Mohammad Bamani Moghadam; mostafa pouralizadeh; Hadi Esmaeilpour Moghadam
Abstract
The purpose of this study is to determine an optimized stock insurance contract in Tehran Stock Exchange. First of all, based on a financial management problem, a risk management contract is designed to minimize the risk of loss that an agent might face. Then, with a mathematical modeling, we will see ...
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The purpose of this study is to determine an optimized stock insurance contract in Tehran Stock Exchange. First of all, based on a financial management problem, a risk management contract is designed to minimize the risk of loss that an agent might face. Then, with a mathematical modeling, we will see that to efficiently manage the stock risk, we need to make sure that only multi-layer contracts, or equivalently, European call options are correctly valued. Therefore, the optimized insurance contract is determined by correct pricing of European call options. Studying more deeply in this area by implementing the proposed algorithm on Tehran stock exchange shows that the optimized value of the insurance contract is a small percentage of the stock initial price; furthermore, it is also a function of the stock return fluctuation. Hence, the volatility and the price of insurance contract are positively correlated. In other words, the more a stock is volatile, the more expensive is an insurance contract.
Ali Reza Shakeibaei; Ebad Teimori
Volume 12, Issue 45 , July 2012, , Pages 99-121
Abstract
The US dollar is frequently used as the invoicing currency of international crude oil trading. Hence, the fluctuation and risk in US dollar exchange rate is believed to underlie the volatility of crude oil price and especially risk transmission to its market. When the prospect of the US dollar is not ...
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The US dollar is frequently used as the invoicing currency of international crude oil trading. Hence, the fluctuation and risk in US dollar exchange rate is believed to underlie the volatility of crude oil price and especially risk transmission to its market. When the prospect of the US dollar is not considered promising, a large amount of money will flow to the oil market, thus oil price will be driven up. As a result, some new investment and speculation opportunities can be derived for traders. For existence such relationship, controlling and monitoring the financial risk between these two markets is necessary. This paper applied new risk management tool, VaR methodology, and Granger causality test in risk to examine the risk spillover effect in both crude oil market and US dollar exchange market. Results show that, from the perspective of market risk, interaction between crude oil market and US dollar exchange rate does not seem strong. So the effect of extreme risk spillover between two markets proves quite limited.
Mir Fize Fallah Shams; Hamid Mahdavi Rad
Volume 12, Issue 44 , April 2012, , Pages 213-234
Abstract
Nowadays, leasing industry is recognized as one of the strategic options in economic development. Leasing companies have a great profitability and are faced to the most risks. Credit risk, business risk, residual value risk and exchange risk are some of important risk in leasing companies. Among them, ...
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Nowadays, leasing industry is recognized as one of the strategic options in economic development. Leasing companies have a great profitability and are faced to the most risks. Credit risk, business risk, residual value risk and exchange risk are some of important risk in leasing companies. Among them, credit risk is the most important. Subsequently, making logic relationship between risk and return is the essential element to devote optimally resources and to guarantee profitability leasing companies.
In this paper, on the basis individual costumers data extracted from Leasing Iran Khodro Company database (since1381-1384) and using tow sample t-student test, and determinant coefficient, we found five variables as factors affect credit risk. They include, net monthly costumer income, loan time, loan amount, net monthly guarantor income and experience. In addition, we apply tow credit risk models (Logit & Probit) for leasing loans. The results of Wald, Log likelihood and Wilk΄S Lambda tests indicate that the efficiency in Logit model (%98.39) is more than Probit model (%97.44).
