{'fraction_non_alphanumeric': 0.024666666666666667, 'fraction_numerical': 0.0, 'mean_word_length': 5.30672268907563, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Guest Editor's Introduction: Risk Measurement and Risk Correlation Analysis

It is well known that the analysis of inter-risk correlation is an important factor in risk measurement. A typical example is the interaction of market risk, credit risk and operational risk, which has attracted considerable concern in financial risk measurement. Usually, risk measurement and risk correlation analysis, to some extent, can be viewed as a class of multi-criteria decision making (MCDM) problems, which deal with the trade-offs among different aspects. For example, in project risk management, the “project triangle” (cost, quality and schedule) is a typical MCDM problem. In addition, some mathematical models such as Copula models are used to measure risk correlation, but risk management must extend far beyond the use of standard measurements in practical operations and applications. An important aspect is to emphasize the correlation analysis of risks and thus effectively measure all kinds of risks. However, complexities involved in risk measurement are growing at least as fast as, if not faster than, tools and methodologies available to those actually responsible for measuring risk in real life situations, as well as academic researchers. The implication is that risk measurement and risk correlation analysis continues to be an under-researched field. Having agreed on this basic fact, the guest editors determined that the purpose of this special issue should be “to assess the current state