scholarly journals MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION

2015 ◽  
Vol 32 (4) ◽  
pp. 793-826 ◽  
Author(s):  
Brian D.O. Anderson ◽  
Manfred Deistler ◽  
Elisabeth Felsenstein ◽  
Bernd Funovits ◽  
Lukas Koelbl ◽  
...  
Keyword(s):  
High Frequency ◽  
Time Series Data ◽  
Series Data ◽  
Frequency Data ◽  
Linear Transformations ◽  
Mixed Frequency ◽  
Second Moments ◽  
Flow Variables ◽  
Parameter Values ◽  
Mixed Frequency Data

This paper is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments of the observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the high frequency parameters to these population second moments. These structural results are obtained using two alternative tools viz. extended Yule Walker equations and blocking of the output process. The cases of stock and flow variables, as well as of general linear transformations of high frequency data, are treated. Finally, we briefly discuss how our constructive identifiability results can be used for parameter estimation based on the sample second moments.

scholarly journals 6.2. MACROECONOMIC AND MARKET FACTORS FOR PREDICTING REAL ESTATE PRICES IN THE EURO ZONE ON MIXED FREQUENCY DATA

2020 ◽  
Author(s):  
А.А. Виноградов
Keyword(s):  
Real Estate ◽  
Euro Area ◽  
High Frequency ◽  
Real Estate Market ◽  
Frequency Data ◽  
The Real Estate ◽  
Mixed Frequency ◽  
The Real Estate Market ◽  
Real Estate Prices ◽  
Mixed Frequency Data

В данной работе исследуется динамика цен на недвижимость в зоне евро. Особенностями рынка недвижимости в зоне евро является разнородность стран, высокие объемы ипотечного рынка. Недвижимость является относительно неликвидным активом, а оценки ее стоимости публикуются реже, чем другие показатели. Актуальность работы заключается в построении модели для цены на недвижимость в зоне евро, которая позволяет построить прогноз и справедливую оценку для динамики цены на недвижимость. Новизной данной статьи является использование модель для данных смешанной частоты (MIDAS), которая позволяет совмещать высокочастотные рыночные показатели и низкочастотные данные по цене недвижимости для прогнозирования цен на жилую и коммерческую недвижимость. Среди факторов, влияющих на рынок недвижимости, были выделены ставки, отражающие состояние денежно-кредитной политики Европейского центрального банка (ЕЦБ) и объем активов ЕЦБ, отражающий меры нестандартной денежно-кредитной политики. В результате на основе высокочастотных данных была построена модель для цен на недвижимость, которая дает более точный прогноз, чем линейная модель, основанная только на квартальном росте валового внутреннего продукта зоны евро. Полученная модель может быть использована как для принятия управленческих решений, исходя из прогноза динамики цен на недвижимость, так и оценки справедливой динамики цен на недвижимость в зоне евро на основе фундаментальных факторов. This paper examines the dynamics of real estate prices in the euro area. The features of the real estate market in the euro area is the heterogeneity of countries, high volumes of the mortgage market. Real estate is a relatively illiquid asset, and estimates of its value are published less frequently than other indicators. The relevance of the work is to build a model for real estate prices in the euro area, which allows one to build a forecast and a fair assessment for the price dynamics of real estate. The novelty of this article is the use of the mixed frequency data sampling model (MIDAS), which allows one to combine high-frequency market indicators and low-frequency data on the price of real estate, to predict the prices of residential and commercial real estate. Among the factors affecting the real estate market, the rates that reflect the state of the ECB's monetary policy and the volume of the ECB's assets reflecting the measures of a non-standard monetary policy were identified. As a result, based on high-frequency data, a model for real estate prices was built, which gives a more accurate forecast than a linear model based on only quarterly GDP growth in the euro area. The resulting model can be used both for making managerial decisions based on the forecast of real estate price dynamics, and for assessing the fair dynamics of real estate prices in the euro area based on fundamental factors.

scholarly journals Recent developments in volatility modeling and applications

2006 ◽  
Vol 2006 ◽  
pp. 1-23 ◽  
Author(s):  
A. Thavaneswaran ◽  
S. S. Appadoo ◽  
C. R. Bector
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Time Series Data ◽  
Financial Time Series ◽  
Empirical Studies ◽  
High Frequency Data ◽  
Series Data ◽  
Frequency Data ◽  
Arma Process ◽  
Volatility Clustering

In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail.

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.

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