Browsing by Author "Bock, David"

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Aspects on the controi of false alarms in statistical surveillance and the impact on the return of financial decision systems
(2004-02-01) Bock, David
Systems for on-line detection of regime shifts are important, e.g. for making timely financial transactions. For daily data, it means that we make a new decision each day, based on the data available, and when there is enough evidence of a regime shift, an alarm is called. There is always the risk of a false alarm and here two principally different ways of controlling the false alarms are compared: systems with a fixed average run length until the first false alarm, and systems with a fixed probability «1) of any false alarm (fixed size). The effects of the two approaches are evaluated in terms of the timeliness of alarms. A system with a fixed size is found to have a drawback: the ability to detect a change deteriorates rapidly with the time of the change. Consequently, the probability of successful detection will tend to zero and the expected delay of a motivated alarm tends to infinity. This drawback is present even when the size is set to be very large (close to 1). Utility measures are used in the investigation, expressing the different costs for the gain of a motivated alarm and the loss of a false alarm. Drawbacks and advantages of the two approaches are investigated. How the choice of the best approach can be guided by the parameters of the process and the relation between the cost of a too earlyor too late alarm is demonstrated. The technique is illustrated by application to transactions of the Hang Seng Index.
Consequences of using the probability of a false alarm as the false alarm measure
(2007-11-26T14:02:43Z) Bock, David
In systems for on-line detection of regime shifts, a process is continually observed. Based on the data available an alarm is given when there is enough evidence of a change. There is a risk of a false alarm and here two different ways of controlling the false alarms are compared: a fixed average run length until the first false alarm and a fixed probability of any false alarm (fixed size). The two approaches are evaluated in terms of the timeliness of alarms. A system with a fixed size is found to have a drawback: the ability to detect a change deteriorates with the time of the change. Consequently, the probability of successful detection will tend to zero and the expected delay of a motivated alarm tends to infinity. This drawback is present even when the size is set to be very large (close to 1). Utility measures expressing the costs for a false or a too late alarm are used in the comparison. How the choice of the best approach can be guided by the parameters of the process and the different costs of alarms is demonstrated. The technique is illustrated by financial transactions of the Hang Seng Index.
Detection of turning points in business cycles
(University of Gothenburg, 2002-06-01) Andersson, Eva; Bock, David; Frisén, Marianne
Methods for on-line monitoring of business cycles are compared with respect to the ability of early prediction of the next turn by an alarm for a turn in a leading index. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is based on a Hidden Markov Model. Another includes a non-parametric estimation procedure. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time to a correct alarm and the predictive value of an alarm are discussed. The three methods are also used to analyze an actual data set of a period of the Swedish industrial production. The relative merits of evaluation of methods by one real data set or by simulations are discussed.
Early warnings for turns in business cycles and finance
(University of Gothenburg, 2003-06-01) Bock, David
In many areas, it is important to detect turning points in time series early and without faults. Turns in business cycles and financial time series are discussed here. A variety of approaches for analyzing the turns in cyclical processes has been proposed. Some of the proposed techniques aim at point prediction of all values of the process. These techniques often give very low accuracy near turns. Other approaches concentrate on predicting the time of the turn. In this thesis we consider prospective monitoring of a stochastic process in order to call an alarm as soon as we have enough evidence that the critical event of interest has occurred. Statistical surveillance deals with the theory and methodology of online detection of an important change in the underlying process of a time series as soon as possible after it has occurred. The theory of statistical surveillance is used in this thesis to construct and compare methods for two important applications. In the first paper, three likelihood-based methods for detection of a tum are compared. The problem is being addressed in a business cycle context. We aim at timely detecting a turning point in a leading indicator of the business cycle. By detecting the turns in a leading indicator we have an instrument for predicting the turns in the business cycle. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve shape. Evaluations are made of e.g. the effects of different specifications of the curve and of the transition probabilities. The second paper investigates inferential differences and similarities between statistical surveillance and some prospective decision rules suggested for trading in financial markets. It is found that the proposed rules can be seen as special cases of classical methods of surveillance and can hence be discussed in the context of optimality properties of surveillance methods. Evaluation measures and utility functions commonly used in statistical surveillance are compared with those generally used in financial settings. Some of the methods are evaluated by case studies. The relative merits of case studies and Monte Carlo studies are discussed.
Evaluations of likelihood based surveillance of volatility
(2007-12-13T11:54:03Z) Bock, David
The volatility of asset returns are important in finance. Different likelihood based methods of statistical surveillance for detecting a change in the variance are evaluated. The differences are how the partial likelihood ratios are weighted. The full likelihood ratio, Shiryaev-Roberts, Shewhart and the CUSUM methods are derived in case of an independent and identically distributed Gaussian process. The behavior of the methods is studied both when there is no change and when the change occurs at different time points. The false alarms are controlled by the median run length. Differences and limiting equalities of the methods are shown. The performances when the process parameters for which the methods are optimized for differ from the true values of the parameters are evaluated. The methods are illustrated on a period of Standard and Poor’s 500 stock market index.
