“What good is a volatility model?” A reexamination after 20 years

2021 ◽  
Vol 21 (2) ◽  
pp. 295-319
Author(s):  
Christopher F. Baum ◽  
Stan Hurn
Keyword(s):  
Time Series ◽  
Original Study ◽  
Replication Study ◽  
Original Sample ◽  
Quantitative Finance ◽  
The Past ◽  
Volatility Model

This article is primarily a replication study of Engle and Patton (2001, Quantitative Finance 1: 237–245), but it also serves as a demonstration of the time-series features introduced into Stata over the past two decades. The dataset used in the original study is extended from the end date of the original sample on 22 August 2000 to 1 August 2017 to examine the robustness of the models.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Searching for g modes

2018 ◽  
Vol 617 ◽  
pp. A108 ◽  
Author(s):  
T. Appourchaux ◽  
P. Boumier ◽  
J. W. Leibacher ◽  
T. Corbard
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Time Shift ◽  
Data Sets ◽  
Mode Amplitude ◽  
The Past ◽  
Mode Detection ◽  
Velocity Calibration ◽  
Remaining Time ◽  
Soho Spacecraft

Context. The recent claims of g-mode detection have restarted the search for these potentially extremely important modes. These claims can be reassessed in view of the different data sets available from the SoHO instruments and ground-based instruments. Aims. We produce a new calibration of the GOLF data with a more consistent p-mode amplitude and a more consistent time shift correction compared to the time series used in the past. Methods. The calibration of 22 yr of GOLF data is done with a simpler approach that uses only the predictive radial velocity of the SoHO spacecraft as a reference. Using p modes, we measure and correct the time shift between ground- and space-based instruments and the GOLF instrument. Results. The p-mode velocity calibration is now consistent to within a few percent with other instruments. The remaining time shifts are within ±5 s for 99.8% of the time series.

Measuring Value Change in Western Industrialized Societies: The Impact of Unemployment

1991 ◽  
Vol 85 (3) ◽  
pp. 905-920 ◽  
Author(s):  
Harold D. Clarke ◽  
Nitish Dutt
Keyword(s):  
Time Series ◽  
Economic Conditions ◽  
Aggregate Level ◽  
Value Change ◽  
Short Term ◽  
Cross Sectional ◽  
The Past ◽  
Value Priorities ◽  
Time Series Analyses ◽  
The Impact

During the past two decades a four-item battery administered in biannual Euro-Barometer surveys has been used to measure changing value priorities in Western European countries. We provide evidence that the measure is seriously flawed. Pooled cross-sectional time series analyses for the 1976–86 period reveal that the Euro-Barometer postmaterialist-materialist value index and two of its components are very sensitive to short-term changes in economic conditions, and that the failure to include a statement about unemployment in the four-item values battery accounts for much of the apparent growth of postmaterialist values in several countries after 1980. The aggregate-level findings are buttressed by analyses of panel data from three countries.

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

scholarly journals A single-station prediction model as a contribution to instantaneous mapping

1994 ◽  
Vol 37 (2) ◽  
Author(s):  
I. Stanislawska
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Method ◽  
Physical Processes ◽  
The Past ◽  
Ionospheric Station ◽  
Single Station ◽  
Daily Forecast

The paper presents two opposite approaches for single-station prediction and forecast. Both methods are based on different assumptions of physical processes in the ionosphere and need the different set of incoming data. Different heliogeophysical data, mainly f0F2 parameters from the past were analyzed for f0F2 obtaining for the requested period ahead. In the first method - the autocovariance prediction method - the time series of f0F2 from one station are used for daily forecast at that point. The second method may be used for obtaining f0F2 not only at the particular ionospheric station, but also at any point within the considered area.

scholarly journals Contribution of the Black Sea observing system to ECOOP

2011 ◽  
Vol 8 (4) ◽  
pp. 1695-1722 ◽  
Author(s):  
G. K. Korotaev ◽  
V. L. Dorofeev ◽  
S. V. Motyzhev ◽  
V. N. Belokopytov ◽  
A. Palazov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Black Sea ◽  
The Black Sea ◽  
Past Century ◽  
Network Integration ◽  
The Past ◽  
Time Operation ◽  
Real Time Operation ◽  
Set Up

