Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.
The equity market is not trading around the clock, and the overnight information has been proved be important for understanding pricing anomalies, improving volatility forecasting accuracy, and so forth. However, there is little research investigating its impact on option pricing. In this paper, we provide a framework that integrates intraday, overnight returns, and realized volatility simultaneously within an augmented Autoregressive Volatility model. The analytical option-pricing formula for the new model is derived through the closed-form moment generation function. The empirical results based on S&P 500 index options show that distinguishing the overnight component from daily returns has the potential capability to reduce the pricing errors, both in-sample and out-of-sample.
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