First Pages
text
article
2017
per
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
https://jferm.khatam.ac.ir/article_66591_08e29967e73bbdbd6ef1b03d7c7bb671.pdf
Intraday Value at Risk Estimation Based on an Asymmetric Autoregressive Conditional Duration Approach
Ahmad
Pouyanfar
Assistant Prof., Finance Department, Khatam University, Tehran, Iran
author
ali
damerloo abhari
MSc. in Finance, Khatam University, Tehran, Iran
author
text
article
2017
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The most important parameter in risk evaluation by intraday value at risk (IVaR) simulation is the irregular spaced high frequency data. There are several methods for model high frequency data and in this paper we propose a method to compute IVaR using real time high frequency transaction data for 10 stock of Tehran Stock Exchange. Transactions durations are modeled by asymmetric autoregressive conditional duration (AACD) and autoregressive conditional duration (ACD) mehhods and IVaR has been calculated by Mont Carlo simulation. Research results show that IVaR calculated using AACD method outperforms. And also results of IVaR calculation shows a daily pattern in IVaR variation. JEL: G23, G32 How to cite this paper: Pouyanfar, A., & Damerloo Abhari, A. (2017). Intraday Value at Risk Estimation Based on an Asymmetric Autoregressive Conditional Duration Approach. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 278–296. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
278
296
https://jferm.khatam.ac.ir/article_66580_2bc4b55a233df5d3093f7616bfe5075e.pdf
Presenting a Model for Measuring Predictability Strength and the Relationship of Stock Index Return and Mutual Fund Flow
Ehsan
Taiebysani
Ph.D., Financial Management, University of Tehran, Iran
author
Saeed
Falahpor
Associate Prof. Financial Management, university of Tehran, Iran
author
text
article
2017
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In this Thesis we examine the dynamic relationship between stock returns and mutual fund flows in Tehran Stock Exchange (TSE) by using VAR model. Afterthat, we checked the impulse response function and forecast error variance decomposition of VAR by Cholesky and generalized function. We find that spillover shocks that is, Tehran Stock Exchange main index (TEDPIX) return shocks and mutual fund flow shocks together explain some percent of the total forecast error variance of stock returns and mutual fund flows. Base on above mentioned results we used Aritificial Neural Network (ANN) with different learning functions for exmaning the relationship between Tehran Stock Exchange main index (TEDPIX) return and mutual fund flow. For selecting the best learning function in ANN, mean square Error has been used. For statistical siginificance, T-statistical paired comparison test was used. We create a spillover index of shocks emanating from stock returns and mutual fund flows and tests whether it can actually predict Tehran returns. We find it does. Using the spillover index, we forecast TEDPIX returns. At the end because of endogeneity, persistency and heteroscedasticity of predicting regression, we used Feasible-Quasi Generalized Least Square (FQGLS) for examining the statistics significance of spillover index, which turns out to be statistically significant. JEL: G17, G23 How to cite this paper: Taiebysani, E., & Fallahpour, S. (2017). Presenting a Model for Measuring Predictability Strength and the Relationship of Stock Index Return and Mutual Fund Flow. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 297–319. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
297
319
https://jferm.khatam.ac.ir/article_66581_28477a12fc15e28e255b1a0e08371277.pdf
Financial Bankruptcy Risk Prediction Based on Accounting, Market and Hybrid Models by using RBF and MLP Neural Networks Technique in TSE
alireza
atefatdoost
Assistant Prof., Business management, shahab danesh University, Qom, Iran
author
maryam
mahmoudi
MSc.in Business Administration, Qom University, Qom, Iran
author
najmeh
ramooz
Assistant Prof., Business management Department, Qom University, Qom, Iran
author
text
article
2017
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In this paper have been studied Financial Bankruptcy Risk Prediction based on accounting and market and hybrid models (combining the two above models) with the use of MLP and RBF techniques of neural networks and the results of these techniques are compared based on the mean square error index in the three mentioned models. The results show that the RBF neural network is more efficient than the MLP network in all three models (accounting, market and hybrid variables) and the accuracy of the hybrid model is more than the accounting and market models. JEL: C45, G17, G33 How to cite this paper: atefatdoost, A., mahmoudi, M., & ramooz, N. (2017). Financial Bankruptcy Risk Prediction Based on Accounting, Market and Hybrid Models (Combination of Two Models)by using RBF and MLP Neural Networks Technique in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 320–339. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
320
339
https://jferm.khatam.ac.ir/article_66583_a89c3d3eed9a29654fbf12309cfce9e0.pdf
The Impact of the Investment Horizon in Optimizing Portfolio using Wavelet and GARCH-COPULA
mohammad ali
Rastegar
Assistant Prof., Financial Engineering,Tarbiat Modares University, Tehran, Iran
author
mohammad
okeinezhad
MSc. Student in financial engineering, Faculty of Management, University of Khatam, Tehran, Iran
author
text
article
2017
