Journal of mathematic and modeling in Finance
Volume:1 Issue: 2, Summer - Autumn 2021

According to most  nancial experts, it is not possible to study the predictability of stock prices without considering the risks affecting stock returns. On the other hand, identifying risks requires determining the share of risk in the total risk and the probability of risk occurrence in different regimes. Accordingly, different DMA models with full dynamics compared to TVP-BMA, BMA and TVP models have been used in the present study to provide this predictability. Findings showed that the DMA model is more efficient than other research models based on MAFE and MSFE indices. The present research was conducted in the period of 1-2003 to 12-2013 (including 144 periods) and was implemented in MATLAB 2014 software space. As the research results show, the bank interest rate coefficient in 45 periods, the  rst lag rate of the bank in terest rate in 37 periods, the in ation rate coefficient in 17 periods,  rst lag coefficient of in ation rate in 26 periods, oil price coefficient in 78 periods,  rst lag rate of oil price in 85 periods, exchange rate coefficient in 64 periods and  rst lag rate of the exchange rate in 35 periods have a signi cant effect on stock returns. The  nal conclusion shows that the stock variables of oil price and the exchange rate had the highest impact on stock returns during the studied period.

Insurance companies and pension funds which deal with human lifetime are interested in mortality forecasting to minimize the longevity risk. In this paper, we studied the mortality forecasting model based on the age-specific death rates by the usage of the state-space framework and Kalman filtering technique. To capture the volatility of time, the time varying trend has been added to the Lee-Carter (LC) model, which is the benchmark methodology in modeling and forecasting mortality since it was introduced in 1992. So, this model is a random walk with time varying drift (TV). We illustrated the performance of the proposed model using Iranian mortality data over the period 1950–2015. Numerical results show that, both models have good fitness and are tangent. So the TV model acts as well as the LC model, but the TV model has the advantages of fewer calculations and the time-varying drift which can be beneficial in time varying data sets.

In this paper, we considered the long-term health insurance as a sequence of annual health insurance policies. To improve the disadvantages of long-term health insurance, we specify the optimal contract including optimal insurance premiums and optimal insurance coverage for the healthcare costs using a negotiation model. We considered two case of known and unknown initial health state. The predictive model for healthcare costs was determined as a time series and state-contingent models. Since the health state changes over time, the insured tends not only to be insured against risk according to her health state, but also to be insured against reclassification of risk. The insurer also seeks a fair premium appropriate to the insured's risk. To achieve this, we determined the optimal contract based on the negotiation model, in which the negotiation parameter is calculated based on the Nash solution. The optimal premium is independent of health state so that the insured is safe against reclassification. However, the insurer coverage is state-contingent and protects the insurer from detriment. Moreover, due to the uncertainty in estimating the parameters of the prediction model, we specified the projection interval by using the bootstrap method for optimal insurance premiums in the coming years. Thus, the insured is aware of the premium intervals at the time of signing the contract with the insurer.

‎An important question in non life insurance research is the ‎estimation of number of future payments and corresponding ‎amount of them. A ‎loss reserve is the money set aside by insurance companies to pay ‎policyholders claims on their policies. The policyholder behavior for reporting claims after its ‎occurrence have significant effect on the costs of the insurance ‎company. This article considers the problem of predicting the amount and number ‎of claims that have been incurred but not reported, ‎say IBNR‎. ‎Using the delay probabilities in monthly level, ‎calculated by the Zero Inflated Gamma Mixture distribution, ‎it predicts IBNR's‎ ‎loss reserve. ‎‎The model advantage in the IBNR reserve is insurers can predict ‎the number of future claims for each future date. ‎This enables ‎them to change the claim reporting process. The practical applications of our findings are applied against a third party liability (TPL) insurance loss portfolio. Additional information about claim can be considered in the loss reserving ‎model and making the prediction of amount more accurate.

Identifying the structures of dependence between financial assets is one of the interesting topics to researchers. However, there are challenges to this purpose. One of them is the modelling of heavy tail distributions. Distributions of financial assets generally have heavier tails than other distributions, such as exponential distributions. Also, the dependence of financial assets in crashes is stronger than in booms and consequently the skewed parameter in the left tail is more.To address these challenges, there is a function called Copula. So, copula functions are suggested for modelling dependency structure between multivariate data without any assumptions on marginal distributions, which they solve the problems of dependency measures such as linear correlation coefficient. Also, tail dependency measures have analytical formulas with copula functions. In general, the copula function connects the joint distribution functions to the marginal distribution of every variables.With regard, we have introduced a factor copula model that is useful for models where variables are based on latent factor structures. Finally, we have estimated the parameters of factor copula by Simulated method of Moment, Newton-Raphson method and Robbins-Monroe algorithm and have compared the results of these methods to each other.

