Abbas Shekari Firouzjaie; Navid Sahebjamnia; Hadi Abdollahzade
Abstract
Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research. Perishable products have a short life and due to their deterioration over time, they cause great damage if not managed. Many ...
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Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research. Perishable products have a short life and due to their deterioration over time, they cause great damage if not managed. Many industries, retailers, and service providers have the opportunity to increase their revenue through optimal pricing of perishable products that must be sold within a certain period. In the pricing issue, a seller must determine the price of several units of a perishable or seasonal product to be sold for a limited time. This article examines pricing policies that increase revenue for the sale of a given inventory with an expiration date. Booster learning algorithms are used to analyze how companies can simultaneously learn and optimize pricing strategy in response to buyers. It is also shown that using reinforcement learning we can model a demand-dependent problem. This paper presents an optimization method in a model-independent environment in which demand is learned and pricing decisions are updated at the moment. We compare the performance of learning algorithms using Monte Carlo simulations.
Saman Vahabi; Amir Teimour Payandeh Najafabadi
Abstract
In this paper, we design a pure-endowment insurance contract and obtain the optimal strategy and consumption for a policyholder with CRRA utility function. In this contract, premiums are received from the policyholder at certain times. Theinsurer undertakes to pay the premiums by a certain guarantee ...
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In this paper, we design a pure-endowment insurance contract and obtain the optimal strategy and consumption for a policyholder with CRRA utility function. In this contract, premiums are received from the policyholder at certain times. Theinsurer undertakes to pay the premiums by a certain guarantee rate, in addition, by investing in a portfolio of risky and risk free assets share invest pro ts. We used Variance Gamma process as a representative of in nite activity jump modelsand sensitivity of jump parameters in an uncertainty nancial market has been studied. Also we compared results using by two forces of mortality.
Mahdi Goldani
Abstract
Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the 30-day closing prices of APPLE in a select group of 100 prominent companies ...
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Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the 30-day closing prices of APPLE in a select group of 100 prominent companies chosen based on their revenue profiles. list of 100 big Companies published by The Fortune Global 500. The evaluated forecasting methods encompass a broad spectrum of approaches, including Moving Average (MA), Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Simple Linear Regression, Multiple Regression, Decision Trees, Random Forests, Neural Networks, and Support Vector Regression (SVR). The information on the dataset was downloaded from Yahoo Finance, and all methods were evaluated in Python. The MAPE method is used to measure the accuracy of the examined methods. Based on the selected dataset, Our findings reveal that SVR, Simple Linear Regression, Neural Networks, and ARIMA consistently outperform other methods in accurately predicting the 30-day APPLE closing prices. In contrast, the Moving Average method exhibits subpar performance, primarily due to its inherent limitations in accommodating the intricate dynamics of financial data, such as trends, seasonality, and unexpected shocks. In conclusion, this comprehensive analysis enhances our understanding of forecasting techniques and paves the way for more informed and precise decision-making in the ever-evolving realm of financial markets.
Fatemeh Atatalab; Amir Teimour Payandeh Najafabadi
Abstract
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 ...
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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.
Parviz Nasiri; Roghaieh Kheirazar; Abbas Rasouli; Ali Shadrokh
Abstract
In this article, according to the importance of the hazard rate function criterion in theevaluation of statistical distributions, its estimation methods are presented. Here, we suggestestimators for the hazard rate function. First, we use the standard deconvolution kerneldensity estimator and suggest ...
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In this article, according to the importance of the hazard rate function criterion in theevaluation of statistical distributions, its estimation methods are presented. Here, we suggestestimators for the hazard rate function. First, we use the standard deconvolution kerneldensity estimator and suggest a plug-in estimator. In the following we investigate asymptoticbehavior of our estimator. For another estimator, we construct the new estimation thehazard rate function according plug-in and CDF. Finally, we consider the performance ofthe suggested estimators by simulation. Mean square error of estimators λˆ(t, p), λˆ(t) and λˆc(t) present in tables 1 till 6.
Mahboubeh Aalaei
Abstract
In this paper, fuzzy set theory is implemented to model internal rate of return for calculating the price of life settlements. Deterministic, probabilistic and stochastic approaches is used to price life settlements in the ...
