scholarly journals Forecasting Low Frequency Macroeconomic Events with High Frequency Data

2020 ◽  
Author(s):  
Michael T. Owyang ◽  
Ana B. Galvão
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data

scholarly journals Does the high-frequency data is helpful for forecasting Russian inflation?

2021 ◽  
Vol 37 (2) ◽  
pp. 318-343
Author(s):  
Dmitriy Tretyakov ◽  
◽  
Nikita Fokin ◽  
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
High Frequency ◽  
Inflation Targeting ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Policy Regime ◽  
The Future ◽  
Forecasting Inflation

Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation rates became more relevant than ever. In the new monetary policy regime, it is important for the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take measures to return inflation to the target level. In addition, for effective monetary policy, the households must trust the actions of monetary authorities and they must be aware of the future dynamics of inflation. Thus, to manage inflationary expectations of economic agents, the Central Bank should actively use the information channel, publish accurate forecasts of consumer price growth. The aim of this work is to build a model for nowcasting, as well as short-term forecasting of the rate of Russian inflation using high-frequency data. Using this type of data in models for forecasting is very promising, since this approach allows to use more information about the dynamics of macroeconomic indicators. The paper shows that using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several basic models, which only use low-frequency data.

scholarly journals Shake, Rattle, and Roll

2001 ◽  
Vol 123 (11) ◽  
pp. 56-58
Author(s):  
John DeGaspari
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Low Frequencies ◽  
Mems Accelerometer ◽  
Running Shoes ◽  
Low Costs ◽  
New Applications ◽  
Thin Reservoir

This article highlights that airbag has been a boon to MEMS business. Sales of the tiny accelerometers that sense when the bags should deploy have helped to drive down prices significantly since the devices were first implemented. Now, the high volumes, low costs, and dependable performance of micro devices are opening the way to new applications. Sneaker companies are looking at MEMS accelerometers in running shoes to act as speedometers of sorts. Advantage of the MEMS accelerometer is that it has a wide bandwidth, capable of reading high as well as low frequencies. High-frequency data provides information about thin reservoir zones, faults, and changes that are taking place as fluids are being drained from pores in the rock, said Denver. Higher frequency signals are critical to accurate interpretation. Low-frequency signals are useful in identifying the type of rock, be it sandstone, shale, or carbonate, for example. The VectorSeis is as rugged as a conventional geophone and can be successfully deployed in down-hole environments to get a closer reading of a reservoir.

scholarly journals Spatial price transmission analysis in Ghanaian agricultural markets: Does the data frequency improve our estimation?

2011 ◽  
Vol 4 (2) ◽  
pp. 301-316
Author(s):  
Joseph Amikuzuno
Keyword(s):  
Developing Countries ◽  
High Frequency ◽  
Low Frequency ◽  
Price Transmission ◽  
Price Adjustment ◽  
High Frequency Data ◽  
Agricultural Markets ◽  
Frequency Data ◽  
Monthly Data ◽  
Data Frequency

Unavailability of high frequency weekly or daily data compels most studies of price transmission in developing countries to use low frequency monthly data for their analyses. Analysing price dynamics, especially in agricultural markets, with monthly data may however yield imprecise price adjustment parameters and lead to wrong inferences on price dynamics. This is because agricultural markets in developing countries usually operate daily or weekly, not monthly, as implied by the market analysts who use low frequency data. This paper investigates the relevance of data frequency in price transmission analysis by using a standard and a threshold vector error correction model to estimate and compare price adjustment parameters for high frequency semi-weekly data and low frequency monthly data obtained from five major fresh tomato markets in Ghana. The results reveal that adjustment parameters estimated from the low frequency data are higher in all cases than those estimated from the high frequency data. There is reason to suspect that using low frequency data, as confirmed in some literature, leads to an overestimation of the price adjustment parameters. More research involving a large number of observations is however needed to enhance our knowledge about the usefulness of high frequency data in price transmission analysis.

Accurate Drilling Data Interpretations Brought Significant Values to a Major Drilling Project

2021 ◽  
Author(s):  
Faisal Rashid ◽  
Hamdan Mohamed Al Saadi ◽  
Shahid Yakubbhai Duivala ◽  
Steve Butt ◽  
Sultan Al Mansoori ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Frequency ◽  
Performance Enhancement ◽  
Low Frequency ◽  
High Frequency Data ◽  
Coding System ◽  
Frequency Data ◽  
Drilling Data ◽  
The Many ◽  
Drilling Project

Abstract With the launch of a mega drilling project in the Middle East, the drilling data during the execution stage was collected in two formats; Low-Frequency Data and High-Frequency Data. This paper explains the effective utilization of data in the performance enhancement scheme. The paper also demonstrates the combination of Low-frequency and High-frequency data can reveal the many secrets of the drilling operations and can open the many sides of drilling operations for improvements. Low-Frequency data was entered manually at the rig-site using an improved coding system to identify the activities start and end times. High-Frequency data was collected through real-time transmission from the different data streaming services at the rig-site. Both data forms were collected simultaneously using stringent rules and close follow-ups to make sure that data collection was free of any reporting mistakes and gaps. Later, the collected data was extracted for different types of analyses and interpretations. Low-frequency data was studied in a novel way to get the best analytical and critical outcome to make sure that the right areas for improvements were identified and actions were implemented for enhanced performance. Improved operations coding system helped the team to categorize the operations and failures in an effective way to set new standards in data analysis. More than 100 different types of analyses using the best data analysis technique, such as trailing average, normalization, trends, etc., were conducted based on the information collected during the execution phase, and many new KPIs were established with challenging milestones to be achieved in the prescribed period. High-Frequency data was split into different sets of KPIs to identify the multiple Invisible Lost Time (ILT) areas to boost the operational efficiency. Various performance enhancement schemes were developed based on High-frequency data. As a result, these schemes were proven to enhance the performance of the mega drilling project. This paper discusses the novel methods of drilling data analysis based on low and high-frequency data and shows the effectiveness of the data presented in a standardized format over a period. It deliberates how the teams were challenged to enhance the performance. Such detailed data analysis will bring valuable information for the industry to utilize the conventional database in modernized ways to get the best outcomes from the data analysis results.

