Return calculation methodology: Evidence from the Hungarian mutual fund industry

2009 ◽  
Vol 59 (4) ◽  
pp. 391-409 ◽  
Author(s):  
P. Erdős ◽  
M. Ormos
Keyword(s):  
Time Series ◽  
Mutual Fund ◽  
Market Model ◽  
Monthly Return ◽  
Calculation Methodology ◽  
Long Time ◽  
Asymptotic Tests ◽  
Best Fitting ◽  
One Year ◽  
Short Time

In the empirical finance literature most frequently monthly returns are applied for measuring fund performance or testing market efficiency. We propose a new return calculation method, the daily recalculated monthly returns which has not been used in academic studies for asset pricing purposes. We argue that our method outperforms daily and monthly return calculations in the case of Hungarian mutual funds when only short time series are available. Daily recalculated monthly returns induce the best fitting property of the market model while the time series remain sufficiently long to derive asymptotic tests even when we work on a one-year-long time series. Using our method the estimated parameters and the R2 s are very close to the results obtained when using monthly returns which are considered a good working approximation.

scholarly journals Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 385 ◽  
Author(s):  
David Cuesta-Frau ◽  
Juan Pablo Murillo-Escobar ◽  
Diana Alexandra Orrego ◽  
Edilson Delgado-Trejos
Keyword(s):  
Time Series ◽  
Classification Performance ◽  
Permutation Entropy ◽  
Series Length ◽  
Stability Point ◽  
Long Time ◽  
Model Time Series ◽  
The Stability ◽  
Forbidden Patterns ◽  
Short Time

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

Analysing “Long Data” on Collective Violence in Indonesia

2015 ◽  
Vol 43 (5) ◽  
pp. 613-633
Author(s):  
David A. Meyer ◽  
Arthur Stein
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Model Building ◽  
Collective Violence ◽  
Temporal Data ◽  
Data Set ◽  
Time Correlations ◽  
Long Time ◽  
Short Time ◽  
Periodic Components

“Long data”, i.e., temporal data disaggregated to short time intervals to form a long time series, is a particularly interesting type of “big data”. Financial data are often available in this form (e.g., many years of daily stock prices), but until recently long data for other social, and even other economic, processes have been rare. Over the last decade, however, long data have begun to be extracted from (digitized) text, and then used to assess or formulate micro-level and macro-level theories. The UN Support Facility for Indonesian Recovery (UNSFIR) collected a long data set of incidents of collective violence in 14 Indonesian provinces during the 14 year period 1990–2003. In this paper we exploit the “length” of the UNSFIR data by applying several time series analysis methods. These reveal some previously unobserved features of collective violence in Indonesia—including periodic components and long time correlations—with important social/political interpretations and consequences for explanatory model building.

scholarly journals Volume transport and mixing of the Faroe Bank Channel overflow from one year of moored measurements

Ocean Science ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 451-470 ◽  
Author(s):  
Jenny E. Ullgren ◽  
Elin Darelius ◽  
Ilker Fer
Keyword(s):  
Time Series ◽  
Interfacial Layer ◽  
Heat Budget ◽  
Gravity Current ◽  
Bottom Layer ◽  
Volume Transport ◽  
Interfacial Layers ◽  
Iceland Basin ◽  
Long Time ◽  
One Year

Abstract. One-year long time series of current velocity and temperature from eight moorings deployed in the Faroe Bank Channel (FBC) are analysed to describe the structure and variability of the dense overflow plume on daily to seasonal timescales. Mooring arrays were deployed in two sections: located 25 km downstream of the main sill, in the channel that geographically confines the overflow plume at both edges (section C), and 60 km further downstream, over the slope (section S). At section C, the average volume transport of overflow waters ( < 3 °C) from the Nordic Seas towards the Iceland Basin was 1.3 ±  0.3 Sv; at section S, transport of modified overflow water ( < 6 °C) was 1.7  ±  0.7 Sv. The volume transport through the slope section was dominated by mesoscale variability at 3–5-day timescales. A simplified view of along-path entrainment of a gravity current may not be accurate for the FBC overflow. As the plume proceeds into the stratified ambient water, there is substantial detrainment from the deeper layer (bounded by the 3 °C isotherm), of comparable magnitude to the entrainment into the interfacial layer (between the 3 and 6 °C isotherms). A time series of gradient Richardson numbers suggests a quiescent plume core capped by turbulent near bottom and interfacial layers in the channel. At section S, in contrast, the entire overflow plume is turbulent. Based on a two-layer heat budget constructed for the overflow, time mean vertical diffusivities across the top of the bottom layer and across the interfacial layer were (30  ±  15) × 10−4 and (120  ±  43) × 10−4  m2 s−1, respectively.

scholarly journals Embedding reconstruction methodology for short time series – application to large El Niño events

