Simulation of Historical Time Series Using MPRE, a Focus on Correlation in Squared Returns

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

Download Full-text

Market Parameter from Historical Time Series

Derivatives and Internal Models ◽

10.1007/978-3-030-22899-6_31 ◽

2019 ◽

pp. 731-751

Author(s):

Hans-Peter Deutsch ◽

Mark W. Beinker

Keyword(s):

Time Series ◽

Historical Time

Download Full-text

Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

Symmetry ◽

10.3390/sym12060954 ◽

2020 ◽

Vol 12 (6) ◽

pp. 954

Author(s):

Aiwu Zhao ◽

Junhong Gao ◽

Hongjun Guan

Keyword(s):

Time Series ◽

Stock Market ◽

Stock Exchange ◽

Forecasting Model ◽

Historical Time ◽

Logical Relationship ◽

Probabilistic Linguistic Term Set ◽

Time Series Dataset ◽

Proposed Model ◽

Linguistic Term

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

Download Full-text

Systematic worldwide statistical correlation of physical and chemical atmospheric parameters before large earthquakes in the last four decades

10.5194/egusphere-egu2020-19809 ◽

2020 ◽

Author(s):

Dedalo Marchetti ◽

Alessandro Piscini ◽

Angelo De Santis ◽

Caroline Ganglo ◽

Gianfranco Cianchini ◽

...

Keyword(s):

Time Series ◽

Large Earthquake ◽

Surface Concentration ◽

Statistical Correlation ◽

Historical Time ◽

Atmospheric Parameters ◽

Swarm Satellites ◽

Preparation Phase ◽

A Chain ◽

Physical And Chemical

Applying a multi-parametric approach, we already investigated the preparatory phase of several medium and large (M6.0 ~ M8.3) earthquakes occurred in the last 6 years in different locations in the World. In some cases, a chain of processes from the lithosphere to atmosphere and ionosphere has been successfully detected (e.g. M7.8 Ecuador 2016: Akhoondzadeh, 2018, ASR, https://doi.org/10.1016/j.asr.2017.07.014; Italian seismic sequence (M6.5) 2016-2017: Marchetti et al., 2019, RSoE, https://doi.org/10.1016/j.rse.2019.04.033; M7.5 Indonesia 2018: Marchetti et al., 2019, JAES, https://doi.org/10.1016/j.jseaes.2019.104097). These analyses underline the importance to study all the &#8220;spheres&#8221; that surround the Earth as suggested by a Geosystemic approach (De Santis et al., 2019, Entropy, https://doi.org/10.3390/e21040412). To analyse the anomalies that occur in the atmosphere we typically calculate the mean and standard deviation of the &#8220;historical time series&#8221; of the investigated parameter based on around 40 years of data, and then we superpose the value of the same quantity in the earthquake year. If the value overpasses two standard deviations of the historical time series, we define this day/parameter as anomalous. Applying the same methodology presented in previous works that studied climatological parameters such as skin temperature, total column water vapour, aerosols, and SO2, which &#160;seem to provide anomalies possibly related to the earthquake preparation phase (e.g. Piscini et al., 2017, PAGeoph, https://doi.org/10.1007/s00024-017-1597-8), here we investigate more atmospheric parameters proposed as possible precursors in the Lithosphere Atmosphere Ionosphere Coupling (LAIC) models (Pulinets and Ouzounov, 2011, JAES, https://doi.org/10.1016/j.jseaes.2010.03.005) such as methane and surface concentration of carbon monoxide. Other parameters, such as dimethylsulfide could be useful in other geophysical events, such as the volcano eruptions (Piscini et al. PAGeoph 2019, https://doi.org/10.1007/s00024-019-02147-x).In this study, we also apply a Worldwide Statistical Correlation (WSC), as it was successfully applied to Swarm satellites electromagnetic anomalies and earthquakes, providing some statistical evidence for such perturbations in ionosphere before the occurrence of M5.5+ earthquakes (De Santis et al., 2019, Sci. Rep., https://doi.org/10.1038/s41598-019-56599-1).The statistical approaches applied to these climatological data, provided by meteorological agencies such as ECMWF and NOAA, provides some interesting concentrations of atmospheric anomalies, preceding from days to several weeks the occurrence of the largest earthquakes from 1980 to 2017.The study of several chemical and physical (e.g. aerosol particles) components in the atmosphere, the involved physical processes, the chemical reactions and chemical constraints (such as the elements lifetime and interactions in the atmosphere) can help to distinguish which LAIC model is more reliable to produce the observed anomalies before the occurrence of a large earthquake.&#160;

Download Full-text

Stochastic Infinite Horizon Forecasts for US Social Security Finances

National Institute Economic Review ◽

10.1177/0027950105061498 ◽

2005 ◽

Vol 194 ◽

pp. 82-93 ◽

Cited By ~ 3

Author(s):

Ronald Lee ◽

Michael Anderson

Keyword(s):

Time Series ◽

Social Security ◽

Infinite Horizon ◽

Fund Performance ◽

Historical Time ◽

Trust Fund ◽

Probability Bounds ◽

Fund Balance ◽

The Us ◽

Mortality Projections

Even over a 75-year horizon, forecasts of PAYGO pension finances are misleadingly optimistic. Infinite horizon forecasts are necessary, but are they possible? We build on earlier stochastic forecasts of the US Social Security trust fund which model key demographic and economic variables as historical time series, and use the fitted models to generate Monte Carlo simulations of future fund performance. Using a 500-year stochastic projection, effectively infinite with discounting, we find a fund balance of −5.15 per cent of payroll, compared to the −3.5 per cent of the 2004 Trustees‘ Report, probably reflecting different mortality projections. Our 95 per cent probability bounds are −10.5 and −1.3 per cent. Such forecasts, which reflect only ‘routine’ uncertainty, have many problems but nonetheless seem worthwhile.

Download Full-text

THE IMPORTANCE OF THE QUALITY AND RELIABILITY OF THE HISTORICAL TIME SERIES FOR THE STUDY OF CLIMATE CHANGE

Revista Brasileira de Climatologia ◽

10.5380/abclima.v14i1.38168 ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 5

Author(s):

Fiorella Acquaotta ◽

Simona Fratianni

Keyword(s):

Climate Change ◽

Time Series ◽

Historical Time ◽

Quality And Reliability

Download Full-text

Statistical characteristics of surrogate data based on geophysical measurements

Nonlinear Processes in Geophysics ◽

10.5194/npg-13-449-2006 ◽

2006 ◽

Vol 13 (4) ◽

pp. 449-466 ◽

Cited By ~ 37

Author(s):

V. Venema ◽

S. Bachner ◽

H. W. Rust ◽

C. Simmer

Keyword(s):

Time Series ◽

Power Spectrum ◽

Surrogate Data ◽

Structure Functions ◽

Statistical Characteristics ◽

Historical Time ◽

Nonlinear Dynamical ◽

Small Scales ◽

Measured Distribution ◽

Geophysical Measurements

Abstract. In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.

Download Full-text