Value at risk and marginal returns in Bahar Azadi coin market and stock market in Iran: using marginal value theory

Master's thesis in the field: executive management

Strategic management

Academic year: 1389-1391

Research abstract

Measuring market risk is a problem that has occupied researchers' minds for a long time. Various approaches have been proposed in this field. These approaches can be divided based on the statistical techniques used into three classes of parametric, semi-parametric and non-parametric approaches. So that most of the risk metrics[1] measure the risk in the form of these approaches. Among different risk measures, value at risk [2] is an emerging measure. In this research, the performance of parametric value at risk in predicting the risk of cash yield index of Tehran Stock Exchange and Bahar Azadi coin is examined.

The results of the post-test [3] of value at risk models in this study indicate that, firstly, the models that consider yield dynamics and yield fluctuations are relatively They have better performance than other models. In the present research, these models include moving average autoregression models[4] and autoregression models subject to variance heterogeneity[5]. Secondly, the models that have flexibility in fitting the parametric distribution to the data show much better performance than other models. In this study, the model beyond the threshold [6] is among these models. On the other hand, the average waiting time for an index that indicates a daily yield lower than the 0.01 threshold is 3 days, and also for a fixed period of time, the probability of observing a minimum daily yield higher than the -0.01 threshold for the next day is about 27%.

Keywords: average waiting time, marginal efficiency, Freyn's value theory, value at risk, parametric approaches, risk.

Chapter One

  Research overview

- Introduction

Investors to‌ When making investment decisions, they simultaneously consider the risk and return of investment options. These two dimensions of investment, i.e. risk and return, if not to say that they are the only influencing dimensions in the field of investment decisions, they are undoubtedly the most important. In fact, what from it to‌ It is interpreted as wise behavior[7], it is nothing but paying attention to these two dimensions when analyzing investment opportunities. in financial and economic literature to‌ It is clearly stated that a wise person is someone who seeks to achieve a certain level of return by bearing the least possible risk. In other words, he wants to achieve the maximum return at a certain level of risk. Therefore, risk is an inseparable part of return, and we cannot talk about investment return without considering the risk involved. In other words, risk is an attribute of efficiency, and it is not possible to describe any adjective based on its attribute. The image of this article is also valid, that is, no adjective can be examined without considering its description. This is exactly the reason that the existing models for describing asset returns are completed with asset return risk models, and risk models have a return component. If we consider the risk and return of an asset as variables, we get a major difference between these two variables. Return is a quantitative variable and risk is a qualitative variable. It is obvious that measuring and analyzing quantitative variables is much simpler than qualitative variables. Measuring a quality and expressing it in the form of a quantity is quite challenging and requires indescribable precision and effort. This is the reason why efforts are still being made to quantify risk and to search for more accurate and rational models. Various classifications of risk have been presented. One of the classifications is based on the factors that cause fluctuations in asset returns. Based on this risk classification into market risk, credit risk, operational risk, liquidity risk, political risk, etc. It is divided.For example, market risk is the risk of loss caused by unexpected movements[8] or fluctuations[9] in market prices or rates, and with this definition, it can be distinguished from other types of risk such as credit and operational risk.

  Following the studies of researchers, various tools for measuring risk have been introduced. Each of these tools is often developed to measure a certain type of risk. In recent years, a general criterion for measuring risk has entered the financial literature. A tool that, due to its features, It has become a standard measure for measuring different types of risk. The witness of this claim is the agreement of the bank supervision committee, that is, Bal [10] in order to quantify the risk of financial institutions and especially banks. This agreement advises banks to use the models of this risk measure to measure market, credit[11] and operational[12] risk. In addition, the capital adequacy requirements of banks are also determined based on the predictions of this measure of the risk faced by financial institutions. Also, many stock exchanges, including the New York Stock Exchange [13] require listed companies to disclose risk based on this criterion. The title is one of the options they have disclosed. This is the measure of the value at risk [14].

1-2- Statement of the basic research problem

Understanding the impact of extreme market events is very important for managers during the crisis. Our experimental investigations show that the efficiency distributions are not determined by conventional methods and that the maximum and minimum efficiency can be satisfactorily modeled in the framework of marginal values.

The marginal value hypothesis (EVT) has attracted the attention of researchers significantly in recent years. (Leadbetter, 1993; Rice and Thomas, 1997) and has had many applications in the field of finance. (Donelson and Day and Rice, 1998; McNeil, 1998 and Longin, 2000)

In financial markets, price limit movements may be similar to market corrections during normal periods or similar to stock market events or bond market crashes or currency crises during abnormal periods. Recently, in emerging markets, the market has experienced many extreme events. Examples of these events include the devaluation of the Mexican currency at the end of 1994, the Brazilian bond crisis at the beginning of 1995, the devaluation of the currencies of Asian countries during 1997, and the Russian crisis in May 1998. The recent turmoil in Asian financial markets provides interesting distributional opportunities to use the marginal value hypothesis to analyze these markets. The financial crisis in East Asia, which started in the middle of 1997, has been one of the most challenging and serious economic events in the 1990s. Although various factors caused the Asian monetary crisis, several factors were common to all Asian countries that experienced this crisis: an inflexible exchange rate system, a weak banking system, and excessive foreign loans were common among all countries that suffered a fall in currency value and stock prices. Based on the importance of East Asia in the global economy, the crisis in this region has had regional and severe effects such as a significant fall in the value of national currencies and a sharp fall in stock indices. Understanding the impact of extreme market events such as the East Asian financial crisis is very important for crisis period managers. (Ewing, 1995 and Longin, 2000)

Since all risk measurement methods regarding the estimation of value at risk (VaR) of an asset portfolio assume that the market trend is stable, therefore extreme market events determine the necessity of using a specific method for crisis managers. A more recent approach to VaR estimation focuses on modeling the posterior distribution based on the limiting value hypothesis (Longin, 2000; Diebold, 1999; McNeill and Frey, 1998; Mendes, 2000). The purpose of this research is to apply the hypothesis of threshold values ??for the analysis of stock and coin markets in Iran.