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In this paper, fuzzy set theory is implemented to model internal rate of return for calculating the price of life settlements. Deterministic, probabilistic and stochastic approaches is used to price life settlements in the secondary market for the Iranian insurance industry. Research findings were presented and analyzed for whole life insurance policies using the interest rates announced in the supplement of Regulation No. 68 and Iranian life table, which recently has been issued to be used by insurance companies. Also, the results of three approaches were compared with surrender value, which indicates the surrender value is lower than the fuzzy price calculated based on the probabilistic and stochastic approaches and it is higher than the price calculated based on the deterministic approach. Therefore, selling life settlements in the secondary market in Iran based on calculated fuzzy price using probabilistic and stochastic approaches will benefit the policyholder. Also, the price is obtained in the form of an interval using the fuzzy sets theory and the investor can decide which price is suitable for this policy based on financial knowledge. Furthermore, in order to show validity of the proposed fuzzy method, the findings are compared to the results of using the random internal rate of return.
Mahdi Pourrafiee; S. M. Esmaeil Pourmohammad Azizi; Marzieh Mohammadi Larijani; Ali Pahlevannezhad
Abstract
According to the rule of equality of equal prices, the price of a foreign commodity within a country depends on the price of the commodity at the origin as well as the exchange rate of that country. According to this rule, if the foreign exchange costs are insignificant, the price of a single commodity ...
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According to the rule of equality of equal prices, the price of a foreign commodity within a country depends on the price of the commodity at the origin as well as the exchange rate of that country. According to this rule, if the foreign exchange costs are insignificant, the price of a single commodity will be the same everywhere in terms of price, and ideally the purchasing power of a currency inside and outside the country will be the same. Due to the effect of the exchange rate on financial assets, study of regime change in exchange rate fluctuations is importance and Regime Switching model is the most complete and populare regime change. The aim of this research is to modeling Euro-Rial exchange rate under the model of Markov regime switching and Markov random regime switching model. In order to evaluate the achieved results, unit root test, which included the Dickey-Fuller test and the Phillips-Peron test, is used to estimates Markov regime switching and Markov random regime switching parameters in order to find the best fluctuations model.
Erfan Salavati; Nazanin Mohseni
Abstract
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, ...
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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.
Robabeh Hosseinpour Samim Mamaghani; Farzad Eskandari
Abstract
In this paper, we considered a Bayesian hierarchical method using the hyper product inverse moment prior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesian variable selection. We showed the posterior probabilities of the true model converge to 1 as the sample ...
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In this paper, we considered a Bayesian hierarchical method using the hyper product inverse moment prior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesian variable selection. We showed the posterior probabilities of the true model converge to 1 as the sample size increases. For computing the posterior probabilities, we implemented the Laplace approximation. The Simpli ed Shotgun Stochastic Search with Screening (S5) procedure for generalized linear model was suggested for exploring the posterior space. Simulation studies and real data analysis using the Bayesian ultrahigh-dimensional generalized linear model indicate that the proposed method had better performance than the previous models. Keywords: Ultrahigh dimensional; Nonlocal prior; Optimal
Matin Abdi; Seyyed Babak Ebrahimi; Amir Abbas Najafi
Abstract
An online portfolio selection algorithm has been presented in this research. Online portfolio selection algorithms are concerned with capital allocation to several stocks to maximize the portfolio return over the long run by deciding the optimal portfolio in each period. Despite other online portfolio ...
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An online portfolio selection algorithm has been presented in this research. Online portfolio selection algorithms are concerned with capital allocation to several stocks to maximize the portfolio return over the long run by deciding the optimal portfolio in each period. Despite other online portfolio selection algorithms that follow Kelly's theory of capital growth and only focus on increasing return in the long term, this algorithm uses the beta risk parameter to exploit upside risk while hedging downside risk. This algorithm follows the pattern-matching approach, uses fuzzy clustering in the sample selection step, and the log-optimal objective function along with the transaction cost and considering the beta risk measure in the portfolio optimization step. The implementation of the proposed algorithm in this research on a 10-stock dataset from the NYSE market in the period of December 2021 to December 2022 shows the superiority of this algorithm in terms of return and risk and the overall Sharpe ratio compared to the algorithms proposed previously in the literature on online portfolio selection.
