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The merit of high-frequency data in portfolio allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - 2011
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...
Persistent link: https://www.econbiz.de/10010308574
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The merit of high-frequency data in portfolio allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - 2011
This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...
Persistent link: https://www.econbiz.de/10010281594
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The random walk of high frequency trading
Aldrich, Eric M.; Heckenbach, Indra; Laughlin, Gregory - 2015
This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns …-frequency asset returns both in ordinary clock time and in trade time. We show that when controlling for pre-scheduled market news … events, trade-time returns of the near-month E-mini S&P 500 futures contract are well characterized by a Gaussian …
Persistent link: https://www.econbiz.de/10011406341
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Money growth and aggregate stock returns
Böing, Tobias; Stadtmann, Georg - 2016
We empirically evaluate the predictive power of money growth measured by M2 for stock returns of the S&P 500 index. We use monthly US data and predict multiperiod returns over 1, 3, and 5 years with long-horizon regressions. In-sample regressions show that money growth is useful for predicting...
Persistent link: https://www.econbiz.de/10011582284
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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of Risk and Financial Management 15 (2022) 11, pp. 1-17
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...
Persistent link: https://www.econbiz.de/10014332691
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Money Growth and Aggregate Stock Returns
Böing, Tobias; Stadtmann, Georg - In: Credit and Capital Markets – Kredit und Kapital 50 (2017) 4, pp. 489-508
We empirically evaluate the predictive power of money growth measured by M2 for stock returns of the S&P 500 index. We use monthly US data and predict multiperiod returns over 1, 3, and 5 years with long-horizon regressions. In-sample regressions show that money growth is useful for predicting...
Persistent link: https://www.econbiz.de/10014521809
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Are momentum traders different? Implications for the momentum puzzle
Menkhoff, Lukas - 2010
This paper examines the puzzlingly high unexploited momentum returns from a new perspective. We analyze characteristics of momentum traders in a sample of 692 fund managers. We find that momentum traders are 'defined' by their short-term horizon, by a behavioural view on the market and by a...
Persistent link: https://www.econbiz.de/10010270394
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Published stock recommendations as institutional investor sentiment in the near-term stock market
Singer, Nico; Dreher, Frank; Laser, Saskia - 2012
This paper investigates the role of published stock recommendations in print and online media as investor sentiment in the near-term German stock market. In line with extant literature on other sentiment measures, vector autoregressions reveal that past stock returns drive today's sentiment, but...
Persistent link: https://www.econbiz.de/10010319293
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Multi-period credit default prediction with time-varying covariates
Orth, Walter - 2011
In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the … context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time …
Persistent link: https://www.econbiz.de/10010304613
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Misspecification Testing in GARCH-MIDAS Models
Conrad, Christian; Schienle, Melanie - 2015
We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a...
Persistent link: https://www.econbiz.de/10011301515