2010 ◽  
Vol 17 (6) ◽  
pp. 753-764 ◽  
Author(s):  
H. F. Astudillo ◽  
F. A. Borotto ◽  
R. Abarca-del-Rio
Keyword(s):  
Time Series ◽  
El Niño ◽  
Time Delays ◽  
El Nino ◽  
Southern Oscillation ◽  
Dimensional Subspace ◽  
Reconstruction Method ◽  
Short Time Series ◽  
Long Time ◽  
Short Time

Abstract. We propose an alternative approach for the embedding space reconstruction method for short time series. An m-dimensional embedding space is reconstructed with a set of time delays including the relevant time scales characterizing the dynamical properties of the system. By using a maximal predictability criterion a d-dimensional subspace is selected with its associated set of time delays, in which a local nonlinear blind forecasting prediction performs the best reconstruction of a particular event of a time series. An locally unfolded d-dimensional embedding space is then obtained. The efficiency of the methodology, which is mathematically consistent with the fundamental definitions of the local nonlinear long time-scale predictability, was tested with a chaotic time series of the Lorenz system. When applied to the Southern Oscillation Index (SOI) (observational data associated with the El Niño-Southern Oscillation phenomena (ENSO)) an optimal set of embedding parameters exists, that allows constructing the main characteristics of the El Niño 1982–1983 and 1997–1998 events, directly from measurements up to 3 to 4 years in advance.

scholarly journals Volume transport and mixing of the Faroe Bank Channel overflow from one year of moored measurements

2015 ◽  
Vol 12 (5) ◽  
pp. 2315-2359 ◽  
Author(s):  
J. E. Ullgren ◽  
E. Darelius ◽  
I. Fer
Keyword(s):  
Time Series ◽  
Interfacial Layer ◽  
Heat Budget ◽  
Gravity Current ◽  
Bottom Layer ◽  
Volume Transport ◽  
Interfacial Layers ◽  
Iceland Basin ◽  
Long Time ◽  
One Year

Abstract. One-year long time series of current velocity and temperature from ten moorings deployed in the Faroe Bank Channel (FBC) are analysed to describe the structure and variability of the dense overflow plume on daily to seasonal time scales. Mooring arrays are deployed in two sections: located 25 km downstream of the main sill, in the channel that geographically confines the overflow plume at both edges (section C), and 60 km further downstream, over the slope (section S). At section C, the average volume transport of overflow waters (< 3 °C) from the Nordic Seas towards the Iceland Basin is 1.3 ± 0.3 Sv; at Section S, transport of modified overflow water (< 6 °C) is 1.8 ± 0.7 Sv. The volume transport through the slope section is dominated by mesoscale variability at 3–5 day time scale. A simplified view of along-path entrainment of a gravity current is not accurate for the FBC overflow. As the plume proceeds into the stratified ambient water, there is substantial detrainment from the deeper layer (bounded by the 3 °C isotherm), of comparable magnitude to the entrainment into the interfacial layer (between the 3 and 6 °C isotherms). Time series of gradient Richardson number suggests a quiescent plume core capped by turbulent near bottom and interfacial layers in the channel. At section S, in contrast, the entire overflow plume is turbulent. Based on a two-layer heat budget constructed for the overflow, mean diffusivities across the top of the bottom layer, and across the interfacial layer are (30 ± 15) × 10−4 m2 s−1 and (119 ± 43) × 10−4 m2 s−1, respectively.

Modelling of overflow emissions in flanders

1998 ◽  
Vol 38 (10) ◽  
pp. 41-48 ◽  
Author(s):  
G. Vaes ◽  
J. Berlamont
Keyword(s):  
Time Series ◽  
Storm Events ◽  
Short Time Series ◽  
Hydrodynamic Simulations ◽  
Sewer Systems ◽  
Linear Behaviour ◽  
Long Time ◽  
Short Time ◽  
Different Characteristics

Ideally, for emission calculations long term hydrodynamic simulations should be performed, but this requires long calculation times. Simplifications are consequently necessary. Due to the non-linear behaviour of sewer systems, hydrodynamic simulations using single storm events often will not lead to a good probability estimation of the overflow emissions. Simplified models using long time simulations give better results if they are well calibrated. To increase the accuracy hydrodynamic simulations with short time series can be used. The short time series are selected from the long time historical rainfall series using a simplified model. To test the accuracy of these three methods, hydrodynamic long term simulations were performed for several (small) sewer systems with different characteristics to compare with.

scholarly journals Design of Management Platform Architecture and Key Algorithm for Massive Monitoring Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wen Liu ◽  
Shanshan Li ◽  
Jiangru Pan ◽  
Lijun Xu ◽  
Zheng Gu ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Acquisition ◽  
Distributed Storage ◽  
Sensor Data ◽  
Management Platform ◽  
Long Time Series ◽  
Industrial Big Data ◽  
Long Time ◽  
One Year

