جستجوی مقالات مرتبط با کلیدواژه « طلا » در نشریات گروه « مالی »

The goal of this research is to identify the effects of oil, dollar and gold prices on the capital market; and meanwhile the investors’ Behavioral Biases.

The same research is a qualitative study of phenomenological type. Field data were collected using in-depth interviewing techniques and theoretical saturation criteria among 10 active participants of Tehran Stock Exchange who have at least three years of experience in investing in financial markets. After the implementation of the analysis phase using the Colaizzi method and MAXQDA10 software, the recorded interviews have been conducted.

Based on data analysis, seven main themes including external factors, parallel markets, cognitive biases, emotional biases, abnormal phenomena (in behavioral economics) as well as cultural and personal factors were extracted from the interviews. The results emphasize on the need for taking measures to predict exchange rates and avoid government-mandated pricing.Key words: Currency, Gold, Oil, Behavioral Biases, Capital Market

The first step in risk management in the field of investment is to calculate the variable that explains the risk accurately. One of the most widely used criteria for calculating risk is the value at risk, which has been the focus of financial researchers for the past three decades. Therefore, the aim of the present study is dynamic modeling and variable time using a technique called Generalized Autoregressive Score (GAS) to estimate value at risk in bitcoin and gold by using daily data since 2010 to 2020 and assuming the distribution of t-student. its results are compared with the results of known AR and GARCH models. The results showed that for gold models such as GAS, GARCH and AR were able to estimate the value at risk at 5% error level. Among them, the GAS model had the best performance. For Bitcoin only two models, GAS and GARCH, are suitable for estimating value at risk and GARCH model is preferable. Also, the duration of risk of value at risk errors for all three models for gold and bitcoin lacks long-term memory, indicating its reliance on financial turmoil.

Finding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Gold indice has helped many investors to optimize portfolios. Therefore, in this study, we introduce models of GARCH family composition and convoultional neural network to predict the daily yield of Gold index will be paid during the period of 1390-1398. In this study, the Gold index is examined using GARCH and EGARCH short-term memory models. Of the two variables, the price of crude oil and the dollar index as factors that their shocks and fluctuations have a major impact on Gold indices are used as control variables. In addition to using convolutional model, considering the better performance of combined models (compared to individual models ) In anticipation In this study, all models of the GARCH family (both short and long run) with the convoultional neural network were combined and using the combined models, the efficiency of the main stock index and the five selected indicators for the next 10 days were predicted step by step and its accuracy Based on the evaluation criteria.

One of the most important topics in financial markets in recent decades is the market efficiency. The efficient market hypothesis states that all available information is fully and immediately reflected in asset prices, therefore it is not possible to achieve systematic profits by price forecasting. The main purpose of this study is to evaluate the weak form of efficient market hypothesis for gold coin futures market in two settings of low volatility and high volatility of returns.
In order to test the weak form market efficiency in prices of gold coin futures market, Markov-switching GARCH and Markov-switching autoregression methods have been applied to the period from 2008 to 2017. The results of the FIGARCH and FARMA models indicate the fact that the series have long term memories of risk and average returns. The results of the estimation for the Markov-switching GARCH model (MS-E-GARCH) and the Markov-switching autoregression (MS-AR) imply that both risk and average return of gold coin futures market is predictable, and as a result the gold coin futures market does not have the weak form of efficiency in both low and high volatility settings and systematic profits could be achieved in this mark

This paper is trying to investigate comovement of the stock market, foreign exchange and gold in Iran. For this purpose, comovement and relationship mutually of these markets in Iran’s economy during the period 30.09.1997- 21.07.2015 with the weekly frequency have been investigated by the wavelet coherence analysis and Econophysics approach. The results of coherence analysis show that in the short time horizon during 2005-2008 and medium-term horizons during 2003-2006, the relationship between stock return and the exchange rate has been in the opposite direction (opposite phase). But in the longer-term horizonsduring2007-2010, the stock return moves after the moving exchange rate that the stock return is a lagging variable. Also, the coherence between gold and the exchange rate in the short term during1998-2002 was high with the same phase. After these years and during 2002-2012, the correlation between gold and the exchange rate had not been high particularly at high long-term horizons but after the year of 2012 with the intensification of sanctions, the coherency of these two markets has been high with the same phase until the year of 2014. Moreover, the coherency between stock return and the gold coin rates show that the intensity of correlation between these two variables during the period of study was low but the correlation of these two variables in 16-64 weeks’ horizons during 2001-2004 was high that of course, the direction of this relationship has been reversed.

Official statistics show that in the recent decades, on average, only about 12% of the country's real GDP has been allocated to investment in the manufacturing sector, including the production of goods and services and a considerable portion of it has been absorbed into unproductive speculative activities.Accordingly, this study with purpose to examining and comparing security of investing in different markets has evaluated the risk of investing in four market including stocks, gold, currency and Iran's housing using the Value at Risk (VaR) Criteria. Also in this study to providing a more accurate analysis of the security investment based on investor's attitudes TOPSIS method has been used.All information required for the study was collected on a monthly basis for during 2002 - 2013.The result of this study showed based on VaR Criterion, the security of investment in the stock market is much lower than other markets, so investors in this market face higher risk of investments relative to other markets.Also, the result of this study showed based on TOPSIS method (according to risk and return critrias) risk averse and risk neutral Investors have the Similar behavior, So that the two groups prefer The investment in the housing market and then investment in the gold market on the parallel markets including exchange and stock. However, unlike the aforementioned groups, risk-taking investors prefered investing in the stock market and then investing in the housing market on the investment in the gold and currency markets.