Kamran Pakizeh; Arman Malek; Mahya Karimzadeh khosroshahi; Hasan Hamidi Razi
Abstract
Cryptocurrencies, which are digitally encrypted and decentralized, continue to attract attention of nancial market players across the world. Because of high volatility in cryptocurrency market, predicting price of cryptocurrencies has become one of the most complicated elds in nancial ...
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Cryptocurrencies, which are digitally encrypted and decentralized, continue to attract attention of nancial market players across the world. Because of high volatility in cryptocurrency market, predicting price of cryptocurrencies has become one of the most complicated elds in nancial markets. In this paper, we use Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to predict price of four well-known cryptocurrencies of Bitcoin (BTC), Ethereum(ETH), Litecoin (LTC), and Ripple (XRP). These models are subdivisions of Articial Intelligence, machine learning and data science. The main aim of this paper is to compare the accuracy of above-mentioned models in forecasting time series data, to nd out which model can better predict price in these four cryptocurrencies. 43 variables consisting of 28 technical indicators and t+10 lags were calculated and appended to the Open, High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies. Applying random forest as feature selection, 25 variables werechosen, 24 of them selected as feature (independent variables) and one as a dependent variable. Each attribute value was converted into a relative standard score, followed by Min-max scaling; we compare models and results of Dieblod Mariano test that is used to examine whether the differences in predictive accuracy with these two models are signi cant, reveal that LSTM reaches better accuracy than GRU for BTC and ETH, but both models convey the same accuracy for LTC and XRP.
Hossein Eslami Mofid Abadi; Marzieh Ebrahimi Shaghaghi; Morteza Taherifard
Abstract
This research has been investigated, economy and balance-sheet effects of the money growth rate targeting. According to financial statements of the banking network and national accounts, using dynamic stochastic general equilibrium New Keynesian and statistical data for the period 1991-2019.For estimating ...
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This research has been investigated, economy and balance-sheet effects of the money growth rate targeting. According to financial statements of the banking network and national accounts, using dynamic stochastic general equilibrium New Keynesian and statistical data for the period 1991-2019.For estimating parameters, is used New Keynesian DSGE model and Bayesian method. This paper verify the validity of the model by analyzing the impulse response functions and Brooks and Goleman test. The results of the model indicate that the effect of negative the money growth rate targeting, reduce deposits, reduce loans interest rates, lead to reducing banks' resources, bank lending and then the health of the banks would compromise. In this way, investment and production will be reduced. Also, the effect of stock prices increasing, deposit, loan decrease and investment and production increase. Therefore, this research suggests the policy of negative the money growth rate targeting coincide with the policy of raising interest rates and stock price rising.
Khadijeh Ghorbanidolatabadi; Hasan Ghalibaf Asl
Abstract
This study seeks to investigate the performance as well as the performance consistency of Iranian mutual funds during the current and subsequent periods. To this end, the Capital Asset Pricing Model along with CARHART’s four-factor model have been utilized to analyze the performance and performance ...
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This study seeks to investigate the performance as well as the performance consistency of Iranian mutual funds during the current and subsequent periods. To this end, the Capital Asset Pricing Model along with CARHART’s four-factor model have been utilized to analyze the performance and performance consistency of investment funds. In order to examine persistency, all models are divided into 10 portfolios (10 distributions) based on the performance of the past one year. Then we considered succeeding 12 months later. Our results revealed that mutual funds in Iran have not outperformed the market, but there is performance consistency. This means mutual funds with the best performance (worst performance) will perform the same (better or worse) in the upcoming years. However, the extent of the best and worst performance of mutual funds is not significantly different. The historical performance of mutual funds can, to some extent, explain future performance. Therefore, investors' reliance on the backgrounds of investment funds as a recourse for investment is well justified. In other words, if investors spend on mutual funds with a past outperformance, there is a reasonable assurance to be repeated the past and will be among the winning funds in future periods. The opposite is also true
Abdolsadeh Neisy; Nasrollah Mahmoudpour; Moslem Peymany; Meisam Amiri
Abstract
Pricing catastrophe swap as an instrument for insurance companies risk management, has received trivial attention in the previous studies, but in most of them, damage severities caused by the disaster has been considered to be fixed. In this study, through considering jumps for modeling the occurrence ...