Ali Bolfake; Seyed Nourollah Mousavi; Sima Mashayekhi
Abstract
This paper proposes a new approach to pricing European options using deep learning techniques under the Heston and Bates models of random fluctuations. The deep learning network is trained with eight input hyper-parameters and three hidden layers, and evaluated using mean squared error, correlation coefficient, ...
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This paper proposes a new approach to pricing European options using deep learning techniques under the Heston and Bates models of random fluctuations. The deep learning network is trained with eight input hyper-parameters and three hidden layers, and evaluated using mean squared error, correlation coefficient, coefficient of determination, and computation time. The generation of data was accomplished through the use of Monte Carlo simulation, employing variance reduction techniques. The results demonstrate that deep learning is an accurate and efficient tool for option pricing, particularly under challenging pricing models like Heston and Bates, which lack a closed-form solution. These findings highlight the potential of deep learning as a valuable tool for option pricing in financial markets.
Parisa Karami; Ali Safdari
Abstract
In financial markets , dynamics of underlying assets are often specified via stochasticdifferential equations of jump - diffusion type . In this paper , we suppose that two financialassets evolved by correlated Brownian motion . The value of a contingent claim written on twounderlying assets under jump ...
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In financial markets , dynamics of underlying assets are often specified via stochasticdifferential equations of jump - diffusion type . In this paper , we suppose that two financialassets evolved by correlated Brownian motion . The value of a contingent claim written on twounderlying assets under jump diffusion model is given by two - dimensional parabolic partialintegro - differential equation ( P I D E ) , which is an extension of the Black - Scholes equation witha new integral term . We show how basket option prices in the jump - diffusion models , mainlyon the Merton model , can be approximated using finite difference method . To avoid a denselinear system solution , we compute the integral term by using the Trapezoidal method . Thenumerical results show the efficiency of proposed method .Keywords: basket option pricing, jump-diffusion models, finite difference method.
Hossein Teimoori Faal; Meyssam Bagheri
Abstract
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 ...
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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.
Saeid Tajdini; Farzad Jafari; Majid Lotfi Ghahroud
Abstract
According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification ...
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According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. For inferring non-linear financial time series, various parametric and non-parametric models are generally used. Since the leverage effect refers to the generally negative correlation between an asset return and its volatility, models such as GJRGARCH and EGARCH have been designed to model leverage effects. However, in some cases, like the Tehran Stock Exchange, the results are different in comparison with some famous stock exchanges such as the S&P500 index of the New York Stock Exchange and the DAX30 index of the Frankfurt Stock Exchange. The purpose of this study is to show this difference and introduce and model the "reversed leverage effect bias" in the indices and stocks in the Tehran Stock Exchange.
Mohammad Qezelbash; Saeid Tajdini; Farzad Jafari; Majid Lotfi Ghahroud; Mohammad Farajnezhad
Abstract
In recent years, cryptocurrency has attracted more attention and is a new option in the economy and the financial sector. The purpose of this study is to the volatility and “herd behavior” of the cryptocurrency, gold, and stock markets in the US. This research is aimed at investor “herd ...
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In recent years, cryptocurrency has attracted more attention and is a new option in the economy and the financial sector. The purpose of this study is to the volatility and “herd behavior” of the cryptocurrency, gold, and stock markets in the US. This research is aimed at investor “herd behavior” and how it correlates with the volatility of three assets: the Standard & Poor's 500 indexes, Bitcoin, and gold. Also, A new formula by applying the conditional standard deviation (risk), maximum return, minimum return, and average return to quantify the herding bias is designed in this research. In this study, the generalized autoregressive conditional heteroscedasticity model (GARCH) and the autoregressive moving average model (ARMA) were both employed. Research results show that Bitcoin is 3.3 times as volatile as the S&P 500 and 4.6 times as volatile as gold. The results of this novel equation also show that the herding bias of Bitcoin is more than 26 times higher than the global average and 10 times higher than the S&P 500. Also, it’s important to consider the energy consumption and sustainability of investments when evaluating their long-term viability and risk. In some cases, investments in companies with strong sustainability practices and low carbon footprints may be seen as lower risk. Since Bitcoin relies on a network of computers to validate transactions based on proof of work and it is an energy consumption consensus mechanism, investment in Bitcoin may be seen as a higher risk.
Maziar Salahi; Tahereh Khodamoradi; Abdelouahed Hamdi
Abstract
The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints ...