Explorative analysis of spatial aspects on the Swedish influenza data
(2007-12-20T13:50:55Z) Bock, David; Pettersson, Kjell
The spatial aspects on the Swedish influenza data are analyzed. During the influenza period, reports on laboratory diagnosed cases and influenza-like-illness are obtained from viral and microbiological laboratories and from sentinel physicians, respectively, in different regions of Sweden. Information about the spatio-temporal patterns might give insight in the way the influenza spreads over Sweden. It might also be used in automated surveillance systems for outbreak and peak detection of the influenza. We describe the regional patterns in Swedish influenza data in different ways. Several natural hypotheses about geographical patterns are examined but can not be verified as consistent over the years. However, we find that, for a group of large cities, the outbreak of the influenza occurs at least four weeks earlier than for the rest of Sweden. The possibilities to utilize this in surveillance systems are briefly discussed.
Likelihood based methods for detection of turning points in business cycles - A comparative study
(University of Gothenburg, 2001-05-01) Andersson, Eva; Bock, David; Frisén, Marianne
Methods for on-line monitoring of business cycles are compared with respect to the ability of early prediction of the next tum by an alarm for a tum in a leading index. Three likelihood-based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is based on a Hidden Markov Model. Another includes a non-parametric estimation procedure. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time and the predictive value of an alarm. The three methods are also used to analyze an actual data set of a period of the Swedish industrial production. The relative merits of evaluation of methods by one real data set or by simulations are discussed.
Modeling influenza incidence for the purpose of on-line monitoring
(2007-11-27T12:08:00Z) Andersson, Eva; Bock, David; Frisén, Marianne
We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain,), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
Monitoring macroeconomic volatility
(2004-01-01) Bock, David; van Dijk, Dick; Franses, Philip Hans
In this paper we develop testing procedures for monitoring the stability of the variance of a time series. While the traditional approach to testing for structural change is retrospective, applying a single test to a historical time series of given length, we consider testing stability in a prospective framework, where the time series are observed online and monitored continuously. The proposed testing procedures have controlled asymptotic size, in that the probability of a false alarm during an infinitely long monitoring period is fixed. A Monte Carlo study is performed to evaluate the test statistics with respect to size and power under different circumstances. We apply our methods to US GDP and its major components in order to investigate when the documented decline in volatility of the US economy during the latter part of the twentieth century could have been detected in real time.
On seasonal filters and monotonicity
(University of Gothenburg, 2001-04-01) Andersson, Eva; Bock, David
Seasonal adjustment is important in for example economic time series where the variation can be due to both seasonal and cyclical movements. In a situation where we want to detect a turning point of a cyclical process exhibiting seasonal variation, it is very important that the seasonal adjustment does not adversely affect the ability to detect the turning points. Thus, it is important that the seasonal adjustment does not alter the monotonicity. In this report, seasonal adjustment using differentiation and moving average methods is analyzed with respect to the effect on turning points.
Prediction models for planning health care resources. During the first wave of the Covid-19 pandemic 2020
(University of Gothenburg, 2022) Grimby-Ekman, Anna; Cronie, Ottmar; Bock, David; Palaszewski, Bo; Norman Kjellström, Anna; School of Public Health and Community Medicine, Institute of Medicine
Due to the Covid-19 pandemic, has emphasized a need for planning health care resources based on only a few aggregated data points and little knowledge of the data-generating process. In the first part of the report, we present the process of our work in spring 2020 during the first wave and especially during the first part with the high demands on health care resources. In the second part of the report, we discuss the logistic growth model (LGM), one of our models used to predict the peak height and the peak timing. We present some different approaches to use the LGM, and compare these to a different data set, Belgium data. For the Swedish regional data, the LGM on raw observations gave a good estimate on the peak height. The adjusted LGM, using cumulative new inpatient beds, fitted the Swedish regional data to a satisfying degree. For the Belgium data, the LGM on raw observations gave a good estimate on peak height and timing. The adjusted LGM, using cumulative new inpatient beds, did not work for the Belgium data as it gave a too early peak time and a too low peak height. The experience from our work, in combination with now existing literature, the process in a similar future situation would include better knowledge on how to find and combine data to get as reliable forecasts as possible and to use creativity in combination with theoretical competence.
Similarities and differences between statistical surveillance and certain decision rules in finance
(2007-12-13T11:42:37Z) Bock, David; Andersson, Eva; Frisén, Marianne
Financial trading rules have the aim of continuously evaluating available information in order to make timely decisions. This is also the aim of methods for statistical surveillance. Many results are available regarding the properties of surveillance methods. We give a review of financial trading rules and use the theory of statistical surveillance to find properties of some commonly used trading rules. In addition, a nonparametric and robust surveillance method is proposed as a trading rule. Evaluation measures used in statistical surveillance are compared with those used in finance. The Hang Seng Index is used for illustration.