With the construction and development of industrial informatization, industrial big data has become a trend within the smart industry. To obtain valuable information on massive data, achieving the acquisition, storage, analysis, and mining is becoming an important area of research. Focusing on the application requirements for industrial fields, we propose a data acquisition and analysis system based on the NB-IoT for industrial applications. The system is an integrated system that includes sensor data acquisition, data transmission, data storage, and analysis mining. In this study, we mainly focused on the use of the NB-IoT network to collect and transmit real-time data for sensors. First, for the long time series (e.g., if we collect the data streams for one year for the sensor with a frequency of 1 Hz, the length of the series will reach 107). Then, we propose DSCS-LTS, a distributed storage and calculation model, and CCCA-LTS, an algorithm for the correlation coefficient of long time series in a distributed environment. Third, we propose a granularity selection algorithm and query process logic for visualization. We tested the platform in our laboratory and an automated production line for one year, and the experimental results using real data sets show that our approach is effective and scalable, can achieve efficient data management, and provide the basis for intelligent enterprise decision-making.

scholarly journals Market concentration in U.S. milk processors and his influence at price paid to cow milk producers

2015 ◽  
Vol 13 (1) ◽  
pp. 625-632
Author(s):  
Everton Anger Cavalheiro ◽  
Kelmara Mendes Vieira ◽  
Carlos Costa
Keyword(s):  
Opposite Direction ◽  
Market Concentration ◽  
Cow Milk ◽  
Production Cycle ◽  
Industrial Concentration ◽  
Short Term ◽  
International Prices ◽  
Long Time ◽  
One Year ◽  
Short Time

In this paper, we analyzed the influence of the four American biggest milk processors into the price paid to producers from 2002 to 2013. Also, we tried to identify the parameters to explain the change in prices paid to producers. The results suggest a moderate concentration. Besides, the industrial concentration of four biggest firms shows a causal flow on the milk’s national price in short time (one year) and that the causal flow in the opposite direction in two years, evidently due to the milk’s production cycle, i.e., the insertion of new milk plants producing in the production cycle will have an impact after a relatively long time, which explains the short-term inelasticity. In other hand, we can see that the international prices have an important influence to U.S. prices paid to producers, indicating some auction characteristics of this product, too confirmed by the influence of the variation of industrial concentration of the four biggest milk processors in this country.

scholarly journals Geodetic secular velocity errors due to interannual surface loading deformation

2015 ◽  
Vol 202 (2) ◽  
pp. 763-767 ◽  
Author(s):  
Alvaro Santamaría-Gómez ◽  
Anthony Mémin
Keyword(s):  
Time Series ◽  
Water Mass ◽  
Water Model ◽  
Continuous Data ◽  
Surface Loading ◽  
Linear Deformation ◽  
Short Time Series ◽  
Long Time ◽  
Short Time ◽  
The Impact

Abstract Geodetic vertical velocities derived from data as short as 3 yr are often assumed to be representative of linear deformation over past decades to millennia. We use two decades of surface loading deformation predictions due to variations of atmospheric, oceanic and continental water mass to assess the effect on secular velocities estimated from short time-series. The interannual deformation is time-correlated at most locations over the globe, with the level of correlation depending mostly on the chosen continental water model. Using the most conservative loading model and 5-yr-long time-series, we found median vertical velocity errors of 0.5 mm yr−1 over the continents (0.3 mm yr−1 globally), exceeding 1 mm yr−1 in regions around the southern Tropic. Horizontal velocity errors were seven times smaller. Unless an accurate loading model is available, a decade of continuous data is required in these regions to mitigate the impact of the interannual loading deformation on secular velocities.

SCALE INVARIANCE IN SELECTIN-MEDIATED LEUKOCYTE ROLLING

Fractals ◽  
2004 ◽  
Vol 12 (02) ◽  
pp. 235-241 ◽  
Author(s):  
MICHAEL R. KING
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Hurst Exponent ◽  
White Blood Cells ◽  
Spatial Arrangement ◽  
Range Analysis ◽  
Rescaled Range Analysis ◽  
Long Time ◽  
Short Time ◽  
Over Time

White blood cells slowly roll along the walls of blood vessels, due to the coordinated formation and breakage of chemical selectin-carbohydrate bonds. Using detailed computer simulations of cells rolling on a selectin surface under flow, we show the time series of the cell translational velocity to be fractal in nature over time scales ranging from 22–211 ms. A rescaled range analysis was performed to determine the Hurst exponent of the velocity time series, for simulations of cells rolling on either a uniform or punctate distribution of P-selectin molecules. The rolling behavior was found to exhibit two very distinct regimes, with a negative Hurst exponent ranging from -(1.2-0.6) over time scales of 23-27 ms, and a positive Hurst exponent of +0.47±0.03 over time scales of 27-211 ms. The short-time Hurst exponent was found to be a strong function of the molecular distribution and also a function of average molecular density, while the long-time Hurst exponent was unchanged over all conditions studied. The implication is that the short-time adhesive behavior of cells interacting with a reactive surface is sensitive to the spatial arrangement of molecules, and the total number of molecules on the surface.

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