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The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints and short selling, specifically in the mean-conditional value-at riskframework. It is shown that, subject to certain conditions, this approach leadsto lower standard deviation. Empirical results obtained from experiments onthe SP index data set from 2016-2021 using various numbers of stocks andconfidence levels indicate that the proposed model outperforms existing modelsin terms of Sharpe ratios.
Soudeh Sheybanifar
Abstract
Since noise present in financial series, often as a result of existence of fraudulent transactions, arbitrage and other factors, causes noise in financial data therefore false estimation of the parameters and hence distorts portfolio allocation strategy, in this paper wavelet transform is used for noise ...
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Since noise present in financial series, often as a result of existence of fraudulent transactions, arbitrage and other factors, causes noise in financial data therefore false estimation of the parameters and hence distorts portfolio allocation strategy, in this paper wavelet transform is used for noise reduction in mean-variance portfolio theory. I apply conditional estimation of the mean and variance of returns along with the simple one obtaining “optimal weights” which later combines with smooth and non-smooth series, result in four optimal portfolio weights and therefore four portfolio returns. After this, I impose the non-negativity constraint (for weights) deduced from the Kuhn-Tucker approach to simulate the no short selling circumstance in Tehran Stock Exchange. Weights and portfolio returns changed dramatically in this step but the main result (which asset to hold) did not. Comparing Sharp ratios, I observed that Regardless of the psychological characteristics of the investor, holding the risk-free asset is almost the optimal choice in this case.
Sajad Nezamdoust; Farzad Eskandari
Abstract
The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models is performed from a likelihood ...
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The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models is performed from a likelihood point of view by exploiting the expectation maximization (EM) method and the Least Square Principle. Ridge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis. Accordingly, we propose a new shrinkage ridge estimation approach. Based on this principle, we propose an iterative algorithm called RidgeIterative Weighted least Square (RIWLS) to estimate the parameters. Monte-Carlo simulation studies are conducted to appraise the performance of our method. The results show that the Proposed estimator perform better than the IWLS method.
Saeid Tajdini; Amir Hamooni; Jamal Maghsoudi; Farzad Jafari; Majid Lotfi Ghahroud
Abstract
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 ...
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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
Maryem Jaziri; Afif Masmoudi
Abstract
Given the importance of policyholder classification in helping to make a good decision in predicting optimal premiums for actuaries.This paper proposes, first, an optimal construction of policyholder classes. Second, Poisson-negative Binomial mixture regression model is proposed as an alternative to ...
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Given the importance of policyholder classification in helping to make a good decision in predicting optimal premiums for actuaries.This paper proposes, first, an optimal construction of policyholder classes. Second, Poisson-negative Binomial mixture regression model is proposed as an alternative to deal with the overdispersion of these classes.The proposed method is unique in that it takes Tunisian data and classifies the insured population based on the K-means approach which is an unsupervised machine learning algorithm. The choice of the model becomes extremely difficult due to the presence of zero mass in one of the classes and the significant degree of overdispersion. For this purpose, we proposed a mixture regression model that leads us to estimate the density of each class and to predict its probability distribution that allows us to understand the underlying properties of our data. In the learning phase, we estimate the values of the model parameters using the Expectation-Maximization algorithm. This allows us to determine the probability of occurrence of each new insured to create the most accurate classification. The goal of using mixed regression is to get as heterogeneous a classification as possible while having a better approximation. The proposed mixed regression model, which uses a number of factors, has been evaluated on different criteria, including mean square error, variance, chi-square test and accuracy. According to the experimental findings on several datasets, the approach can reach an overall accuracy of 80%. Then, the application on real Tunisian data shows the effectiveness of using the mixed regression model.
Zahra Pourahmadi; Dariush Farid; Hamid Reza Mirzaei
Abstract
Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in ...
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Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in financial markets, the FTS utilizes reinforcement learning (RL), a subset of AI, for training. The model incorporates technical analysis and a constrained policy to enhance decision-making capabilities. The proposed algorithm is applied to the Tehran Securities Exchange, evaluating its efficiency across 45 periods using three different stock market indices. Performance comparisons are made against common strategies such as buy and hold, randomly selected actions, and maintaining the initial stock portfolio, with and without transaction costs. The results indicate that the FTS outperforms these methods, exhibiting excellent performance metrics including Sharp ratio, PP, PF, and MDD. Consequently, the findings suggest that the FTS serves as a valuable asset management tool in the Iranian financial market.
emad koosha; Mohsen Seighaly; Ebrahim Abbasi
Abstract
The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to ...
