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Why does return predictability concentrate in bad times?
Cujean, Julien; Hasler, Michael - 2017
We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...
Persistent link: https://www.econbiz.de/10011721618
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Congation and return predictabiity in asset markets : an experiment with two Lucas trees
Noussair, Charles; Popescu, Andreea Victoria - 2020
Persistent link: https://www.econbiz.de/10012227958
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Examining the sources of excess return predictability : stochastic volatility or market inefficiency?
Lansing, Kevin J.; LeRoy, Stephen F.; Ma, Jun - 2018
Persistent link: https://www.econbiz.de/10011977460
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The multi-fractal model of asset returns : its estimation via GMM and its use for volatility forecasting
Lux, Thomas (contributor) - 2003 - [Elektronische Ressource]
Persistent link: https://www.econbiz.de/10001781206
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The Markov-switching multi-fractal model of asset returns : GMM estimation and linear forecasting of volatility
Lux, Thomas (contributor) - 2004 - [Elektronische Ressource],rev
Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … volatility components. From a practical point of view, ML also becomes computationally unfeasible for large numbers of components … forecasts which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo …
Persistent link: https://www.econbiz.de/10002468813
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The Markov-Switching Multifractal Model of asset returns : GMM estimation and linear forecasting of volatility
Lux, Thomas (contributor) - 2006
forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo … linear compared to optimal forecasts is small. Extending the number of volatility components beyond what is feasible with MLE …
Persistent link: https://www.econbiz.de/10003392192
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High-frequency data and stock-bond investing
Lai, Yu-Sheng - In: Journal of forecasting 41 (2022) 8, pp. 1623-1638
Persistent link: https://www.econbiz.de/10013465728
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Tail risk and long memory in financial markets
Nguyen, Duc Binh Benno - 2018
Persistent link: https://www.econbiz.de/10012173996
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Modelling and forecasting volatility in international financial markets
Enow, Samuel Tabot - 2023
Persistent link: https://www.econbiz.de/10014287162
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Hurst Dynamics of S&P500 Returns : Implications and Impact on Market Efficiency, Long Memory, Multifractality and Financial Crises Predictability
Vogl, Markus; Roetzel, Peter Gordon - 2021
In this study, we apply a rolling window approach to wavelet-filtered (denoised) S&P500 returns (2000–2020) to obtain time varying Hurst exponents. We analyse the dynamics of the Hurst exponents by applying statistical tests (e.g., for stationarity, Gaussianity and self-similarity), a...
Persistent link: https://www.econbiz.de/10013229642