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Pricing catastrophe swap as an instrument for insurance companies risk management, has received trivial attention in the previous studies, but in most of them, damage severities caused by the disaster has been considered to be fixed. In this study, through considering jumps for modeling the occurrence of disasters as in Unger [32] and completing it through considering damages caused by natural disasters as stochastic, an integro-differential model was extracted to value catastrophe swap contracts. In determining the swap price changes, the Ito command was followed and to achieve the catastrophic swap model, the generalization of the Black and Scholes modeling method was used. [3]. With regard to the initial and boundary conditions, extracted model does not have an analytical solution; thus, its answer was approximated using the finite difference numerical method and the effect of considering the damage as stochastic on swap value was analyzed. In addition, the model and the extracted numerical solution were separately implemented on the data about the earthquake damage in the United States and Iran. The results showed that prices will experience a regular upward trend until damage growth, damage severities, and occurrence probability of a catastrophe are not so high that the buyer of the swap is forced to pay compensation to the swap’s seller. Of course, the prices will fall sharply as soon as they reach and cross the threshold.
Kamran Ayati
Abstract
In this article supply demand based on prices volumes are extracted as measure of swaps between two or more indexes by neural network for recommend Market Makers to increase performance of Large Traded Volumes in real time Markets Quotes. Neural network are widely applicable tools for ...
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In this article supply demand based on prices volumes are extracted as measure of swaps between two or more indexes by neural network for recommend Market Makers to increase performance of Large Traded Volumes in real time Markets Quotes. Neural network are widely applicable tools for develop operators performances in financial market applications. In classic economy when an equilibrium was Unbalanced must be a side of supply or demand was over than other one.in more indexes decisions for check balance condition between more than two indexes in real time market a neural network classification trigger is good suggestion. other methods such as indicators oscillators and numerical methods and statistical methods were been slow. The latency of candle data in clients solved by time stamp in log file and export of these triggers can draw by graphical Line or shape in data.an equilibrium point as middle of these balances for pairs of indexes are connected with triangle shape.
Chunhua Feng; Cadavious Jones
Abstract
In this paper, a three coupled Kaldor-Kalecki model with multiple delays is investigated. By means of the generalized Chafee's criterion, some sufficient conditions to guarantee the existence of oscillatory solution for the model are obtained. Computer simulations are provided to demonstrate the proposed ...
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In this paper, a three coupled Kaldor-Kalecki model with multiple delays is investigated. By means of the generalized Chafee's criterion, some sufficient conditions to guarantee the existence of oscillatory solution for the model are obtained. Computer simulations are provided to demonstrate the proposed results.
Sedighe sharifian; Ali R. Soheili; Abdolsadeh Neisy
Abstract
The bond market is an important part of the financial markets . The coupon bonds are issued by companies or banks for increasing capital , and the interest is paid by banks or companies, periodically . In terms of maturities , bonds are divided ...
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The bond market is an important part of the financial markets . The coupon bonds are issued by companies or banks for increasing capital , and the interest is paid by banks or companies, periodically . In terms of maturities , bonds are divided into three categories as follows : short term , medium term , and long term .In this paper , we model the fractional bond pricing under fractional stochastic differential equation . We implement the multiquadric approximation for solving the fractional bond pricing equation . The equation is discretized in the time direction base on modified Riemann-- Liouville derivative and finite difference methods and is approximated by using the multiquadric approximation method in the space direction which achives the semi-- discrete solution . We investigate the unconditional stability and convergence of the proposed method. Numerical results demonstrate the efficiency and ability of the presented method .
