The Experts below are selected from a list of 21894 Experts worldwide ranked by ideXlab platform
Stefan Zeisberger - One of the best experts on this subject based on the ideXlab platform.
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what makes an investment risky an analysis of Price Path characteristics
Social Science Research Network, 2019Co-Authors: Charlotte Borsboom, Stefan ZeisbergerAbstract:We examine the influence of financial asset historical Price Path characteristics on investors' risk perception, return beliefs and investment propensity. To that end, we run a series of survey experiments in which we present various Price patterns to individuals with vested interest in financial matters. Our findings reveal that Price Paths with identical daily and monthly returns (and consequently identical return standard deviation) can lead to substantially different risk perception by investors, indicating that historical volatility is not sufficient to explain risk perception. Salient features such as highs, lows and crashes are the most influential drivers of perceived risk in Price Paths. Return forecasts are primarily driven by past overall returns and the most recent Price developments. Perceived risk and return beliefs strongly predict investment propensity.
Galit Shmueli - One of the best experts on this subject based on the ideXlab platform.
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real time forecasting of online auctions via functional k nearest neighbors
International Journal of Forecasting, 2010Co-Authors: Shu Zhang, Wolfgang Jank, Galit ShmueliAbstract:Abstract Forecasting Prices in online auctions is important for both buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K -nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weights their contributions by their relevance in terms of auction, seller and product features, and by the similarity of the Price Paths. We capture an auction’s Price Path by borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure the distances between two Price Paths via the Kullback–Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing an improved predictive performance over several competing models. We also investigate the performance across various levels of data heterogeneity, and find that fKNN is particularly effective for forecasting heterogeneous auction populations.
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real time forecasting of online auctions via functional k nearest neighbors
Social Science Research Network, 2009Co-Authors: Shu Zhang, Wolfgang Jank, Galit ShmueliAbstract:Forecasting the Price in online auctions is important for buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K-nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction, seller and product features, and by similarity of the Price Paths. We capture an auction's Price Path borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure distances between two Price Paths via the Kullback-Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing improved predictive performance over several competing models. We also investigate performance across various levels of data heterogeneity, finding that fKNN is particularly effective for forecasting heterogeneous auction populations.
Joe Chen - One of the best experts on this subject based on the ideXlab platform.
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cartel pricing dynamics with cost variability and endogenous buyer detection
International Journal of Industrial Organization, 2006Co-Authors: Joseph E Harrington, Joe ChenAbstract:This paper characterizes collusive pricing patterns when buyers may detect the presence of a cartel. Buyers are assumed to become suspicious when observed Prices are anomalous. We find that the cartel Price Path is comprised of two phases. During the transitional phase, Price is generally rising and relatively unresponsive to cost shocks. During the stationary phase, Price responds to cost but is much less sensitive than under non-collusion or simple monopoly. The length of the transition phase is decreasing in the variance of cost shocks. It is also shown that the cartel Price Path may overshoot its long-run level so that Price converges from above.
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the impact of the corporate leniency program on cartel formation and the cartel Price Path
CIRJE F-Series, 2005Co-Authors: Joe Chen, Joseph E HarringtonAbstract:Previous research exploring the effect of corporate leniency programs has modelled the oligopoly stage game as a Prisoners' Dilemma. Using numerical analysis, we consider the Bertrand Price game and allow the probability of detection and penalties to be sensitive to firms' Prices. Consistent with earlier results, a maximal leniency program necessarily makes collusion more difficult. However, we also find that par-tial leniency programs - such as in the U.S.- can make collusion easier compared too offering no leniency. We also show that even if cartel formation is not deterred, a leniency program can reduce the Prices charged by firms.
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he impact of the corporate leniency program on cartel formation and the cartel Price Path
Research Papers in Economics, 2005Co-Authors: Joseph E Harrington, Joe ChenAbstract:Previous research exploring the effect of corporate leniency programs has modelled the oligopoly stage game as a Prisoners?Dilemma. Using numerical analysis, we consider the Bertrand Price game and allow the probability of detection and penalties to be sensitive to firms?Prices. Consistent with earlier results, a maximal leniency program necessarily makes collusion more difficult. However, we also find that partial leniency programs - such as in the U.S. - can make collusion easier compared to offering no leniency. We also show that even if cartel formation is not deterred, a leniency program can reduce the Prices charged by firms.
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cartel pricing dynamics with cost variability and endogenous buyer detection
CIRJE F-Series, 2005Co-Authors: Joseph E Harrington, Joe ChenAbstract:This paper characterizes collusive pricing patterns when buyers may detect the presence of a cartel. Buyers are assumed to become suspicious when observed Prices are anomalous. We find that the cartel Price Path is comprised of two phases. During the transitional phase, Price is generally rising and relatively unresponsive to cost shocks. During the stationary phase, Price responds to cost but is much less sensitive than under non-collusion or simple monopoly; a low Price variance may then be a collusive marker. Compared to when firms do not collude, cost shocks take a longer time to pass-through to Price.
Shu Zhang - One of the best experts on this subject based on the ideXlab platform.
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real time forecasting of online auctions via functional k nearest neighbors
International Journal of Forecasting, 2010Co-Authors: Shu Zhang, Wolfgang Jank, Galit ShmueliAbstract:Abstract Forecasting Prices in online auctions is important for both buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K -nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weights their contributions by their relevance in terms of auction, seller and product features, and by the similarity of the Price Paths. We capture an auction’s Price Path by borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure the distances between two Price Paths via the Kullback–Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing an improved predictive performance over several competing models. We also investigate the performance across various levels of data heterogeneity, and find that fKNN is particularly effective for forecasting heterogeneous auction populations.
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real time forecasting of online auctions via functional k nearest neighbors
Social Science Research Network, 2009Co-Authors: Shu Zhang, Wolfgang Jank, Galit ShmueliAbstract:Forecasting the Price in online auctions is important for buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K-nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction, seller and product features, and by similarity of the Price Paths. We capture an auction's Price Path borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure distances between two Price Paths via the Kullback-Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing improved predictive performance over several competing models. We also investigate performance across various levels of data heterogeneity, finding that fKNN is particularly effective for forecasting heterogeneous auction populations.
Joachim Schleich - One of the best experts on this subject based on the ideXlab platform.
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delaying the introduction of emissions trading systems implications for power plant investment and operation from a multi stage decision model
Energy Economics, 2015Co-Authors: Jianlei Mo, Joachim SchleichAbstract:Relying on real options theory, we employ a multistage decision model to analyze the effect of delaying the introduction of emission trading systems (ETS) on power plant investments in carbon capture and storage (CCS) retrofits, on plant operation, and on carbon dioxide (CO2) abatement. Unlike previous studies, we assume that the investment decision is made before the ETS is in place, and we allow CCS operating flexibility for new power plant investments. Thus, the plant may be run in CCS-off mode if carbon Prices are low. We employ Monte Carlo simulation methods to account for uncertainties in the Prices of CO2 certificates, other inputs, and output Prices, relying on a realistic parameterization for a supercritical pulverized coal plant in China. We find that CCS operating flexibility lowers the critical carbon Price needed to support CCS investment because it renders CCS investment less irreversible. For a low carbon Price Path, operating flexibility also implies that delaying the introduction of an ETS hardly affects plant CO2 abatement since the plant operator is better off purchasing emission certificates rather than operating the plant in CCS mode. Interestingly, for low carbon Prices we find a U-shaped relation between the length of the delay and the economic value of the plant. Thus, delaying the introduction of an ETS may make investors worse off.