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We formulate a price discovery model in which the price discovery measures vary either locally, say, for instance, at intervals of 30 minutes or at a daily frequency. Given the empirical and theoretical evidence that price discovery measures relate to highly persistent fundamentals, we adopt a...
Persistent link: https://www.econbiz.de/10014353587
We estimate a general microstructure model of the transitory and permanent impact of order flow on stock prices. Jumps …-capitalization stocks traded on the Euronext-Paris Bourse. We find that, at tick frequency, the overnight return, the intraday jumps, and … microstructure model explains on average 47.7% of the total variation. Once jumps are filtered and parameters are estimated in real …
Persistent link: https://www.econbiz.de/10010256970
The simultaneous occurrence of jumps in several stocks can be associated with major financial news, triggers short …
Persistent link: https://www.econbiz.de/10011544772
While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little … digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading … volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction …
Persistent link: https://www.econbiz.de/10013323741
of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit … interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling … frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we …
Persistent link: https://www.econbiz.de/10010281504
During the financial crisis, stock returns became more correlated, increasing the R-square of market models estimated during that time. However, the overall increase in volatility also increased the standard error of these models. As a result, conventional event studies tend to find too many...
Persistent link: https://www.econbiz.de/10013043016
This paper introduces a new class of stochastic volatility models which allows for stochastic volatility of volatility (SVV): Volatility modulated non-Gaussian Ornstein-Uhlenbeck (VMOU) processes. Various probabilistic properties of (integrated) VMOU processes are presented. Further we study the...
Persistent link: https://www.econbiz.de/10013117444
The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial 'dynamically purified' price process that in theory allows to eliminate the impact of the stock price movements....
Persistent link: https://www.econbiz.de/10013063198
We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...
Persistent link: https://www.econbiz.de/10014351081
Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...
Persistent link: https://www.econbiz.de/10014236793