Abdulrashid Jamnia; Mohammad Reza Sasouli; Emambakhsh Heidouzahi; Mohsen Dahmarde Ghaleno
Abstract
The capital or stock market along with the money market is one of the most important parts of financial sector of the nation’s economy, providing long-term financing required for efficient production and service activities. The total stock price index as reflector of stock market fluctuation is ...
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The capital or stock market along with the money market is one of the most important parts of financial sector of the nation’s economy, providing long-term financing required for efficient production and service activities. The total stock price index as reflector of stock market fluctuation is important for finance practitioners and policy-makers. Therefore, in this research, a comparative investigation was presented on two superior deep-learning-based models, including long short-term memory (LSTM), and convolutional neural network long short-term memory (CNN)-LSTM, applied for analysing prediction of the total stock price index of Tehran stock exchange (TSE) market. The complete dataset utilized in the current analysis covered the period from September 23, 2011 to June 22, 2021 with a total of 3,739 trading days in the TSE market. Forecasting accuracy and performance of the two proposed models were appraised using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) criteria. Based on the results, the CNN-LSTM showed the lowest values of the aforementioned metrics compared to the LSTM model, and it was found that the CNN-LSTM model could be effective in providing the best prediction performance of the total stock price index on the TSE market. Eventually, graphically and numerically, various prediction results obtained from the proposed models were analysed for more comprehensive analysis.
Maryam Esna-Ashari; Farzan Khamesian; Farbod Khanizadeh
Abstract
Given the significant increase in fraudulent claims and the resulting financial losses, it is important to adopt a scientific approach to detect and prevent such cases. In fact, not equipping companies with an intelligent system to detect suspicious cases has led to the ...
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Given the significant increase in fraudulent claims and the resulting financial losses, it is important to adopt a scientific approach to detect and prevent such cases. In fact, not equipping companies with an intelligent system to detect suspicious cases has led to the payment of such losses, which may in the short term lead to customer happiness but eventually will have negative financial consequences for both insurers and insured. Since data labeled fraud is really limited, this paper, provides insurance companies with an algorithm for identifying suspicious cases. This is obtained with the help of an unsupervised algorithm to detect anomalies in the data set. The use of this algorithm enables insurance companies to detect fraudulent patterns that are difficult to detect even for experienced experts. According to the outcomes, the frequency of financial losses, the time of and the type of incident are the most important factors to in detecting suspicious cases.
Farzad Jafari; Amir Hamooni; Saeid Tajdini; Mohammad Qezelbash; Niloufar Ebrahimiyan
Abstract
In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic ...
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In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic indicators including inflation, and GDP per capita. And then we modeled the effect of widespread Russian economic sanctions on the value of the Russian ruble during the turbulent days of February 9 to April 9. Our research results show that there is not much difference between market value and fair value in the long run. Also, by observing the behavior of the ruble during the turbulent days of February 25, 2022, to April 26, 2022, and by entering the conditional risk factor and weighted average of the ruble, the USD to ruble equality between 76.23 and 91.6 was evaluated
Tayebeh Nasiri; Ali Zakeri; Azim Aminataei
Abstract
We consider European style options with risk-neutral parameters and time-fractional Levy diffusion equation of the exponential option pricing model in this paper. In a real market, volatility is a measure of the quantity of inflation in asset prices and changes. This makes it essential to accurately ...
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We consider European style options with risk-neutral parameters and time-fractional Levy diffusion equation of the exponential option pricing model in this paper. In a real market, volatility is a measure of the quantity of inflation in asset prices and changes. This makes it essential to accurately measure portfolio volatility, asset valuation, risk management, and monetary policy. We consider volatility as a function of time. Estimating volatility in the time-fractional Levy diffusion equation is an inverse problem. We use a numerical technique based on Chebyshev wavelets to estimate volatility and the price of European call and put options. To determine unknown values, the minimization of a least-squares function is used. Because the obtained corresponding system of linear equations is ill-posed, we use the Levenberg-Marquardt regularization technique. Finally, the proposed numerical algorithm has been used in a numerical example. The results demonstrate the accuracy and effectiveness of the methodology used.
