It is well-established that the …nancial time series display some stylized fatcs such as volatility clustering, high kurtosis, low starting and slow-decaying autocorrelation function and the Talyor e¤ect as well. In order to evaluate volatility models’capacity in capturing such facts, we apply both standard and robust measures of kurtosis and autocorrelation of squares to GARCH, EGARCH and ARSV...

This essay introduces a new featured section, to be published in Small Axe annually, that explores the critical vocabulary of field Caribbean studies.

Foreign exchange market is one of the most complex dynamic market with high volatility, non linear and irregularity. As the globalization spread to the world, exchange rates forecasting become more important and complicated. Many external factors influence its volatility. To forecast the exchange rates, those external variables can be used and usually chosen based on the correlation to the pred...

We introduce a local volatility model for the valuation of options on commodity futures by using European vanilla option prices. The corresponding calibration problem is addressed within an online framework, allowing the use of multiple price surfaces. Since uncertainty in the observation of the underlying future prices translates to uncertainty in data locations, we propose a model-based adjus...

We analyze the relations between the stock market and a stock bulletin board system (BBS) in Japan. Previous studies in the USA found that the characteristics of messages posted on stock BBSs can predict market volatility and trading volume. We develop hypotheses based on the results of those analyses and apply statistical analysis to the data about companies mentioned in a large number of mess...

Financial Markets: Very Noisy Information Processing Malik Magdon-Ismail, Alexander Nicholson and Yaser S. Abu-Mostafa Abstract| We report new results about the impact of noise on information processing, with application to nancial markets. These results quantify the tradeo between the amount of data and the noise level in the data. They also provide estimates for the performance of a learning ...

Artificial neural networks has been used successfully for forecasting nonlinear time series. In this paper, we present a novel approach to forecast the volatility of financial time series using a multilayer perceptron with adaptive activation functions; model parameters are estimated by maximizing the natural logarithm of the residuals. To guaranty that the variance is always zero or positive, ...