Advanced trading strategies - page 6

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The study assessed effect of futures trading on cluster beans price volatility in selected three markets of Rajasthan during the period of 2003-2015 using symmetric GARCH (1,1), and asymmetric EGARCH (1,1) and TGARCH (1,1) models. The results indicate futures trading have significant effect in reducing cluster bean prices volatility in various selected markets of Rajasthan. In addition, averagely among the three cluster beans selected markets in Rajasthan, prices in Anoopgarh showed lower volatility according to the models used compared to Sri Ganganagar. However, Hanumangarh showed the highest price volatility from shocks. Persistence of shocks was however longer in Sri Ganganagar market compared to Anoopgarh with the lowest persistence of shocks on volatility recorded in Hanumangarh. The results further show asymmetric effect of prices volatility whereby cluster bean market prices respond with much more volatility to unexpected increases in prices (good news) than it does to decreases in prices (bad news).
 
Among many strategies for financial trading, pairs trading has played an important role in practical and academic frameworks. Loosely speaking, it involves a statistical arbitrage tool for identifying and exploiting the inefficiencies of two long-term, related financial assets. When a significant deviation from this equilibrium is observed, a profit might result. In this paper, we propose a pairs trading strategy entirely based on linear state space models designed for modelling the spread formed with a pair of assets. Once an adequate state space model for the spread is estimated, we use the Kalman filter to calculate conditional probabilities that the spread will return to its long-term mean. The strategy is activated upon large values of these conditional probabilities: the spread is bought or sold accordingly. Two applications with real data from the US and Brazilian markets are offered, and even though they probably rely on limited evidence, they already indicate that a very basic portfolio consisting of a sole spread outperforms some of the main market benchmarks.
 

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.


 
We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstratedby empirical experiments on forecasting the challenging  S&P500 market
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