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Predicting crises and price jumps in the stock market and based on different models has been growing over the last decade. Due to the presence of big data, this issue has led to the growth of developments in the field of machine learning and deep learning models. Due to the importance of this issue, This study examined the ability of different machine learning models to predict the jumps in the total index of the Tehran Stock Exchange during the period 2013 to 2020. For this purpose, first stock market jumps were extracted based on the ARJI-GARCH approach and then these jumps were predicted by considering the possible effective variables including global and domestic markets. The prediction results of 1-, 3-, and 6-day periods for the out-of-sample period show that the machine learning method based on the long short-term memory (LSTM) network, a recurrent neural network, has a better result than other models.

Objective: Global catastrophic risks are classified into 62 categories, 34 of which have been recorded in Iran. Such risks are major threats to the Iranian economy. Using the capacity of the alternative risk transfer instruments, as one of the recommended ex-ante risk management strategies, creates financial protection in the macro frameworks of the domestic catastrophe risk management via incorporating the financial instruments of the Iranian capital market as well as the Islamic financial market. Transferring such catastrophic risks requires appropriate financial instruments. The purpose of this study is to provide, explain and design the domestic catastrophic risk management framework by alternative risk transfer instruments.Methods: The present study has been conducted based on a systematic literature review method. For this purpose, the related literature was systematically reviewed over a period of eight months. Firstly, after specifying the subject of study, the research question was developed. Then, the research protocol was designed and an appropriate mental model was designed accordingly. Subsequently, inclusion and exclusion criteria were determined. The researchers next began the comprehensive and systematic research through which 112 papers were examined. After classifying, screening, and measuring their quality, 73 studies were selected for the final in-depth analysis. In the next step, through the purposive and snowball sampling method, 18 experts were interviewed. The Delphi method was used to obtain the opinion of experts until data saturation was achieved. Their viewpoint was embedded in the final proposed framework. In the last step, the obtained data were classified, codified (using the open coding method through MAXQDA 2020 software) synthesized, and finally summarized. The trustworthiness and authenticity of the current research was measured according to the four-stage model of Lincoln and Guba.Results: In general, all the factors that affect the catastrophic risk management framework can be classified into four main categories of “necessity, implementation of infrastructure, risk measurement, and designing appropriate financial instruments”. Each category includes several subcategories and codes. The proposed framework in the present study is made up of 4 categories, 10 sub-categories, and 415. Also, 37 risks were collected and classified out of 62 catastrophic risks to obtain the “Iran Disaster Risks Diversity Table”. Finally, the appropriate financial instruments for risk transferring based on the capacity of the domestic capital market, i.e. “Insurance Sukuk Vekala” and “Cat Takaful (CT) Sukuk” (for the international Islamic Capital Market), were designed and proposed by this research.Conclusion: Finally, a schematic model was proposed to establish Iran’s catastrophic risk management framework through alternative risk transfer instruments, according to the diversity of detected factors. As a multi-dimensional concept, the framework should be considered in the implementation process.

Knowledge is a key element in fostering product and service innovation. One of the newest concepts proposed for using knowledge resources outside the organization is the concept of knowledge co-creation. Knowledge co-creation means the processes of voluntary collaboration between customers, researchers and organizational managers to create knowledge about products and services. The most important actor that plays a role in the co-creation of knowledge is the customer. The issue of customer knowledge co-creation is not yet well known in our country's insurance industry, so identifying its components and determining the influence of each of them is one of the challenges of current insurance companies. Therefore, the purpose of this study is to identify the components of customer knowledge co-creation and provide a model of relationships between customer knowledge co-creation components in the country's insurance industry.The present study is a fundamental research based on the purpose and based on the data collection method is a descriptive-survey approach. In order to identify the components of the research, interviews were conducted with 14 experts including insurance industry managers and faculty members who are experts in this field. In order to present the model of component relationships, the standard questionnaire of interpretive structural modelling approach, which is based on pairwise comparisons, was completed by 10 experts. Based on the results of semi-structured interviews with experts, 51 primary codes and 7 categories were extracted, which were classified into 5 levels by interpretive structural modeling approach. In this model, the component of "ICT infrastructure" is at the lowest level, which indicates the highest penetration among other components, which means that the change and development of this component, causes a change and development of other components of the model. Also, the components of "motivating customers" and "building trust", which are at the first level of the model, are the most influential components of the model. Therefore, in order to optimally implement the process of customer knowledge co-creation in insurance companies, strengthening the components of lower levels of the model, which have more influence, should be a priority in management decisions.