Abstract. Regular observations in the Black Sea basin started in the past century, and quite good multidisciplinary observing system operated in the 70–80ies based on the ship observations. Modern oceanographic observing system in the basin is built according to the GOOS principles. It includes space remote sensing observations, data of free floating buoys and costal observational network. Integration of the observing system and its real-time operation were started within the framework of the FP5 ARENA project and later were improved during the FP6 ASCABOS project. The coastal observing system which includes time series from the coastal platforms and multidisciplinary surveys of the coastal areas fulfilled by the research vessels was set up during the ECOOP. Paper describes all components of the Black Sea observing system operated during the ECOOP project and its applications in the framework of the project.

scholarly journals Self-Employment and Labour Market Transitions in New Zealand

2013 ◽  
Author(s):  
Jinyi Shao ◽  
Mallika Kelkar
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Labour Market ◽  
Labour Force ◽  
Labour Force Survey ◽  
The Past ◽  
Self Employment ◽  
Labour Market Transitions ◽  
Household Labour ◽  
Market Transitions

Self-employment in New Zealand has been trending up in the past two years, following subdued growth between 2000 and 2010. Self-employed people made up 11.3% of total employed in the year to March 2012 (251,800 workers), compared with 10.1% in the year to March 2010. Self-employment is defined in this paper as those people operating their own business without employees. The paper explores time series trends in self-employment, in particular across three post-recession periods. Characteristics of self-employed workers are also identified. This paper also investigates movements in and out of self-employment in order to understand the recent growth in this type of employment. The analysis uses longitudinal Household Labour Force Survey (HLFS) data. The HLFS provides official measures of a range of labour market indicators, including the number of people employed, unemployed and not in the labour force.

scholarly journals Trends in Stratospheric Ozone: Lessons Learned from a 3D Chemical Transport Model

2006 ◽  
Vol 63 (3) ◽  
pp. 1028-1041 ◽  
Author(s):  
Richard S. Stolarski ◽  
Anne R. Douglass ◽  
Stephen Steenrod ◽  
Steven Pawson
Keyword(s):  
Time Series ◽  
Solar Cycle ◽  
Ultraviolet Radiation ◽  
Boundary Conditions ◽  
Interannual Variability ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
The Past

Abstract Stratospheric ozone is affected by external factors such as chlorofluorcarbons (CFCs), volcanoes, and the 11-yr solar cycle variation of ultraviolet radiation. Dynamical variability due to the quasi-biennial oscillation and other factors also contribute to stratospheric ozone variability. A research focus during the past two decades has been to quantify the downward trend in ozone due to the increase in industrially produced CFCs. During the coming decades research will focus on detection and attribution of the expected recovery of ozone as the CFCs are slowly removed from the atmosphere. A chemical transport model (CTM) has been used to simulate stratospheric composition for the past 30 yr and the next 20 yr using 50 yr of winds and temperatures from a general circulation model (GCM). The simulation includes the solar cycle in ultraviolet radiation, a representation of aerosol surface areas based on observations including volcanic perturbations from El Chichon in 1982 and Pinatubo in 1991, and time-dependent mixing ratio boundary conditions for CFCs, halons, and other source gases such as N2O and CH4. A second CTM simulation was carried out for identical solar flux and boundary conditions but with constant “background” aerosol conditions. The GCM integration included an online ozonelike tracer with specified production and loss that was used to evaluate the effects of interannual variability in dynamics. Statistical time series analysis was applied to both observed and simulated ozone to examine the capability of the analyses for the determination of trends in ozone due to CFCs and to separate these trends from the solar cycle and volcanic effects in the atmosphere. The results point out several difficulties associated with the interpretation of time series analyses of atmospheric ozone data. In particular, it is shown that lengthening the dataset reduces the uncertainty in derived trend due to interannual dynamic variability. It is further shown that interannual variability can make it difficult to accurately assess the impact of a volcanic eruption, such as Pinatubo, on ozone. Such uncertainties make it difficult to obtain an early proof of ozone recovery in response to decreasing chlorine.

scholarly journals Twenty Years of Time Series Econometrics in Ten Pictures

2017 ◽  
Vol 31 (2) ◽  
pp. 59-86 ◽  
Author(s):  
James H. Stock ◽  
Mark W. Watson
Keyword(s):  
Time Series ◽  
Causal Effects ◽  
Economic Time Series ◽  
Economic Relationships ◽  
Time Series Econometrics ◽  
The Past ◽  
Dynamic Structural Models ◽  
Economic Time ◽  
Dynamic Stochastic ◽  
Improved Methods

This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.

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