Sharif Digital Repository

In many methods of calculating Value-at-Risk (VaR), we need to calculate the value of the portfolio several times for different scenarios. Because an explicit formula is not available to calculate the value of some fixed income assets, calculating VaR for portfolios containing these assets imposes a heavy computational burden. In this study, we introduce a new method for calculating VaR for such portfolios. In this method, some of the existing scenarios are selected as basic scenarios and the value of the portfolio is calculated only for each of them. Next, using the calculated values, the portfolio values for other scenarios are estimated by interpolation (or extrapolation). Finally, by... 

Insurance companies in Iran operate in a completely competitive and dynamic environment. Because customer acquisition in these companies is significantly more expensive than customer retention, with timely forecasting of churning customers, they can manage their customers more effectively. In this study, in order to predict customer churn in the insurance industry, the data of one of the Iranian insurance companies that has more than two million insurers were used. In order to identify important data and variables, previous studies were reviewed, and on the other hand, the Central Insurance Regulations of the Islamic Republic of Iran, as well as the information of the insurance contracts of... 

In this study, we combined supervised and unsupervised machine learning algorithms, included the benefits of true identification of good borrowers and costs of false identification of bad borrowers, and then proposed a model for predicting the default of loan applicants with a profit-based approach. The results show that our proposed model has the best performance in profit measure in comparison with individual supervised models. In fact, we first divided the data into two train sets and one test set. We have constructed our model by training unsupervised models on the first train set and supervised models on the second train set. The results of implementing the model on the Australian and... 

I investigate methods of integrating prior hierarchical structure into hierarchical portfolio optimization methods. My contributions to the literature are forming a prior hierarchical structure based on investors' priorities and generating a unique representative distance matrix, which can be used as an input to other portfolio optimization methods too. In addition, I use SIC and GICs industry classifications as priory information for S&P500 companies and use them as a complementary input to the Hierarchical Risk Parity model and Hierarchical Equal Risk Contribution and compare the resultant portfolios' performance with (López de Prado, 2019)’s method of integrating prior information and... 

In the basic model of this thesis, which is called nested cluster optimization, only one distance function is used for clustering to form clusters with similar characteristics, while depending on whether the optimization model is long-only or long-short, different functions can be used. The aim of this thesis is to find the optimal distance function between assets in the simple nested cluster optimization so that during three different and separate strategies, based on three criteria of the lowest risk, the highest Sharpe ratio, and the highest return, the optimal distance function of assets is selected and clustering and finally weighting the portfolio to be done. The optimal distance... 

Cryptocurrencies (cryptos), as a new type of money, are considered a medium of exchange, an investment asset, and a hedging tool in today's world. In 2008, bitcoin as the first cryptocurrency was introduced, which has survived through recent years and has gained more and more popularity every day. Cryptos are one of the first applications of blockchain, the technology that many expect to revolutionize the future world in different ways. We aim to investigate what affects the bitcoin price, based on artificial intelligence and, in particular, machine learning. First, we find features impacting bitcoin price via a thorough investigation of the literature. Then, applying machine learning and... 

This study delves into the intricate realm of risk evaluation within the domain of specific financial derivatives, notably options. Unlike other financial instruments, like bonds, options are susceptible to broader risks. A distinctive trait characterizing this category of instruments is their non-linear price behavior relative to their pricing parameters. Consequently, evaluating the risk of these securities is notably more intricate when juxtaposed with analogous scenarios involving fixed-income instruments, such as debt securities. A paramount facet in options risk assessment is the inherent uncertainty stemming from first-order fluctuations in the underlying asset’s volatility. The... 

Classical financial literature believes that people's decisions in financial markets are rational and that asset prices remain at their intrinsic value. On the other hand, behavioral finance literature believes that there are limitations in investors' decision-making and the impact of decisions on emotions, and states that investors' emotions directly affect asset prices. The aim of this research is to investigate which of the famous indicators introduced in the literature as a representative of the emotional behavior of investors has a better performance in predicting the returns of cryptocurrencies. For this purpose, in the first step, the information related to the calculation of three...