Mahdieh Aminian Shahrokhabadi; Hossein Azari
Abstract
This article's primary goal is to compute an explicit transmutation-based solution to a degenerate hyperbolic equation of second order in terms of time. To reduce a new problem to a problem that has already been solved, or at the very least to a smaller problem, is ...
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This article's primary goal is to compute an explicit transmutation-based solution to a degenerate hyperbolic equation of second order in terms of time. To reduce a new problem to a problem that has already been solved, or at the very least to a smaller problem, is a standard mathematics strategy known as the transmutations method. similar to utilizing heat equations to solve wave equations. Using transmutation methods, we solve this problem using the well-known Kolmogorov equation. We present the solution of wave equations using transmutation methods and show that it is equivalent to the solution obtained by applying the Fourier transform in order to support our methodology.
Fatemeh Samadi; Hossein Eslami Mofid Abadi
Abstract
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 ...
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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.
Reza Raei; Alireza Najjarpour
Abstract
This research has three main goals. The first goal is to investigate the contagion of the risk from the financial sector to other industries. The second objective is to examine the impact of the competitiveness of industries on the spread of the risk sequence from the financial sector to the industries, ...
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This research has three main goals. The first goal is to investigate the contagion of the risk from the financial sector to other industries. The second objective is to examine the impact of the competitiveness of industries on the spread of the risk sequence from the financial sector to the industries, and the third objective is to examine the effect of three main industrial indicators, namely, net debt, value spread and investment spread, on the risk contagion from the financial sector to other industries. In this research, a new measurement method of the spillover of the risk sequence from the financial sector to other industries has been introduced as the occurrence of similar conditions, which for each industry in each period is equal to the number of simultaneous occurrences of severe negative returns in that industry and the financial sector. Empirical findings show that the contagion of the risk from the financial sector to other industries was significant and this contagion was greater for competitive industries due to the greater need for external financing. The occurrence of similar conditions in each sector has a positive relationship with the net debt of that industry. Also, there is no relationship between the value spread and the investment spread with the occurrence of similar conditions.
Asma Hamzeh; Faezeh Banimostafaarab; Fatemeh Atatalab
Abstract
The rating of insurance companies is one of the necessary and operational policies to regulate and evaluate the performance of the insurance industry. It informs shareholders, customers, insurers, and even regulatory authorities, as well as formal and informal support bodies, about the current performance ...
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The rating of insurance companies is one of the necessary and operational policies to regulate and evaluate the performance of the insurance industry. It informs shareholders, customers, insurers, and even regulatory authorities, as well as formal and informal support bodies, about the current performance of insurance companies and their capabilities and prospects for the future. The rating of insurance companies in terms of the regulatory indicators and decision-making and implementation of the administrative measures for the companies based on the regulatory rating of each company is one of the needs of the regulatory body. Therefore, doing this properly requires using the indicators in principal areas, weighting them according to their importance, and implementing the model, finally. For this reason, in this study, first, the effective indicators for the regulatory rating of insurance companies were identified using documentary studies and relevant writings, and the initial indicators were scrutinized and completed using the results of a questionnaire. Then, the indicators prioritization and weighting and implementation of the model for regulatory rating of insurance companies are performed for 2019. Weighting the indicators is done by the Shannon entropy method, and the rating of insurance companies is implemented under three different scenarios with the TOPSIS model and the weighted average method.
Roya Karimkhani; Yousef Edrisi Tabriz; Ghasem Ahmadi
Abstract
Forecasting price trends in financial markets is of particular importance for traders because price trends are inherently dynamic and forecasting these trends is complicated. In this study, we present a new hybrid method based on combination of the dynamic mode decomposition method ...
