bayesian method

V, a loss reserve is a prediction of the amount an insurer will need to pay for future claims. Researchers have been exploring methods to incorporate dependencies among multiple loss triangles to improve the accuracy of outstanding claim prediction. This study aims to predict outstanding claims in dependent run-off triangles by considering the dependence among the outstanding claims paid in each run-off triangle.

The study considers the dependence among corresponding outstanding claims in run-off triangles related to different lines of insurance. It also takes into account the calendar year of payment of claims, in addition to factors such as the year of claim occurrence and the number of years of delay in payment. Two methods are used to model the inter-triangular and intra-triangular dependencies. The first method involves modeling the dependence among triangles by using a multivariate distribution for outstanding claims in the corresponding cells of run-off triangles. The calendar dependence within each run-off triangle is incorporated by adding a calendar year effect factor to the mean of the outstanding claims distribution. The second method uses a multivariate distribution for the outstanding claims of the calendar years corresponding to run-off triangles, capturing both types of dependence. Bayesian approach and Hamiltonian Monte-Carlo sampling methods are employed to estimate model parameters.

The study utilizes data from an Iranian insurance company on outstanding claims in car body insurance and third-party car insurance from 2012 to 2015. The two methods of calendar dependence modeling are compared using a scale mixture multivariate distribution with normal marginal distributions and copula dependence. The mean absolute percentage error is used to measure the accuracy of the prediction. The results show that using a multivariate distribution for calendar dependence modeling leads to a more accurate prediction compared to adding the calendar year effect factor to the mean model.

Based on the findings, it is concluded that modeling the calendar dependence among outstanding claims in run-off triangles using a multivariate distribution improves the accuracy of reserves prediction compared to using the calendar year effect factor. This approach can enhance the prediction of outstanding claims and contribute to the insurer's profitability and solvency.

Examining the dynamics of oil commodity prices at different times is of particular importance, as fluctuations in commodity prices can be a serious problem for policymakers. Monetary policy shocks are among the factors that can affect the price of petroleum products. Therefore, identifying as accurately as possible the factors affecting price changes can be of great help to policymakers. Due to this, the purpose of the present study is to investigate the effect of monetary policy shocks on price dynamics of selected petroleum products group in Iran using Bayesian Var model in the period of 2008-1 to 2017-4. Selected petroleum products include bitumen, slack wax oil and sulfur. The results showed that in the long run, liquidity, the price of slack wax oil and the price of sulfur had the greatest impact on the price of bitumen, slack wax oil and sulfur, respectively. Interest rates have been studied the least impact on commodities.

The concept of financial sustainability has been in the economic literature for nearly two decades. In the theoretical financial literature, firm financial sustainability can be described as a financial system consisting of financial intermediaries, markets, and market infrastructures that are capable of withstanding risk shocks and resolving financial imbalances. Therefore, according to the above argument, the purpose of the present study is to present a proposed model for measuring the financial sustainability of the company by using Econophysics and artificial neural network using a sample of 132 companies listed in Tehran Stock Exchange during 2015-2019. The results of the first hypothesis show that the prediction of financial sustainability based on the Econophysics method can provide better results. Also, based on the result of the second hypothesis, the Bayesian method can predict better financial sustainability. Finally, by comparing the Econophysics and Bayesian approaches to predicting financial sustainability of the firm, it can be concluded that the prediction of financial sustainability based on the economophysical method yields better results than the Bayesian method.

Monetary policy shocks are among the factors that can affect the prices of industrial goods. Therefore, determining the factors which affect price changes might come in handy for policymakers. Considering this, the purpose of this study is to investigate the effect of monetary policy shocks on the price dynamics of selected industrial commodities group in Iran using a Bayesian VAR in the period of 2008-1 to 2017-4. Due to statistical limitations, the selected industrial commodities include aluminum, copper, gold, beams and zinc. The results showed that in long-term the exchange rate had the most impact on the prices of aluminum, gold and beams; copper and zinc have had the most impact on their prices.  The least impact on all goods except beams was through the price of beams and the least impact on the price of beams was from the price of zinc.

Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial economics for modeling and calculating volatility. In the first approach, conditional variance is modeled as a function of the square of the past shocks of return on assets. Models of the GARCH type fall into this category. In the alternative approach, volatility is assumed to be a random variable, which evolves using nonlinear patterns of Gaussian state space. This type of model is known as Stochastic Volatility (SV).  Because, SV models include two kinds of noise processes, one for observations and another for hidden, volatility, thus, they are more realistic and more flexible in calculating volatility than GARCH type.  This study attempts to analyze the volatility in stock returns of 50 companies, which are active in Tehran Stock Market using symmetric and asymmetric methods of Stochastic Volatility, which is different in the presence of leverage effect. The empirical comparison of these two models by calculating the posterior probability of accuracy of each model using the MCMC Bayesian method represents a significant advantage of the ASV model. The results in both symmetric and asymmetric methods represent the very high stability of the volatility generated by the shocks on stock returns; therefore, the Tehran Stock market changes in returns due to this high sustainability will be predictable.

