IDENTIFYING NONLINEAR SERIAL DEPENDENCE IN VOLATILE, HIGH-FREQUENCY TIME SERIES AND ITS IMPLICATIONS FOR VOLATILITY MODELING

2010 ◽  
Vol 14 (S1) ◽  
pp. 88-110 ◽  
Author(s):  
Phillip Wild ◽  
John Foster ◽  
Melvin J. Hinich
Keyword(s):  
Time Series ◽  
Electricity Market ◽  
High Frequency ◽  
Time Series Data ◽  
New South ◽  
Series Data ◽  
Serial Dependence ◽  
South Wales ◽  
High Volatility ◽  
Wholesale Electricity Market

In this article, we show how tests of nonlinear serial dependence can be applied to high-frequency time series data that exhibit high volatility, strong mean reversion, and leptokurtotis. Portmanteau correlation, bicorrelation, and tricorrelation tests are used to detect nonlinear serial dependence in the data. Trimming is used to control for the presence of outliers in the data. The data that are employed are 161,786 half-hourly spot electricity price observations recorded over nearly a decade in the wholesale electricity market in New South Wales, Australia. Strong evidence of nonlinear serial dependence is found and its implications for time series modeling are discussed.

scholarly journals A New Method to Detect Outliers in High-frequency Time Series

2018 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Ilaria Lucrezia Amerise ◽  
Agostino Tarsitano
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Time Series Data ◽  
New Method ◽  
Series Data ◽  
Absolute Difference ◽  
Data Preparation ◽  
Simple Method ◽  
Pass Through ◽  
Nonparametric Procedure

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

scholarly journals 8. High Frequency Stellar Oscillations: IV: Photoelectric Monitoring of Southern White Dwarfs

1971 ◽  
Vol 42 ◽  
pp. 41-45
Author(s):  
J. E. Hesser ◽  
B. M. Lasker
Keyword(s):  
Time Series ◽  
High Frequency ◽  
White Dwarfs ◽  
Time Series Data ◽  
Power Spectra ◽  
Series Data ◽  
Period Range ◽  
Stellar Oscillations ◽  
Continuous Power

Time-series data for 14 stars in the list of Eggen and Greenstein have been used to compute their power spectra, which confirm previously found quiescency in the 4 to 700 sec period range. Additionally, characteristics of the continuous power spectra are considered.

scholarly journals A non-equilibrium formulation of food security resilience

2017 ◽  
Vol 4 (1) ◽  
pp. 160874 ◽  
Author(s):  
Matteo Smerlak ◽  
Bapu Vaitla
Keyword(s):  
Time Series ◽  
Food Security ◽  
High Frequency ◽  
Time Series Data ◽  
Security Policies ◽  
Series Data ◽  
Poor Countries ◽  
Long Run ◽  
Per Capita ◽  
Non Equilibrium

Resilience, the ability to recover from adverse events, is of fundamental importance to food security. This is especially true in poor countries, where basic needs are frequently threatened by economic, environmental and health shocks. An empirically sound formalization of the concept of food security resilience, however, is lacking. Here, we introduce a general non-equilibrium framework for quantifying resilience based on the statistical notion of persistence. Our approach can be applied to any food security variable for which high-frequency time-series data are available. We illustrate our method with per capita kilocalorie availability for 161 countries between 1961 and 2011. We find that resilient countries are not necessarily those that are characterized by high levels or less volatile fluctuations of kilocalorie intake. Accordingly, food security policies and programmes will need to be tailored not only to welfare levels at any one time, but also to long-run welfare dynamics.

scholarly journals Comparative Angler Catches of Australian Bass (Macquaria novemaculeata) in Three Major River Systems in New South Wales, Australia

2005 ◽  
Vol 18 (2) ◽  
Author(s):  
B. VAN DER WALT ◽  
R. A. FARAGHER ◽  
J. HARRIS
Keyword(s):  
Time Series Data ◽  
New South ◽  
New South Wales ◽  
River System ◽  
Size Composition ◽  
Series Data ◽  
Severe Drought ◽  
Catch Per Unit Effort ◽  
South Wales ◽  
Catch Rates

