Real-Time Monitoring of the Us Inflation Expectation Process

Golosnoy, Vasyl; Roestel, Jan

doi:10.1017/s1365100517000670

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…To remedy this challenge, they suggested monitoring residuals of the ARMA model instead of the raw (original) values of financial key characteristics. Similar ideas could also be found in Kovářík, Sarga [ 16 ], Sadeghi, Owlia [ 17 ] and Golosnoy and Roestel [ 18 ]. In addition to the autocorrelation effect, control charts in financial time series may suffer from two other undermining factors, i.e., non-normality of observations that causes asymmetric distribution of errors, and data generation delay by which the control chart loses its real performance.…”

Section: Introductionsupporting

confidence: 78%

“…In this area of study, any performance comparisons and signal interpretation were not provided by Szetela [25]. It is expected that the signals from a control chart can play a role, as such a financial technical indicator, is used in forecasting the market's future evolution and direction [42][43][44] while there is no novel research for this aim and also, some researchers such as Hassan, Kumiega [22], Garthoff, Golosnoy [23], Dumičić and Z ˇmuk [24] and Golosnoy and Roestel [18] stated that the signals could not be applied directly as a technical indicator and it is necessary to consult with the experts for a proper decision. To bridge these knowledge gaps, this study aims to monitor the relationship between the price of assets in a portfolio as a profile monitoring approach.…”

Section: Introductionmentioning

confidence: 99%

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A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

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Section: Introductionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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“…Golosnoy and Hogrefe 2013 ) or monitoring changes in the inflation expectation process (cf. Golosnoy and Roestel 2019 ) where—based on publicly available information—the majority of signals could be (although often with some time delay) either interpreted as reasonable ones or classified as outliers. On the contrary, interpreting dates of financial signals is not an easy task, as it would require gathering much more external information which appears to be a costly process.…”

Section: The Empirical Applicationmentioning

confidence: 99%

Control charts for measurement error models

et al. 2022

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We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.

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“…Golosnoy V proposed an econometric method to monitor changes in the level of related agents in a daily frequency sequence. Its empirical evidence shows that online monitoring of riskadjusted U.S. forward break-even inflation rates through the cumulative sum (CUSUM) detector seems to be helpful in extracting signals of potential changes in time [6]. Chrum J employs a structure called modular multi-object NEAT (MM-NEAT) to devise quantitative neural networks.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic Early Warning Method Based on Support Vector Machine under Multi-Sensor Data Fusion Technology

Tian

Shao

Yang

2022

Mobile Information Systems

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This article focuses on the macroeconomic early warning based on support vector machines under multi-sensor data fusion technology. The economic crisis has always been a topic of great concern to the entire world, and there has never been a lack of research and prevention of it in the development process. At present, the economic early warning system that is frequently mentioned includes statistical models and artificial intelligence models. Economic early warning is not just a topic for discussion on a national basis. In the personal field, a thorough enough understanding of economic early warning can also better control investment incomes such as stocks. Analyzing the direction of macroeconomic policies has also become an indispensable ability for investors. In order not to be affected by the economic crisis, early warning is a crucial link. Therefore, in this article, a multi-sensor data fusion macroeconomic early warning model based on support vectors is proposed. In addition, this article also discusses each subpart separately. First, this article mentions the multi-sensor data fusion model. It conducts reasonable collection and control of data information in multiple sensors and uses computers and smart devices to improve system performance and make data clearer. At the same time, this article shows the JDL model of data fusion. It can optimize signals and processes and targets and conduct situation assessment and effect assessment. Then, this article analyzes the support vector machine economic early warning system. It discusses the four parts of linear support vector machine, nonlinear support vector machine, SVC macroeconomic early warning principle, and support vector classification macroeconomic early warning system. By gradually analyzing the macroeconomic early warning process of support vector machines, this study uses the optimal hyperplane to optimize toward the direction of minimizing economic risks. Then, this article talked about the macroeconomic early warning method based on neural network. The difficult points of traditional early warning models can be discarded. It more easily handles the complex algorithms, qualitative indicators, and quantitative indicators of highly nonlinear models, and it demonstrates the macroeconomic early warning system that optimizes the BP neural network with genetic algorithms to solve the various shortcomings of the macroeconomic early warning process. Finally, this study conducts the multi-sensor data fusion macroeconomic early warning model experiment based on support vector machine. It is divided into three parts. In the first part, the model, design system, and application of 60 sets of sensors are compared with traditional weighted least-squares filtering, and it is concluded that the accuracy and trustworthiness prediction effect of this model is better. The second part uses the data of listed companies to conduct experiments, which verify the performance of the model with different data sets. It can be obtained that its prediction effect is better. The third part is to compare the performance with several traditional models, and it is concluded that the convergence effect is good and the error is small. Its average accuracy is 5.63% higher than the average accuracy of the highest precision warning model in the traditional model. This article discusses and concludes that this model has a good future in macroeconomic early warning.

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Real-Time Monitoring of the Us Inflation Expectation Process

Cited by 7 publications

References 52 publications

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Control charts for measurement error models

Macroeconomic Early Warning Method Based on Support Vector Machine under Multi-Sensor Data Fusion Technology

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