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on Market Microstructure |
| By: | Hafner, C. M. |
| Abstract: | We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an iid assumption. We derive large sample properties for both estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process. Finally, we demonstrate the model performance and its empirical relevance on two applications. First, we study the impact of stock splits on the illiquidity dynamics of the five largest US technology company stocks. Second, we investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index. |
| Keywords: | Nonparametric, Semiparametric, Splits, Structural Change |
| JEL: | C12 C14 |
| Date: | 2022–02–23 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camjip:2206&r= |
| By: | Staffa, Ruben |
| Abstract: | This paper uses novel news ticker data to evaluate the effect of sovereign risk on economic and financial outcomes. The use of intraday news enables me to derive policy events and respective timestamps that potentially alter investors' beliefs about a sovereign's willingness to service its debt and thereby sovereign risk. Following the high frequency identification literature, in the tradition of Kuttner (2001) and Guerkaynak et al. (2005), associated variation in sovereign risk is then obtained by capturing bond price movements within narrowly defined time windows around the event time. I conduct the outlined identification for Italy since its large bond market and its frequent coverage in the news render it a suitable candidate country. Using the identified shocks in an instrumental variable local projection setting yields a strong instrument and robust results in line with theoretical predictions. I document a dampening effect of sovereign risk on output. Also, borrowing costs for the private sector increase and inflation rises in response to higher sovereign risk. |
| Keywords: | high frequency identification,instrument,local projections,sovereign risk,text data |
| JEL: | C36 E43 E62 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:82022&r= |
| By: | Chao Zhang; Yihuang Zhang; Mihai Cucuringu; Zhongmin Qian |
| Abstract: | We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting diurnal effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.08962&r= |