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The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to the nature of price behavior in financial markets, have been reported to be well in studies. In this respect, measuring and comparing the accuracy and precision of random forest (RF), long-short-term memory (LSTM), and recurrent neural network (RNN) models in predicting the top and bottom of Bitcoin prices are the main objectives of the present study. The approach to predicting top and bottom prices using machine learning models can be considered as the innovative aspect of this research, while many studies seek to predict prices as time series, simple, or logarithmic price returns. Pricing top and bottom data as target variables and technical analysis indicators as feature variables in the 1-hour time frame from 1/1/2018 to 6/31/2022 served as input to the mentioned models for learning. Validation and testing are presented and used. 70% of the data are considered learning data, 20% as validation data, and the remaining 10% as test data. The result of this research shows over 80% accuracy in predicting the top and bottom Bitcoin price, and the random forest model’s prediction is more accurate than the LSTM and RNN models.
Farshid Mehrdoust; Idin Noorani; Mahdi Khavari
Abstract
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 ...
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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.
Ali Tamoradi; Zoleikha Morsaliarzanagh; Zeinab Rezaei; Ebrahim Abbasi
Abstract
The present study aims to investigate the effect of corporate irresponsibility on stock price crash risk by emphasizing the moderating role of financial expertise of the audit committee in companies listed on the Tehran Stock Exchange. To estimate the multiple regression model to test the hypothesis, ...
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The present study aims to investigate the effect of corporate irresponsibility on stock price crash risk by emphasizing the moderating role of financial expertise of the audit committee in companies listed on the Tehran Stock Exchange. To estimate the multiple regression model to test the hypothesis, the aforementioned model was used using panel data by the pooled data method in companies listed on the Tehran Stock Exchange and Eviews 9 statistical software was used for statistical analysis. In this research, 150 companies (1050 company-years) were selected to test the research hypothesis between 2014 and 2020. The Levin, Lien and Wu tests were used to test the reliability of research data, the Jarque-Bera test was used to determine the normality of the data, the regression method was used to express the relationship between variables, t-test statistics to test the significance of regression coefficients, and finally the F-test statistic was used to determine the significance of the equation. In general, the results of testing the research hypotheses indicate that corporate irresponsibility has a significant positive effect on stock price crash risk. The results also show that the financial expertise of the audit committee has a significant moderating effect on the relationship between corporate irresponsibility and stock price crash risk. In fact, the financial expertise of the audit committee reduces the positive relationship between corporate irresponsibility and stock price crash risk.
Ali R. Soheili; Yasser Taherinasab; Mohammad Amini
Abstract
In this paper, we analyze the strong convergence and stability of the Compensated Splite-step $theta$ (CSS$theta$) and Forward-Backward Euler-Maruyama (FBEM) methods for Numerical solutions of Stochastic Differential Equations with jumps (SDEwJs),where $sqrt{2}-1leqthetaleq 1$. The drift term ...
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In this paper, we analyze the strong convergence and stability of the Compensated Splite-step $theta$ (CSS$theta$) and Forward-Backward Euler-Maruyama (FBEM) methods for Numerical solutions of Stochastic Differential Equations with jumps (SDEwJs),where $sqrt{2}-1leqthetaleq 1$. The drift term $f$ has a one-sided Lipschitz condition, the diffusion term $g$ and jump term $h$ satisfy global Lipschitz condition. Furthermore, we discuss about the stability of SDEwJs with constant coefficients and present new useful relations between their coefficients. Finally we examine the correctness and efficiency of theorems with some examples.In this paper, we analyze the strong convergence and stability of the Compensated Splite-step $theta$ (CSS$theta$) and Forward-Backward Euler-Maruyama (FBEM) methods for Numerical solutions of Stochastic Differential Equations with jumps (SDEwJs),where $sqrt{2}-1leqthetaleq 1$. The drift term $f$ has a one-sided Lipschitz condition, the diffusion term $g$ and jump term $h$ satisfy global Lipschitz condition. Furthermore, we discuss about the stability of SDEwJs with constant coefficients and present new useful relations between their coefficients. Finally we examine the correctness and efficiency of theorems with some examples.