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The purpose of our paper is to show how investors can used the multi-scale nature of assets into their portfolio decisions. We decompose weekly return series of 3stocks listed in the Tehran stock exchange Index (TSE) from 2011 to 2014 into different time scales to separate short-term noise from long-run trends. We decompose data by applying wavelet Transform techniques. Then, we apply ARIMA(p,d.q)_GARCH(1,1)_Copula to determine return and Value at Risk (VaR). The process first extracts the filtered residuals and variance from each return series with an ARIMA and asymmetric GARCH model, then constructs the sample marginal cumulative distribution function (CDF) of each asset using a Gaussian kernel estimate for the interior and a generalized Pareto distribution (GPD) estimate for the upper and lower tails. A Student's t copula is then fit to the data and used to induce correlation between the simulated residuals of each asset. Finally, the simulation assesses the Value-at-Risk (VaR) of the equity portfolio over a different horizon. We to get the best weight of each stock in the portfolio. We have identified the best VAR based ratio.In this study, we had predicted a portfolio for each time horizon according to risk and return portfolio. Our results provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. JEL: G32, G11 How to cite this paper: Rastegar, M. A., & Okeinezhad, M. (2017). The Impact of the Investment Horizon in Optimizing Portfolio using Wavelet and GARCH-COPULA. Journal of Risk Modeling and Financial Engineering, 2(3), 340–361. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
340
361
https://jferm.khatam.ac.ir/article_45201_f225298e0897fd571441d97b81624c20.pdf
Optimization of Multi-Objective Portfolios Based on Mean, Variance, Entropy and Particle Swarm Algorithm
Reza
Raei
Prof. Finance Department, Tehran University, Tehran, Iran
author
saeed
bajalan
Assistant Prof,. Finance Department,Tehran University,Tehran, Iran
author
mostafa
habibi
MSc. Student in Finance, Tehran university, Tehran, Iran
author
ali
nikahd
MSc. Student in Finance, Tehran university, Tehran, Iran
author
text
article
2017
per
Most optimization problems in the real world have several goals that are usually in conflict with each other. Investors in the capital market are also pursuing several goals for optimizing the stock portfolio. The purpose of this paper was to optimize multi-objective portfolios based on ARMA-GARCH predictions and entropy. to provide solutions using the method of particle swarm algorithm. The statistical sample of this study includes the top 30 Tehran Stock Exchange (TSE). Initially, Autoregressive integrated moving average (ARIMA) efficiency series was modeled. Then, in order to evaluate the asset portfolio risk, we first calculated the risk based on generalized autoregressive conditional heteroskedasticity (GARCH) models. Also, the results of this study show that the multi-objective optimization algorithm based on the method of particle swarm algorithm is successful in creating stock portfolios. According to the findings of the research, the application of the Particle Swarm Algorithm (PSO) in the selection and optimization of stock portfolios is recommended. JEL: G10, G17, G19 How to cite this paper: Raei, R., Bajelan, S., Habibi, M., & Nikahd, A. (2017). Optimization of Multi-Objective Portfolios Based on Mean, Variance, Entropy and Particle Swarm Algorithm. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 362–379. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
362
379
https://jferm.khatam.ac.ir/article_66584_56dc21ae64a330b372928fbebe0cf2e5.pdf
Asset Allocation Modeling: A Combined Regime-Switching and Black-Litterman Model
Mohammad Mahdi
Mousavi
Associate Prof., Economic Department, Khatam University, Tehran, Iran
author
Shahireh
Naderi
MSc., Finance Management, Khatam University, Tehran, Iran
author
Khadijeh
Hasanlou
Associate Prof., Industrial Engineering Department, Khatam University, Tehran, Iran
author
text
article
2017
per
One of the most debated issues of investment management is the relative importance of asset allocation versus security selection. Regimes changes present a big challenge to traditional asset allocation, demanding a more adaptive approach. The purpose of this study is to develop a framework for dynamic asset allocation modeling in the presence of regime switching. This research builds on previous works to develop a combined regime-switching and Black-Litterman for optimal asset allocation in Iran asset classes considering data during 212 months, ranging from March 1999 to September 2016. The results show the existence of different financial regimes that lead to variable optimal asset allocations across different regimes. Finally, it is suggested that the combination of regime-switching and Black-Litterman model for mixture of stock and 1-year banking deposit investment gives significantly better results than other models in terms of performance and a modified sharp ratio.
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
380
397
https://jferm.khatam.ac.ir/article_45933_e81d0829af61ed9d220f61a7516dfa86.pdf
Empirical Study on the Existence of Long-term Memory in TSE Returns
Ali
Raoofi
Allameh Tabataba'i University
author
taymoor
mohammadi
allameh tabatabaee
author
text
article
2017
per
Over the past few decades, long memory processes were assigned an essential part of the time series analysis. This feature changes the statistical behavior of estimations and predictions drastically. Consequently, many theoretical results and methodologies used in time series with short memory such as ARMA processes are not suitable for long memory models. Therefore, time series memory of Tehran Stock Exchange returns are estimated and interpreted in this paper. To do this, R/S, MRS, and GPH tests are used to estimate the fractional difference parameter. Test results show the existence of long memory in stock exchange returns series; therefore, long memory models should be used to estimate and forecast. Also the weak form of market efficiency hypothesis can be disaffirmed by using the results. JEL: C16، G1، G14 How to cite this paper: Raoofi, A., & Mohammadi, T. (2018). Empirical Study on the Existence of Long-term Memory in TSE Returns. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 397–424. (In Persian)
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
398
425
https://jferm.khatam.ac.ir/article_66585_cac1534957877d7d3e92798de4b19e68.pdf
English Abstracts
text
article
2017
per
Journal of Risk modeling and Financial Engineering
دانشگاه خاتم
2538-5372
2
v.
3
no.
2017
https://jferm.khatam.ac.ir/article_66592_17a48bbbdd059caf3f47021efdf8b56a.pdf