Similarities and differences between statistical surveillance and certain decision rules in finance
(University of Gothenburg, 2003-04-01) Bock, David
this paper we consider prospective decision rules that aim at extracting early signals about what decision to make, e.g. sell an asset. The decision rules are based on prospective monitoring of a statistic in order to detect a regime shift, e.g. a turn in the trend of asset prices. In the finance literature there are several suggested prospective decision rules that aim at detecting a turn in the price, for example the Filter rule and the rules that use moving averages. Another approach that has been proposed is the hidden Markov model (HMM) approach, where the price level is assumed to change between an upward and a downward trend according to a Markov chain. An approach not often used in a financial setting is statistical surveillance, which deals with the theory and methodology of online detection of an important change in the underlying process of a time series as soon as possible after it has occurred. In this paper inferential differences and similarities between statistical surveillance, two variants of the Filter rule, a rule based on moving averages and an HMM approach are investigated. A new non-parametric and robust approach never used in financial settings is proposed. Further, the purpose is to enhance the use of proper evaluation, where the timeliness of the alarm is considered. Evaluation measures and optimality criteria commonly used in statistical surveillance are reviewed and compared with those generally used in a financial setting. The methods are evaluated on Hang Seng Index.
Some statistical aspects of methods for detection of turning points in business cycles
(Taylor & Francis, 2006) Andersson, Eva; Bock, David; Frisén, Marianne
Methods for on-line turning point detection in business cycles are discussed. The statistical properties of three likelihood based methods are compared. One is based on a Hidden Markov Model, another includes a non-parametric estimation procedure and the third combines features of the other two. The methods are illustrated by monitoring a period of the Swedish industrial production. Evaluation measures that reflect timeliness are used. The effects of smoothing, seasonal variation, autoregression and multivariate issues on methods for timely detection are discussed
Some statistical aspects on methods for detection of turning points in business cycles
(University of Gothenburg, 2002-07-01) Andersson, Eva; Bock, David; Frisén, Marianne
Statistical and practical aspects on methods for on-line turning point detection in business cycles are discussed. When a method is used on a real data set, there are a number of special data problems to be considered. Among these are: the effect of smoothing, seasonal variation, autoregression, the presence of a trend and problems with multivariate data. Different approaches to these data problems are reviewed and discussed. In a practical situation, another important aspect is the estimation procedure for the parameters of the monitoring system. Three likelihood based methods for turning point detection are compared, one based on a Hidden Markov Model and another including a non-parametric estimation procedure. The three methods are used to analyze an actual data set of a period of the Swedish industrial production. The relative merits of comparing methods by one real data set or by simulations are discussed.
Statistical issues in public health monitoring - A review and discussion
(University of Gothenburg, 2001-02-01) Sonesson, Christian; Bock, David
A review of methods, suggested in the literature, for sequential detection of changes in public health surveillance data is presented. Many authors have noticed the need for prospective methods and there has been an increased interest in both the statistical as well as epidemiological literature on this type of problem in the recent years. However, most of the vast literature in public health monitoring deals with retrospective methods. This is especially apparent dealing with spatial methods. Evaluations with respect to the statistical properties of special interest for on-line surveillance are rare. The special aspects of prospective statistical surveillance as well as different ways of evaluating such methods are described. Attention is given to methods including only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the ShiryaevRoberts method are derived.
Statistical surveillance of cyclical processes with application to turns in business cycles
(University of Gothenburg, 2002-08-01) Andersson, Eva; Bock, David; Frisén, Marianne
On-line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood based methods for detection of a turn are compared in detail. One of the methods is based on a Hidden Markov Model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time and the predictive value of an alarm.
Statistical Surveillance of Epidemics: Peak Detection of Influenza in Sweden
(2007-11-28T12:58:30Z) Bock, David; Andersson, Eva; Frisén, Marianne
A statistical surveillance system gives a signal as soon as data give enough evidence of an important event. We consider on-line surveillance systems for detecting changes in influenza incidence. One important feature of the influenza cycle is the start of the influenza season, and another one is the change to a decline (the peak). In this report we discuss statistical methods for on-line peak detection. One motive for doing this is the need for health resource planning. Surveillance systems were adapted for Swedish data on laboratory verified diagnoses of influenza. In Sweden, the parameters of the influenza cycles vary too much from year to year for parametric methods to be useful. We suggest a non-parametric method based on the monotonicity properties of the increase and decline around a peak. A Monte Carlo study indicated that this method has useful stochastic properties. The method was applied to Swedish data on laboratory verified diagnoses of influenza for seven periods.
Statistical Surveillance. Optimal decision times in economics
(2004) Bock, David