spaces; Orthogonal projection; Splitting subspaces; Time series Let H be an arbitrary Hilbert space. For three closed subspaces A; B and M , the orthogonal projection of x is denoted by (x|M); (M |A)=(M |B) means (y|A)=(y|B) #y #M , and we write A#B to denote the vector sum, i.e. the closure of {#+#|# # A; # #B}: De#nition 1. Let H be an arbitrary Hilbert space and H 1 and H 2 be two closed subspaces satisfying H 1 2 =H . V which is a splitting subspace for H 1 and H 2 if and only if (H 1 V)=(H 1 |V ). Now, we consider a bivariate full-rank wide sense stationary process, [y; u] # , de#ned on ( # F; P). We denote by H u = sp{u t ; t =0; the closed subspace of L ( # F; P) spanned by 1; 2;:::}; i.e. the closure with respect to mean-square convergence of the linear manifold generated by the subset of L (# F; P): We will also be interested in the closed subspaces H u (t)=sp{u s ; s6t} and H y (t)=sp{y s ; s6t}: De#nition 2. Let [y; u] # be a bivariate full-rank wide sense stationary process and let the Wold decomposition of the process be y (u t Then the process [y; u] # is weakly feedback free if a 21;i =0;i=1; 2;::: . These de#nitions appear in [1] and, in particular, the concept of splitting subspace has been developed in [2]