Predicting the Turning Points of a Time Series

Wecker, William E.

doi:10.1086/296032

Cited by 95 publications

(47 citation statements)

References 8 publications

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“…1 As we investigate monthly time series, we define τ = 2. Choosing τ = 1 would result in a model too sensitive to smaller movements of the time series, whereas with τ > 2 the model would react with inacceptable delay.…”

Section: Probabilistic Statements For Turning Points In Time Seriesmentioning

confidence: 99%

“…In this paper, our intention is to explicitly incorporate this uncertainty into probabilistic statements for turning points in monthly financial time series. We implement a Monte-Carlo-based regression introduced by Wecker [1] and enhanced by Kling [2], which is described in section 2. To decide which models (ARMA or VAR) perform better we take the view of a participant in the financial markets.…”

Section: Introductionmentioning

confidence: 99%

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Data Mining for the Detection of Turning Points in Financial Time Series

Poddig

Huber

1999

Advances in Intelligent Data Analysis

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Abstract. One of the most challenging problems in econometrics is the prediction of turning points in financial time series. We compare ARMA-and Vector-Autoregressive (VAR-) models by examining their abilities to predict turning points in monthly time series. An approach proposed by Wecker[1] and enhanced by Kling[2] forms the basis to explicitly incorporate uncertainty in the forecasts by producing probabilistic statements for turning points. To allow for possible structural change within the time period under investigation, we conduct Data Mining by using rolling regressions over a fix-sized window. For each datapoint a multitude of models is estimated. The models are evaluated by an economic performance criterion, the Sharpe-Ratio, and a testing procedure for its statistical significance developed by Jobson/Korkie [3]. We find that ARMA-models seem to be valuable forecasting tools for predicting turning points, whereas the performance of the VAR-models is disappointing.

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Section: Probabilistic Statements For Turning Points In Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Mining for the Detection of Turning Points in Financial Time Series

Poddig

Huber

1999

Advances in Intelligent Data Analysis

View full text Add to dashboard Cite

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“…ARMA-and VAR-models. With those estimates we applied a Monte-Carlo based procedure developed by Wecker [1] and Kling [2] to obtain probabilistic statements about near-by TPs. A TP is detected if the probability reaches or exceeds a certain threshold θ, e.g.…”

Section: The Detection Of Turning Points In Financial Time Seriesmentioning

confidence: 99%

“…In this paper, we implement a Monte-Carlo-based regression approach introduced by Wecker [1] and enhanced by Kling [2] to produce probabilistic statements for near-by TPs in monthly financial time series. This method needs forecasts of future values of the time series.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series

Poddig

Huber

1999

Principles of Data Mining and Knowledge Discovery

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Abstract. The aim of this paper is to compare the influence of different model selection criteria on the performance of ARMA-and VAR-models to predict turning points in nine financial time series. As the true data generating process (DGP) in general is unknown, so is the model that mimics the DGP. In order to find the model which fits the data best, we conduct data mining by estimating a multitude of models and selecting the best one optimizing a well-defined model selection criterion. In the focus of interest are two simple in-sample criteria (AIC, SIC) and a more complicated out-of-sample model selection procedure. We apply Analysis of Variance to assess which selection criterion produces the best forecasts. Our results indicate that there are no differences in the predictive quality when alternative model selection criteria are used.

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“…Pentikainen and Rantala (1981) used simulation to estimate when an insurance company might reach a "ruin barrier." Wecker (1979) used simulation to estimate the turning points in the future of a time series.…”

Section: Introductionmentioning

confidence: 99%

Sampling the Future: A Bayesian Approach to Forecasting From Univariate Time Series Models

Thompson

Miller

1986

Journal of Business & Economic Statistics

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The Box-Jenkins methodology for modeling and forecasting from univariate time series models has long been considered a standard to which other forecasting techniques have been compared. To a Bayesian statistician, however, the method lacks an important facet-a provision for modeling uncertainty about parameter estimates. We present a technique called sampling the future for including this feature in both the estimation and forecasting stages. Although it is relatively easy to use Bayesian methods to estimate the parameters in an autoregressive integrated moving average (ARIMA) model, there are severe difficulties in producing forecasts from such a model. The multiperiod predictive density does not have a convenient closed form, so approximations are needed. In this article, exact Bayesian forecasting is approximated by simulating the joint predictive distribution. First, parameter sets are randomly generated from the joint posterior distribution. These are then used to simulate future paths of the time series. This bundle of many possible realizations is used to project the future in several ways. Highest probability forecast regions are formed and portrayed with computer graphics. The predictive density's shape is explored. Finaliy, we discuss a method that allows the analyst to subjectively modify the posterior distribution on the parameters and produce alternate forecasts.

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Predicting the Turning Points of a Time Series

Cited by 95 publications

References 8 publications

Data Mining for the Detection of Turning Points in Financial Time Series

Data Mining for the Detection of Turning Points in Financial Time Series

A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series

Sampling the Future: A Bayesian Approach to Forecasting From Univariate Time Series Models

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