Predicting future price volatility: Empirical evidence from an emerging limit order market

Jain, Pawan; Jiang, Christine X.

doi:10.1016/j.pacfin.2014.01.006

Cited by 14 publications

(5 citation statements)

References 74 publications

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“…3 The only related study is Jain and Jiang (2014), which shows that the limit order book slope consistently and significantly predicts future price volatility. However, the paper does not model the price impact of incoming orders nor evaluates the forecast accuracy of volatility.…”

Section: Introductionmentioning

confidence: 99%

Volatility Modeling and Prediction: The Role of Price Impact

et al. 2018

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In this paper, we are interested in exploring the role of price impact, derived from the order book, in modeling and predicting stock volatility. This is motivated by the market microstructure literature that examines the mechanics of price formation and its relevance to market quality. Using a comprehensive dataset of intraday bids, asks, and three levels of market depths for 148 stocks in the Shanghai Stock Exchange from 2005 to 2016, we find substantial intraday impact from incoming bid and ask limit and market orders on stock prices. More importantly, the permanent price impact at the daily level is a significant determinant of stock volatility dynamics as suggested by the panel VAR estimation. Furthermore, when we augment traditional volatility models with the time series of daily price impact, the augmented models produce significantly more accurate volatility predictions at the one-day ahead forecasting horizon. These volatility predictions also offer economic gains to a mean-variance utility investor in a portfolio setting.

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Section: Introductionmentioning

confidence: 99%

Volatility Modeling and Prediction: The Role of Price Impact

et al. 2018

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“…The impact is, 2 Our sample compares favourably to 50 US stocks for a sample period of 21 days in Cont et al (2014); 30 stocks in the Euronext Amsterdam exchange with a two-month sample periods in Hautsch and Huang (2012); and 100 stocks in Nasdaq over two years in Engle and Patton (2004). 3 The only related study is Jain and Jiang (2014), which shows that the limit order book slope consistently and significantly predicts future price volatility. However, the paper does not model the price impact of incoming orders nor evaluates the forecast accuracy of volatility.…”

Section: Introductionmentioning

confidence: 88%

Volatility modeling and prediction: the role of price impact

Jiang

Cao

Liu

et al. 2019

Quantitative Finance

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In this paper, we are interested in exploring the role of price impact, derived from the order book, in modeling and predicting stock volatility. This is motivated by the market microstructure literature that examines the mechanics of price formation and its relevance to market quality. Using a comprehensive dataset of intraday bids, asks, and three levels of market depths for 148 stocks in the Shanghai Stock Exchange from 2005 to 2016, we find substantial intraday impact from incoming bid and ask limit and market orders on stock prices. More importantly, the permanent price impact at the daily level is a significant determinant of stock volatility dynamics as suggested by the panel VAR estimation.Furthermore, when we augment traditional volatility models with the time series of daily price impact, the augmented models produce significantly more accurate volatility predictions at the one-day ahead forecasting horizon. These volatility predictions also offer economic gains to a mean-variance utility investor in a portfolio setting. JEL code: C53, G14

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“…Other variables other than price returns have been used to forecast price volatility. Jain and Jiang [9] aim to predict future price volatility using the limit order book (LOB) from the Shanghai Stock Exchange (SHSE). The data is obtained from SHSE from January 2009 to December 2009.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Closing Price of Cryptocurrency Ethereum

Ravele,

Sigauke,

Rambevha

2024

Preprint

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Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.

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Predicting future price volatility: Empirical evidence from an emerging limit order market

Cited by 14 publications

References 74 publications

Volatility Modeling and Prediction: The Role of Price Impact

Volatility Modeling and Prediction: The Role of Price Impact

Volatility modeling and prediction: the role of price impact

Predicting Closing Price of Cryptocurrency Ethereum

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