Monitoring the mean of multivariate financial time series

Garthoff, Robert; Golosnoy, Vasyl; Schmid, Wolfgang

doi:10.1002/asmb.1980

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…The appealing detection characteristics of the standard CUSUM chart for monitoring changes in the mean have been derived by Moustakides (1986Moustakides ( , 2008. Properties of both univariate and multivariate CUSUM control charts for residuals extracted from models similar to our specification are investigated in detail by Ord et al (2009) and Garthoff et al (2013). Following Montgomery (2013), the one-sided CUSUM statistics for detecting upward shifts ( > 0) is defined as a recursion…”

Section: The On-line Monitoring Proceduresmentioning

confidence: 99%

Real-Time Monitoring of the Us Inflation Expectation Process

Golosnoy

Roestel

2018

Macroecon. Dynam.

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Real-time supervision of shifts in inflation expectations is an important issue for monetary policy makers, especially in the presence of economic uncertainty. In this paper, we elaborate tools for on-line monitoring of such shifts by extracting valuable information from noisy daily financial market data. For this purpose, first, we suggest a new risk adjustment for observable proxies of medium and long run inflation expectations assuming that the latter are well-anchored. Second, we propose an econometric methodology for sequential monitoring of level changes in the associated proxies at daily frequency. Our empirical evidence shows that the on-line surveillance of risk adjusted US forward breakeven inflation rates by means of the cumulative sum (CUSUM) detector appears to be helpful to extract timely signals on potential shifts. In particular, the obtained signals indicate important turning points in market-based measures of inflation expectations, which also tend to materialize in lower frequency experts' surveys.

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Section: The On-line Monitoring Proceduresmentioning

confidence: 99%

Real-Time Monitoring of the Us Inflation Expectation Process

Golosnoy

Roestel

2018

Macroecon. Dynam.

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“…Considering these values, T 2 j is computed for each of the historical data. Finding an outlier in Phase I is a possible task in industry but as mentioned by some researches such as Garthoff, Golosnoy [ 23 ], Dumičić and Žmuk [ 24 ] and others, this approach is usually unachievable in financial market as we are not able to terminate the trading process in a market. On the other hand, the control limits entailing Lower Control Limit (LCL) and Upper Control Limit (UCL) of conventional T 2 control charts in the previous researches such as Kang and Albin [ 26 ], Noorossana, Amiri [ 63 ] and Soleimani, Noorossana [ 64 ] were computed by chi-square distribution and it was shown that the Run Length (RL) had a geometric distribution.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…To interpret each signal as bearish or bullish, they provided some rules and conditions based on the experts’ opinions. The 30-year observations of US Dollar (USD), Euro, and Japan Yen were monitored with EWMA and CUSUM control charts in Garthoff, Golosnoy [ 23 ]. They used a GARCH model with consideration of the covariance matrix as a multivariate problem.…”

Section: Introductionmentioning

confidence: 99%

“…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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“…Tracking biological phenomena in natural environment requires monitoring population dynamics of target species 4‐7 . Monitoring financial time series is of importance in business activities 8 . With the monitoring activities, anomalous behavior of the observed processes can be detected and precautionary countermeasures can be taken 9‐11 …”

Section: Introductionmentioning

confidence: 99%

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

Yoshioka

Yaegashi

Tsujimura

et al. 2020

Appl Stoch Models Bus & Ind

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Planning a cost‐efficient monitoring policy of stochastic processes arises from many industrial problems. We formulate a simple discrete‐time monitoring problem of continuous‐time stochastic processes with its applications to several industrial problems. A key in our model is a doubling trick of the variables, with which we can construct an algorithm to solve the problem. The cost‐efficient monitoring policy balancing between the observation cost and information loss is governed by an optimality equation of a fixed point type, which is solvable with an iterative algorithm based on the Feynman‐Kac formula. This is a new linkage between monitoring problems and mathematical sciences. We show regularity results of the optimization problem and present a numerical algorithm for its approximation. A problem having model ambiguity is presented as well. The presented model is applied to problems of environment, ecology, and energy, having qualitatively different target stochastic processes with each other.

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Monitoring the mean of multivariate financial time series

Cited by 14 publications

References 41 publications

Real-Time Monitoring of the Us Inflation Expectation Process

Real-Time Monitoring of the Us Inflation Expectation Process

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

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

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