The importance of predicting bankruptcy risk of firms is increasing because of later financial crisis. Despite practical researchers trying to present models for predicting this risk, it seems that an optimum and acceptable model that is reliable for financial statement users and auditors in order to increase their ability in decision making and professional judgment has not been presented yet. Therefore more researches in this region can help to understand financial crisis and bankruptcy risk of firms and finally increase the probability of accessing such a model. This research investigates 31 financial ratios have been used with Bayesian approach to test market information of firms listed in TSE during 1386 to 1396. Finally a model consist of 6 variables (retained earnings to total asset, changes in net income to the sum of absolute value of two years net income, financial leverage, equity book value to debt book value, excess of debt to total asset and loss index) has been designed and presented for predicting this risk. The ability and prediction accuracy of this model has been tested by Receiver Operating Characteristics (ROC) curve and actual information of crisis and non-crisis firms. The results show high precision and accuracy of the model.

Studies in the field of export performance show that the investigation of export performance reveals a wide range of factors affecting export performance and corporate success. On the other hand, the customer-Orientation consists of a set of capabilities that are not easily replicable and can provide a long-term competitive advantage for the companies. This study investigate the effect of customer Orientation on the export performance of companies exporting home appliances, which considering trust, communication and behavioral commitment as a mediator. In this regard, a questionnaire with 38 questions was sent to all identified companies. In this research, due to the non-normality of data and small sample size, Bayesian structural analysis method was used and the method of estimating the parameters in the structure is generalized least squares error method. The results of the analysis of 146 questionnaires obtained using Bayesian Structural Analysis showed that all three variables of trust, communication and behavioral commitment influenced the relationship between customer-orientation and export performance. Results suggest manager to use trust, behavioral commitment, and communication to develop customer orientation and enhancing firm’s export performance.

Financial markets especially capital market can make strong connections with other parts of the economy. After 2007/2008 financial crisis and global extensive economic recession, the economists show interest in the financial markets function again. The purpose of this study is to design and estimate a Dynamic Stochastic General Equilibrium new Keynesian model with Stock market dynamism to investigate the stock market channel effectiveness mechanism on macroeconomics variables. So an open DSGE model containing households, firms, banks, government and central bank was designed and after log-linearization, then the model’s parameters were estimated using quarterly data 1996:3-2013:2 and experimental studies results. The model estimation using Bayesian method results show the effect of stock market on macroeconomic variables but due to the small size of Iran’s capital market the effect is meaningful according to size but the duration is not strong or stable. After all, it’s preferred that Iran’s central bank focus on total index growth rate rather than total index deviations in reaction to a shock in capital market in order to decrease the effects of financial mechanisms on real and nominal macroeconomic variables.

Implementation of monetary policies and adjustments in liquidity are the most important tools of the Central Bank of Iran used in structural reforms in the banking industry. This policy influences the efficiency of the monetary policy through its impact on bank credit channels. In this study we used dynamic stochastic general equilibrium models as one of the most innovative approaches to monetary policy to assess the effects of monetary policy on the economy. We use quarterly data from April of 1990 to June of 2010. We used Bayesian methods to estimate the parameters of DSGE models and used DSGE simulation to measure the effect of monetary shock on the economy and the structure of the banking industry on the efficiency of the monetary policy. This study concludes that monetary expansion resulted in an increase in inflation.The results suggest that the increased competition in attracting deposits during monetary shocks, results in an increased consumption as well as deposits for households. However due to lower level of loans for producers, production increases less. On the other hand, increased competition in lending causes results in a higher increase in household consumption, wage labor, interest rates on deposits and loans. However in these conditions, following a monetary shock and in the initial period exclusive banks compared to competitive banks had a lower increase in concessional lending and thereby at the same time, the positive impacts on employment and production will be lower. In other words, increasing competition in lending in the banking industry results in higher impact of inflation on liquidity growth.

The existing earthquake early warning systems estimate the location and magnitude of earthquakes and then earth movements at a site are estimated as a function of the epicentral distance and soil properties of the site. In the past two decades we have seen a raPid development of methods for earthquake early warning systems. This review study was focused on an earthquake early warning system considering fault limitation. also systems with rupture geometry estimate and slip size on a limited fault in real time were described for earthquake early warning system. In some systems, a method for estimating an earthquake fault dimensions in real time is done by classifying seismic records into near and far source. Peak earth movements were analyzed and used Bayesian model to find a function which can classify near and far source records in the best way based on these parameters. The diagnostic function is useful for estimating fault rupture dimensions in real, especially for large earthquakes. An analytical function for estimating slip on the fault result from near-source earth movement observations is constructed and used.