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on Market Microstructure |
| By: | Fernando Moreno-Pino; Stefan Zohren |
| Abstract: | Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data, thereby naturally mimicking (via a data-driven approach) the econometric models which incorporate realised measures of volatility into the forecast. This allows us to take advantage of the abundance of intraday observations, helping us to avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate DeepVol's performance. The reported empirical results suggest that the proposed deep learning-based approach learns global features from high-frequency data, achieving more accurate predictions than traditional methodologies, yielding to more appropriate risk measures. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.04797&r=mst |
| By: | Rene Carmona; Claire Zeng |
| Abstract: | This paper investigates the impact of anonymous trading on the agents' strategy in an optimal execution framework. It mainly explores the specificity of order attribution on the Toronto Stock Exchange, where brokers can choose to either trade with their own identity or under a generic anonymous code that is common to all the brokers. We formulate a stochastic differential game for the optimal execution problem of a population of $N$ brokers and incorporate permanent and temporary price impacts for both the identity-revealed and anonymous trading processes. We then formulate the limiting mean-field game of controls with common noise and obtain a solution in closed-form via the probabilistic approach for the Almgren-Chris price impact framework. Finally, we perform a sensitivity analysis to explore the impact of the model parameters on the optimal strategy. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.04167&r=mst |
| By: | Alain P. Chaboud; Caren Cox; Michael J. Fleming; Ellen Correia Golay; Yesol Huh; Frank M. Keane; Kyle Lee; Krista B. Schwarz; Clara Vega; Carolyn Windover |
| Abstract: | While the U.S. Treasury market remains the deepest and most liquid securities market in the world, several episodes of abrupt deterioration in market functioning over recent years have brought the market’s resilience into focus. The adoption of all-to-all trading in the Treasury market could be one avenue to strengthen market resilience. Conceptually, all-to-all trading would allow any market participant to trade directly with any other market participant. This could be particularly helpful in times of stress, when the capacity of traditional intermediaries may be tested. In this paper, we discuss what all-to-all trading would mean for the cash secondary Treasury market, the benefits it might bring, and the conditions that might make adoption of the protocol more likely. We also review several trading protocols operating in the Treasury market that widen the field of trading partners and discuss the challenges to broader adoption of such protocols. |
| Keywords: | Treasury market; market structure; all-to-all |
| JEL: | D47 G10 G23 |
| Date: | 2022–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:94959&r=mst |