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Forecasting price trends in financial markets is of particular importance for traders because price trends are inherently dynamic and forecasting these trends is complicated. In this study, we present a new hybrid method based on combination of the dynamic mode decomposition method and long short-term memory method for forecasting financial markets. This new method is in this way that we first extract the dominant and coherent data using the dynamic mode decomposition method and then predict financial market trends with the help of these data and the long short-term memory method. To demonstrate the efficacy of this method, we present three practical examples: closing price of US Dollar to Iranian Rial, closing prices of zob roy Isfahan stock, and also closing prices of siman shargh stock. These examples exhibit bullish, bearish, and neutral behaviors, respectively. It seems that the proposed new method works better in predicting the financial market than the existing long-short-term memory method.
Nooshin Hakamipour
Abstract
The stress-strength model is a commonly utilized topic in reliability studies. In many reliability analyses involving stress-strength models, it is typically assumed that the stress and strength variables are unrelated. Nevertheless, this assumption is often impractical in real-world scenarios. This ...
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The stress-strength model is a commonly utilized topic in reliability studies. In many reliability analyses involving stress-strength models, it is typically assumed that the stress and strength variables are unrelated. Nevertheless, this assumption is often impractical in real-world scenarios. This research assumes that the strength and stress variables follow the Pareto distribution, and a Gumbel copula is employed to represent their relationship. Additionally, the data is gathered through the Type-I progressively hybrid censoring scheme. The method of maximum likelihood estimation is used for point estimation, while asymptotic and Bootstrap percentile confidence intervals are employed for interval estimation of the unknown parameters and system reliability. Simulation is employed to assess the effectiveness of the suggested estimators. Subsequently, an actual dataset is examined to showcase the practicality of the stress-strength model. Simulation is employed to assess the effectiveness of the suggested estimators. Subsequently, a real dataset is examined to demonstrate the practicality of the stress-strength model.
Parisa Nabati
Abstract
This paper presents a nonlinear autoregressive model with Ornstein Uhlenbeck processes innovation driven with white noise. Notations and preliminaries are presented about the Ornstein Uhlenbeck processes that have important ...
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This paper presents a nonlinear autoregressive model with Ornstein Uhlenbeck processes innovation driven with white noise. Notations and preliminaries are presented about the Ornstein Uhlenbeck processes that have important applications in finance. The parameter estimation for these processes is constructed from the time continuous likelihood function that leads to an explicit maximum likelihood estimator. A semiparametric method is proposed to estimate the nonlinear autoregressive function using the conditional least square method for parametric estimation and the nonparametric kernel approach by using the nonparametric factor that is derived by a local L2-fitting criterion for the regression adjustment estimation. Then the Monte Carlo numerical simulation studies are carried out to show the efficiency and accuracy of the present work. The mean square error (MSE) is a measure of the average squared deviation of the estimated function values from the actual ones. The values of MSE indicate that the innovation in noise structure is performed well in comparison with the existing noise in the nonlinear autoregressive models.
Payam Hanafizadeh; Hadiseh Salmani
Abstract
In this study, Robust Net Present Value (RNPV) has been developed for evaluation of projects with infinite life. In this method, the changes of uncertain net incomes in a financial cash flow are postulated in a convex, continuous, and closed region. It has been indicated that RNPV, in the infinite life ...
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In this study, Robust Net Present Value (RNPV) has been developed for evaluation of projects with infinite life. In this method, the changes of uncertain net incomes in a financial cash flow are postulated in a convex, continuous, and closed region. It has been indicated that RNPV, in the infinite life horizon, is calculable only when the net incomes are uncorrelated. Compared to traditional methods, this study considers the variance matrix of net incomes, takes uncertainty into account during the evaluation of investment projects with infinite life period. One important finding when using this method is that one does not need to calculate the covariance matrix in the evaluation of projects with infinite life. The only requirement is to estimate the value of maximum variance for the given financial cash flow. The proposed method is also easy to both calculate and understand in practice. MATLAB software is used for implementation. Lastly, the features of the developed method have been analyzed using some numerical examples for a project with infinite lifetime.
Marzieh Vahdani; Ali Safdari
Abstract
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. ...
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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.
