Real Time Monitoring of the US Inflation Expectation Process

Golosnoy, Vasyl; Roestel, Jan

doi:10.2139/ssrn.2607503

Cited by 4 publications

(3 citation statements)

References 34 publications

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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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Section: The Empirical Applicationmentioning

confidence: 99%

Control charts for measurement error models

et al. 2022

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“…Rębisz 27 applied control charts for country risk monitoring for various countries using the credit ratings. Golosnoy and Roestel 28 used CUSUM control charts for real‐time monitoring of shifts in inflation expectations and specially to forward break‐even inflation (FBI) series.…”

Section: Introductionmentioning

confidence: 99%

Control charts in financial applications: An overview

Bisiotis

Psarakis

Yannacopoulos

2021

Quality & Reliability Eng

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Statistical process control (SPC) methods have been widely popular not only in industrial applications but also in recent years in several nonindustrial scientific areas. The basic tools of SPC are control charts. SPC methods have been applied to various financial applications such as portfolio surveillance, stock trading, and interest rates. The purpose of this paper is to present the main applications of statistical monitoring process methods, specifically control charts, to the financial area. We focus on applications in stock markets such as technical trading rules, portfolio monitoring problems providing some directions for future research.

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“…Since there is considerable demand for having reasonable IE estimates, a sizable literature has focused on analyzing the government's conventional and inflation-indexed bonds, which can implicitly provide a vast amount of information about the expectations of nominal and real interest rates obtained from the market. Such estimates are also known to be an important complement to the IE estimates provided by the survey data (see Nautz et al (2017), Golosnoy and Roestel (2018)). Despite the fact that inflation-indexed bonds have been more frequently and widely issued in recent times, one would still have great difficulties in integrating market information from multiple countries.…”

Section: Introductionmentioning

confidence: 99%