A joint program between New South Wales Fisheries and three fishing clubs was initiated in 1988 with the aim of collecting standardised catch and effort data during biannual fishing competitions on three major rivers in New South Wales. This paper examines the data to determine trends in the catch of the target species, Australian bass (Macquaria novemaculeata) and to evaluate whether the data can be used to assess Australian bass populations over time. Distinct trends in Australian bass mean length in each river system were evident but catch rates were more variable. Median catch per unit effort was similar (mostly between 0.5 and 1.5 Australian bass·h-1 ) in the Nepean and Williams Rivers although catch rates in the Manning River were nearly always zero. There was an increasing trend in the mean length of Australian bass in all three rivers, possibly representing a recovery in fish populations following severe drought from 1979 to 1983. Low or zero catch rates were continually recorded in the Manning River and size composition data indicated a lack of recruitment through most of the study period. The standardised format of the data collection program provided qualitative and reliable time series data allowing the determination of long-term trends in the population structure of Australian bass which can be used for monitoring and management purposes.

ESTIMATING BIVARIATE GARCH-JUMP MODEL BASED ON HIGH FREQUENCY DATA: THE CASE OF REVALUATION OF THE CHINESE YUAN IN JULY 2005

2010 ◽  
Vol 27 (02) ◽  
pp. 287-300 ◽  
Author(s):  
XINHONG LU ◽  
KEN-ICHI KAWAI ◽  
KOICHI MAEKAWA
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
High Frequency ◽  
Value At Risk ◽  
Time Series Data ◽  
Model Performance ◽  
Series Data ◽  
Jump Model ◽  
Large Jump ◽  
Chinese Yuan

This paper analyzes the behavior of one-minute high-frequency time-series data of exchange rates for five currencies (Japanese Yen, Australian Dollar, Canadian Dollar, Euro, and Pound Sterling) against the US Dollar when the Chinese Yuan was revalued on July 21st, 2005. The data show the following distinctive features: (1) There is a large jump in the exchange rates time series at the time of the Yuan revaluation. (2) Large volatility in the returns of exchange rates is observed for a while after the jump. (3) There are many other jumps, possibly correlated, in each exchange rate time series. To capture these features we fit the following models to the data: (i) a univariate GARCH-Jump model with a large jump that is influential on volatility, and (ii) a bivariate GARCH-Jump model with correlated Poisson jumps. For comparison, we also estimate these GARCH models without the associated jumps. The model performance is evaluated based on Value-at-Risk (VaR).

An Analysis of Coincidence between KSE-100 and S&P 500 Index using Spectral Approach

2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Syed Monis Jawed
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
High Frequency ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Spectral Approach ◽  
In Series ◽  
Data Points

<span>When dealing with time series data, particularly of higher frequency,<br /><span>we are often interested in figuring out periods which are of vital<br /><span>importance. Here in this research, the returns on KSE-100 and S&amp;P<br /><span>500 index are taken on daily basis from September 2001 to June 2013.<br /><span>As thousands of data points (due to high frequency) are considered,<br /><span>it is impossible for us to figure out any pattern in series, unless<br /><span>suitable filtering is applied on them. For this purpose, a power<br /><span>spectrum will be made by means of a fast fourier transform. This will<br /><span>yield us the events that has influenced KSE-100 index considerably<br /><span>in post 9/11 scenario.</span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

scholarly journals PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk)

2018 ◽  
Vol 7 (2) ◽  
pp. 110-118
Author(s):  
Dea Manuella Widodo ◽  
Sudarno Sudarno ◽  
Abdul Hoyyi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Step Function ◽  
Series Data ◽  
Model Data ◽  
Conditional Heteroscedasticity ◽  
Constant Variance ◽  
High Volatility ◽  
Autoregressive Conditional Heteroscedasticity

The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous).  In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH 

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