Hadi Bagherzadeh Valami; Zeinab Sinaei nasab
Abstract
In the process of evaluating the Decision Making Units, two factors of efficiency and production size can be used. When the production size of a unit is not optimal, its Returns To Scale (RTS) determines that changing the resources in anotherdirection would enhance its productivity. In most previous ...
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In the process of evaluating the Decision Making Units, two factors of efficiency and production size can be used. When the production size of a unit is not optimal, its Returns To Scale (RTS) determines that changing the resources in anotherdirection would enhance its productivity. In most previous research, RTS is considered to be increasing or decreasing, and frontier analysis is used to determine it. The concept of RTS in Network Data Envelopment Analysis (DEA) is so interesting. In this paper a method based on Most Productive Scale Size (MPSS) in several steps is developed, in addition to determining that RTS of units for each unit in directional manner, the shortest changes in resources for achieving the right size for network production is also obtained. In this approach, the computational complexity, and the ambiguity in units RTS is not present.
Monireh Riahi; Felix Kuebler; Abdolali Basiri; Sajjad Rahmany
Abstract
In this paper, we address the problem of analyzing and computing all steady states of an overlapping generation (OLG) model with production and many generations. The characterization of steady states coincides with a geometrical representation of the algebraic variety of a polynomial ideal, and, in principle, ...
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In this paper, we address the problem of analyzing and computing all steady states of an overlapping generation (OLG) model with production and many generations. The characterization of steady states coincides with a geometrical representation of the algebraic variety of a polynomial ideal, and, in principle, one can apply computational algebraic geometry methods to solve the problem. However, it is infeasible for standard methods to solve problems with a large number of variables and parameters. Instead, we use the specific structure of the economic problem to develop a new algorithm that does not employ the usual steps for the computation of Grobner basis such as the computation of successive S-polynomial and expensive division.
Mehdi Rezaei; Najmeh Neshat; Abbasali Jafari Nodoushan; Amir Mohammad Ahmadzade semeskande
Abstract
In this research, we investigated the interactive effects between the macroeconomic variables of currency, gold, and oil on two indicators of total and equal weighted indices considering the importance of correlation between economic variables and stock market indices. ...
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In this research, we investigated the interactive effects between the macroeconomic variables of currency, gold, and oil on two indicators of total and equal weighted indices considering the importance of correlation between economic variables and stock market indices. In this regard, the analysis of Pearson correlation and regression coefficients have been used to investigate the existence of an interactive effect among them, and a Multi-Layer Perceptron Neural Network (MLP NN) model has been used to simulate this effect. The models have been fitted as a time series based on the daily data related to the economic variables and the mentioned indicators during march 2016 to that of 2021. Investigating the interactive effects between variables has been done using SPSS statistical software, and Artificial Neural Network (ANN) simulation developed in MATLAB programming environment. The extracted results indicate the existence of an interactive effect among these economic variables. The simulation results show the high ability of ANN in modeling and predicting the total price and equal-weighted indices, and this model has been able to make more accurate predictions by considering these interactive effects as well.
Saeed Vahdati; Foad Shokrollahi
Abstract
This article proposes a new numerical technique for pricing asset-or-nothing options using the Black-Scholes partial differential equation (PDE). We first use the θ−weighted method to discretize the time domain, and then use Haar wavelets to approximate the functions and derivatives with ...
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This article proposes a new numerical technique for pricing asset-or-nothing options using the Black-Scholes partial differential equation (PDE). We first use the θ−weighted method to discretize the time domain, and then use Haar wavelets to approximate the functions and derivatives with respect to the asset price variable. By using some vector and matrix calculations, we reduce the PDE to a system of linear equations that can be solved at each time step for different asset prices. We perform an error analysis to show the convergence of our technique. We also provide some numerical examples to compare our technique with some existing methods and to demonstrate its efficiency and accuracy.
Atefeh Kanani Dizaji; Amir Teimour Payandeh Najafabadi; Mohammad Zokaei
Abstract
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 ...
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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.
