Table 2 - uploaded by Peter Malec
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Bandwidths for SBF Estimator of Additive Component. Bandwidths determined by minimis- ing modified PLS criterion (18).
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... Previous researches (Malec, 2016;Rahimikia and Poon, 2020a,b) usually calibrate one model for each stock. This prediction model f s for stock s can be written as: ...
The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.
... Later, Valenzuela et al. (2015) used a new relative liquidity measure to show that disagreement in mid-price led to high volatility. Malec (2016) used a semiparametric intraday GARCH model with a seasonality factor and found LOB data from the LOBSTER helped to improve out-of-sample volatility forecasting performance. Pham et al. (2018) found AXS LOB slope drives the volume-volatility relation first through the lagged LOB information and then indirectly through the volume-volatility relation. ...
... Pham et al. (2018) found AXS LOB slope drives the volume-volatility relation first through the lagged LOB information and then indirectly through the volume-volatility relation. Apart from Malec (2016), most previous studies have performed in-sample analysis only. As Poon and Granger (2003) states, 'Taking a utilitarian viewpoint, we believe that the success of a volatility model lies in its out-of-sample forecasting power.' ...
This paper tests if the limit order book (LOB) and news stories from 27 July 2007 to 27 January 2022 can help forecast realised volatility (RV) for stocks. The out-of-sample forecasting results indicate that the CHAR model outperformed all other HAR-family of models. For high volatility days, the simple news count outperformed more sophisticated news sentiments compiled following Loughran and McDonald (2011), while LOB depth outperformed the LOB slope. This impact is more marked for news count than depth during the COVID pandemic. Interestingly, for normal volatility days, the market seems to be driven by buying pressure, as the ask-side of the LOB is more price informative than the bid-side. The reverse is true for high volatility days. A series of forecasting evaluation tests and alternative model specifications confirm our results' robustness.