scholarly journals On the Gains of Using High Frequency Data in Portfolio Selection

2018 ◽  
Vol 65 (4) ◽  
pp. 365-383
Author(s):  
Rui Pedro Brito ◽  
Helder Sebastião ◽  
Pedro Godinho
Keyword(s):  
Relative Risk ◽  
Risk Aversion ◽  
Portfolio Selection ◽  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Relative Risk Aversion ◽  
Frequency Data ◽  
Crra Preferences ◽  
Mean Variance

Abstract This paper analyzes empirically the performance gains of using high frequency data in portfolio selection. Assuming Constant Relative Risk Aversion (CRRA) preferences, with different relative risk aversion levels, we compare low and high frequency portfolios within mean-variance, mean-variance-skewness and mean-variance-skewness-kurtosis frameworks. Using data on fourteen stocks of the Euronext Paris, from January 1999 to December 2005, we conclude that the high frequency portfolios outperform the low frequency portfolios for every out-of-sample measure, irrespectively to the relative risk aversion coefficient considered. The empirical results also suggest that for moderate relative risk aversion the best performance is always achieved through the jointly use of the realized variance, skewness and kurtosis. This claim is reinforced when trading costs are taken into account.

scholarly journals Progressive transfer learning for low-frequency data prediction in full waveform inversion

Geophysics ◽  
2021 ◽  
pp. 1-82
Author(s):  
Wenyi Hu ◽  
Yuchen Jin ◽  
Xuqing Wu ◽  
Jiefu Chen
Keyword(s):  
Transfer Learning ◽  
High Frequency ◽  
Waveform Inversion ◽  
Low Frequency ◽  
High Frequency Data ◽  
Training Dataset ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Velocity Models ◽  
Full Waveform

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. The synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

scholarly journals Beauty and Ugliness of Aggregation over Time: A Survey

2017 ◽  
Vol 68 (3) ◽  
Author(s):  
Nlandu Mamingi
Keyword(s):  
Public Policy ◽  
Time Series ◽  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Economic Time Series ◽  
Negative Effects ◽  
Economic Time ◽  
Over Time

AbstractThis paper delivers an up-to-date literature review dealing with aggregation over time of economic time series, e.g. the transformation of high-frequency data to low frequency data, with a focus on its benefits (the beauty) and its costs (the ugliness). While there are some benefits associated with aggregating data over time, the negative effects are numerous. Aggregation over time is shown to have implications for inferences, public policy and forecasting.

SIZE IDENTIFICATION OF UNDERWATER OBJECTS FROM BACKSCATTERING SIGNALS OF ARBITRARY LOOKING ANGLES

2004 ◽  
Vol 12 (03) ◽  
pp. 301-317 ◽  
Author(s):  
W. LI ◽  
G. R. LIU ◽  
X. M. ZHANG
Keyword(s):  
High Frequency ◽  
Present Method ◽  
Low Frequency ◽  
High Frequency Data ◽  
Physical Optics ◽  
Frequency Data ◽  
Profile Function ◽  
Elastic Bodies ◽  
Underwater Object ◽  
Ramp Response

The inverse problem of determining the size, shape and orientation of a submerged object using the scattered field data is studied. Based on the physical optics approximate, the profile function of the object is found directly proportional to its ramp response that is the second integral of the impulse response. Through analyzing the feature of the ramp response in different computed frequency ranges, it is found that the low-frequency data are essential to the shape of the underwater object while the high-frequency data are very important to the size of the object. Therefore, when employing the high-frequency data to compute the ramp response, the edge of the object can only be highlighted in the illuminated region at certain aspect. Based on this finding, a new method is developed to estimate the size of underwater objects. The present method uses different frequency ranges to determine different parameters of the underwater objects so as to achieve the best accuracy. A number of examples are presented to demonstrate the effectiveness of the present method in using the ramp response technique to identify the size of both rigid and elastic bodies.

A Comparative Study of VAR Measure Using Different Frequency Data of China’s Fuel Oil Futures Market

2011 ◽  
Vol 361-363 ◽  
pp. 1887-1891
Author(s):  
Feng Wang
Keyword(s):  
High Frequency ◽  
Value At Risk ◽  
Low Frequency ◽  
Futures Market ◽  
Fuel Oil ◽  
High Frequency Data ◽  
Frequency Data ◽  
Oil Futures ◽  
Ultra High Frequency ◽  
Fuel Oil Futures

By using datas of Chinese fuel oil futures market, this pater establishes VAR model based on low frequency, high frequency and ultra-high frequency data, to measure the value at risk, and compares the prediction accuracy of different frequency. The research results show that the high frequency and ultra-high frequency data have better accuracy in the VAR measuring, as they contain more intraday information and can reflect the futures market microstructure better.

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