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Many agent-based models of financial markets have been able to reproduce certain stylized facts that are observed in actual empirical time series data by using " zero-intelligence " agents whose behaviour is largely random in order to ascertain whether certain phenomena arise from market micro-structure as opposed to strategic behaviour. Although these(More)
In this paper, we apply the meshfree radial basis function (RBF) interpolation to numerically approximate zero-coupon bond prices and survival probabilities in order to price credit default swap (CDS) contracts. We assume that the interest rate follows a Cox-Ingersoll-Ross process while the default intensity is described by the Exponential-Vasicek model.(More)
BACKGROUND Sharing information with the team is critical in developing a shared mental model in an emergency, and fundamental to effective teamwork. We developed a structured call-out tool, encapsulated in the acronym 'SNAPPI': Stop; Notify; Assessment; Plan; Priorities; Invite ideas. We explored whether a video-based intervention could improve structured(More)
Liquidity plays an important role in trading and represents a nontrivial economic concept that is difficult to measure as it involves several three dimensions to investigate. In this paper, we explore the liquidity in electronic markets by estimating a time-varying Gamma distribution of volume adjusted prices for both bid and ask side of in the order book.(More)
  • Rafael Velasco-Fuentes, Wing Lon Ng, RAFAEL VELASCO–FUENTES
  • 2008
This paper shows that it is possible to recover normality of asset returns through a stochastic time change, where the appropriate operational time is determined through a function of the cumulative number of trades and/or the cumulative volume. Ané and Geman (2000) showed that the re-centered cumulative number of trades could be used as the appropriate(More)
This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency. The empirical data used in our investigation are five-minute mid-price time series from FX markets. The ANFIS optimisation involves back-testing(More)
The field of econophysics has established that empirical financial time series data exhibit several robust scaling laws, but to date there has been relatively little attempt to explain these scaling phenomena. In this paper we explore the scaling of the absolute changes in logarithmic price with respect to the size of the time interval over which they are(More)
Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. Unlike with time series, DC samples data at irregular time intervals. In this paper, we propose a contrarian trading strategy that is based(More)