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Early warning signals of financial crises with multi-scale quantile regressions of log-periodic power law singularities
Zhang, Qun; Zhang, Qunzhi; Sornette, Didier - 2015
We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle...
Persistent link: https://www.econbiz.de/10011412424
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Information flows around the globe : predicting opening gaps from overnight foreign stock price patterns
Gooijer, Jan G. de; Diks, Cees G. H.; Gatarek, Lukasz T. - 2009
This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on...
Persistent link: https://www.econbiz.de/10011379456
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Towards a Consolidation of Worldwide Journal Rankings — A Classification Using Random Forests and Aggregate Rating via Data Envelopment Analysis
Pishchulov, Grigory; Tuselmann, Heinz; Sinkovics, Rudolf R - 2014
The question of how to assess research outputs published in journals is now a global concern for academics. Numerous journal ratings and rankings exist, some featuring perceptual and peer-review-based journal ranks, some focusing on objective information related to citations, some using a...
Persistent link: https://www.econbiz.de/10014036933
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Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors?
Teng, Huei-Wen; Li, Yu-Hsien - 2022
In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...
Persistent link: https://www.econbiz.de/10014235825
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Evaluating density forecasts with an application to stock market returns
Raaij, Gabriela de; Raunig, Burkhard - 2002
Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...
Persistent link: https://www.econbiz.de/10011431370
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Factor structural time series models for official statistics with an application to hours worked in Germany
Weigand, Roland; Wanger, Susanne; Zapf, Ines - 2015
factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step …. The methods are applied to the estimation of paid and unpaid overtime work as well as flows on working-time accounts in … Germany, which enter the statistics on hours worked in the national accounts. …
Persistent link: https://www.econbiz.de/10011309972
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Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility : International Evidence
Degiannakis, Stavros Antonios - 2018
The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...
Persistent link: https://www.econbiz.de/10012910119
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Hur bör forskningsprestationer mätas?
Henrekson, Magnus; Bernard, Kristin Magnusson; … - In: Ekonomisk debatt 34 (2006) 3, pp. 62-77
Persistent link: https://www.econbiz.de/10003368041
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Predicting extreme VaR : nonparametric quantile regression with refinements from extreme value theory
Schaumburg, Julia - 2010
This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...
Persistent link: https://www.econbiz.de/10003952845
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Multistep Quantile Forecasts for Supply Chain and Logistic Operations : Bootstrapping, the GARCH model and Quantile Regression Based Approaches
Bruzda, Joanna - 2018
In this paper, we discuss and compare empirically various ways of computing multistep quantile forecasts of demand, with a special emphasis on the use of the quantile regression methodology. Such forecasts constitute a basis for production planning and inventory management in logistic systems...
Persistent link: https://www.econbiz.de/10012932647