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on Market Microstructure |
| By: | Qiang Liu; Zhi Liu; Chuanhai Zhang |
| Abstract: | In this paper, we are interested in testing if the volatility process is constant or not during a given time span by using high-frequency data with the presence of jumps and microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric way to depict the discrepancy between local variation and global variation. We show that our proposed test estimator converges to a standard normal distribution if the volatility is constant, otherwise it diverges to infinity. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to do the heteroscedasticity test for some real high-frequency financial data. We observe that in almost half of the days tested, the assumption of constant volatility within a day is violated. And this is due to that the stock prices during opening and closing periods are highly volatile and account for a relative large proportion of intraday variation. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.07659&r=all |
| By: | Christopher Kath; Florian Ziel |
| Abstract: | Optimal execution, i.e., the determination of the most cost-effective way to trade volumes in continuous trading sessions, has been a topic of interest in the equity trading world for years. Electricity intraday trading slowly follows this trend but is far from being well-researched. The underlying problem is a very complex one. Energy traders, producers, and electricity wholesale companies receive various position updates from customer businesses, renewable energy production, or plant outages and need to trade these positions in intraday markets. They have a variety of options when it comes to position sizing or timing. Is it better to trade all amounts at once? Should they split orders into smaller pieces? Taking the German continuous hourly intraday market as an example, this paper derives an appropriate model for electricity trading. We present our results from an out-of-sample study and differentiate between simple benchmark models and our more refined optimization approach that takes into account order book depth, time to delivery, and different trading regimes like XBID (Cross-Border Intraday Project) trading. Our paper is highly relevant as it contributes further insight into the academic discussion of algorithmic execution in continuous intraday markets and serves as an orientation for practitioners. Our initial results suggest that optimal execution strategies have a considerable monetary impact. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.07892&r=all |
| By: | Dimitrakopoulos, Stefanos; Tsionas, Mike G.; Aknouche, Abdelhakim |
| Abstract: | We propose a new model for transaction data that accounts jointly for the time duration between transactions and for the discreteness of the intraday stock price changes. Duration is assumed to follow a stochastic conditional duration model, while price discreteness is captured by an autoregressive moving average ordinal-response model with stochastic volatility and time-varying parameters. The proposed model also allows for endogeneity of the trade durations as well as for leverage and in-mean effects. In a purely Bayesian framework we conduct a forecasting exercise using multiple high-frequency transaction data sets and show that the proposed model produces better point and density forecasts than competing models. |
| Keywords: | Ordinal-response models, irregularly spaced data, stochastic conditional duration, time varying ARMA-SV model, Bayesian MCMC, model confidence set. |
| JEL: | C1 C11 C15 C4 C41 C5 C51 C53 C58 |
| Date: | 2020–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103250&r=all |
| By: | Qi Zhao |
| Abstract: | This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed time horizon from a looking back period. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. In a realistic trading simulation setting, the prediction made by the model could be easily monetized. Moreover, this study shows that the LSTM model could extract universal features from trade-by-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data. This study exceeds existing researches in term of the scale and precision of data used, as well as the high prediction accuracy achieved. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.07404&r=all |
| By: | Artur Sokolovsky; Luca Arnaboldi |
| Abstract: | The study introduces an automated trading system for S\&P500 E-mini futures (ES) based on state-of-the-art machine learning. Concretely: we extract a set of scenarios from the tick market data to train the model and further use the predictions to model trading. We define the scenarios from the local extrema of the price action. Price extrema is a commonly traded pattern, however, to the best of our knowledge, there is no study presenting a pipeline for automated classification and profitability evaluation. Our study is filling this gap by presenting a broad evaluation of the approach showing the resulting average Sharpe ratio of 6.32. However, we do not take into account order execution queues, which of course affect the result in the live-trading setting. The obtained performance results give us confidence that this approach is worthwhile. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.09993&r=all |