The economic downturn in recent years has had a significant negative impact on corporates performance. In the last two years, as in the last years of 2010s, many companies have been influenced by the economic conditions and some have gone bankrupt. This has led to an increase in companies' financial risk. One of the significant branches of financial risk is the emph{company's credit risk}. Lenders and investors attach great importance to determining a company's credit risk when granting a credit facility. Credit risk means the possibility of default on repayment of facilities received by a company. There are various models for assessing credit risk using statistical models or machine learning. In this paper, we will investigate the machine learning task of the binary classification of firms into bankrupt and healthy based on the emph{spectral graph theory}. We first construct an emph{adjacency graph} from a list of firms with their corresponding emph{feature vectors}. Next, we first embed this graph into a one-dimensional Euclidean space and then into a two dimensional Euclidean space to obtain two lower-dimensional representations of the original data points. Finally, we apply the emph{support vector machine} and the emph{multi-layer perceptron} neural network techniques to proceed binary emph{node classification}. The results of the proposed method on the given dataset (selected firms of Tehran stock exchange market) show a comparative advantage over PCA method of emph{dimension reduction}. Finally, we conclude the paper with some discussions on further research directions.

One of the longest-lasting controversies in the international macroeconomic literature is the purchasing power parity theory. It is the most controversial subject that has been tested with various econometric models in different timeframes and geographic data sets. It is a common assumption used regarding the exchange rate and the validity of the Law of One Price. The present article aimed to present a new model to estimate the fair value of exchange rate which is one of the most critical factors in trade balance among countries, based on balanced trade-monetary theory by assessing the under or over-valuation of currencies. We can assume that a country with a strong economy should have strong money and vice versa. The results showed undervaluation of the dollar versus Yuan, Pound and Yen by 1.41, 1.149, and 1.126 times, respectively in 2018. Therefore, among the U.K., China, and Japan, Japan and the U.K. had a better trade balance with the U.S. than China

In this paper, we discuss the calibration of the geometric Brownian motion model equipped with Markov-switching factor. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the expectation-maximization algorithm. We also implement an empirical application to evaluate the performance of the suggested model. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the expectation-maximization algorithm. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the expectation-maximization algorithm.

This study aims to optimize the portfolio using the genetic operator and network centralization. The statistical population of the study is the top 50 companies of Tehran Stock Exchange, in the first quarter of 2021, and to calculate the size of centrality, we used the difference in the overall performance of each company compared to all the top companies, based on a standard hybridization indicator. Then based on the companies’ performance in the capital market, the geometric mean of risk and return of efficient companies are determined, and given the real limitations of the budget, the requirements and expectations of the investors compared to the market’s performance and the risk-free investment, the problem of decision-making for the composition of the investment in the form of a multi-purpose paradigm is formulated. By using the modified optimization algorithm and the genetic algorithm with dual operators, we optimized the investment’s composition. Finally, we use the compound linear regression with data analysis approach to evaluate the effect of individual and systemic operators on determining the investment strategy, and the results represented the positive effect of these two operators.

In this paper, we first present a nonlinear structural model for pricing mortgage-backed securities. These derivatives are considered to be the primary cause of the 2008 financial crisis that was raised in the United States. We focus our work on pass-through mortgages, which pay both the principal and interest to the investors. We begin our work by introducing the factors that affect the market of mortgage-backed securities. Then, by applying some assumptions and conditions to the parameters of the initial model, and without the loss of generality, we show that this model can be greatly simplified. We focus our attention on how the change in interest rates can affect the value of mortgage-backed securities. Various numerical methods can be used to solve the reduced model that is achieved. We ‎adapt the mesh-less method of radial basis functions to solve the reduced model. The numerical results indicate that the method that we have used can capture the market trends in a specific interval.

In this paper, considering risks of a portfolio such as mean return, variance of returns, and moments of higher order as output variables including desirable and undesirable outputs, we introduce a non-radial and slack based score to measure efficiency of portfolios. Using the present measure, ranking of portfolios is provided which is consistent with standard risk-return ratios in finance. We provide illustrations to show the effects of this contribution on the measures of technical efficiency and ranking of portfolios on a sample set of daily prices of banks and credit institutions listed on the first stock market of Tehran Securities Exchange (TSE). The advantage of this paper is to present a model based on stock market returns and risk, which is based on the DEA view of the production possibility set. Of course, in making it, the quadratic property of variance and the origin of coordinates have been used as a moderating point.

Business expansions being engaged in variety of industries in purpose of getting bigger market share, role of corporate governance within the financial decision. One of the important issues in corporate governance is block trading with purpose of control or invest in target firms. If the plan is to acquire majority of shares and decision making, block trade along with paying premium are of great importance. The purpose of this study is to determine factors affecting on premium of block trading of firms listed in Tehran Stock Exchange or Iran Fara Bourse. Due to the significant impact of companies in refining and petrochemical sectors on whole economy and capital market, this kind of firms should have been considered specially. Multivariate regression and ordinary least squares (OLS) method was used to study the model related to the influential factors on the paid premium of the block trading. Finding of the research shows that financial structure, features of block trading, profitability and efficiency are among factors affecting on premium and also the type of company does not effect on premium.