Developing Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithm

2018 ◽  
Author(s):  
Yu-Ying Chen ◽  
Wei-Lun Chen ◽  
Szu-Hao Huang
Keyword(s):  
High Frequency ◽  
Pairs Trading ◽  
Arbitrage Strategy

Ultra-High-Frequency Pairs Trading in Gold ETFs

2017 ◽  
Author(s):  
Thong Dao ◽  
Frank McGroarty ◽  
Andrew Urquhart
Keyword(s):  
High Frequency ◽  
Pairs Trading ◽  
Ultra High Frequency

scholarly journals Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500

2019 ◽  
Vol 12 (2) ◽  
pp. 51 ◽  
Author(s):  
Johannes Stübinger ◽  
Lucas Schneider
Keyword(s):  
High Frequency ◽  
Test Procedure ◽  
Research Area ◽  
High Frequency Data ◽  
Frequency Data ◽  
Stock Selection ◽  
Deep Dive ◽  
Statistical Arbitrage ◽  
Jump Diffusion Model ◽  
Arbitrage Strategy

This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump–diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998–December 2015. In particular, the established stock selection and trading framework identifies overnight price gaps based on an advanced jump test procedure and exploits temporary market anomalies during the first minutes of a trading day. The existence of the assumed mean-reverting property is confirmed by a preliminary analysis of the S&P 500 index; this characteristic is particularly significant 120 min after market opening. In the empirical back-testing study, the strategy delivers statistically- and economically-significant returns of 51.47 percent p.a.and an annualized Sharpe ratio of 2.38 after transaction costs. We benchmarked our trading algorithm against existing quantitative strategies from the same research area and found its performance superior in a multitude of risk-return characteristics. Finally, a deep dive analysis shows that our results are consistently profitable and robust against drawdowns, even in recent years.

scholarly journals Pairs trading with the persistence-based decomposition model

2020 ◽  
Vol 20 (2) ◽  
pp. 151
Author(s):  
Jonas Rende
Keyword(s):  
High Frequency ◽  
Scientific Community ◽  
Large Scale ◽  
Model Fitting ◽  
Short Selling ◽  
High Frequency Data ◽  
Frequency Data ◽  
Pairs Trading ◽  
Risk Return ◽  
The Impact

Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using SP 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent.

scholarly journals Efficiency of S&P CNX Nifty Index Option of the National Stock Exchange (NSE), India, using Box Spread Arbitrage Strategy

2014 ◽  
Vol 15 (3) ◽  
pp. 269 ◽  
Author(s):  
G. P. Girish ◽  
Nikhil Rastogi
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Estimation Error ◽  
High Frequency Data ◽  
Frequency Data ◽  
Index Options ◽  
Time Period ◽  
Arbitrage Strategy ◽  
Computational Simplicity ◽  
Index Option

Box spread is a trading strategy in which one simultaneously buys and sells options having the same underlying asset and time to expiration, but different exercise prices. This study examined the efficiency of European style S&P CNX Nifty Index options of National Stock Exchange, (NSE) India by making use of high-frequency data on put and call options written on Nifty (Time-stamped transactions data) for the time period between 1st January 2002 and 31st December 2005 using box-spread arbitrage strategy. The advantages of box-spreads include reduced joint hypothesis problem since there is no consideration of pricing model or market equilibrium, no consideration of inter-market non-synchronicity since trading box spreads involve only one market, computational simplicity with less chances of mis-specification error, estimation error and the fact that buying and selling box spreads more or less replicates risk-free lending and borrowing. One thousand three hundreds and fifty eight exercisable box-spreads were found for the time period considered of which 78 Box spreads were found to be profitable after incorporating transaction costs (32 profitable box spreads were identified for the year 2002, 19 in 2003, 14 in 2004 and 13 in 2005) The results of our study suggest that internal option market efficiency has improved over the years for S&P CNX Nifty Index options of NSE India.     

Sign in / Sign up

Export Citation Format

Share Document