دکتر الهام فرزانگان

One of the well-known stylized facts of the distribution of asset returns is the pattern of skewness and kurtosis. Previous research has shown that financial crises and turbulences induce shocks that significantly affect the return distributions, which in addition to creating fat tails, also leads to an asymmetric reaction of the tails. Although skewness and kurtosis have a common impact on tail risk, their significance for risk forecasting has not been considered in empirical financial studies.Developing models for accurate risk forecasting is an important consideration that has always received considerable attention from policymakers, economists, financial market participants, and researchers.For this purpose, in this study, following Jimenez et al. (2022), to estimate the return density, a semi-nonparametric approach is adopted which is based on the asymptotic properties of Gram-Charlier extensions. This approach allows examining the significance of the inclusion of Hermit polynomials and their crossed products in the Gram-Charlier densities for risk forecasting. In fact, in the framework. Evaluating the risk measures in a semi-nonparametric framework allows for capturing all stylized facts of the return series for assessing skewness and kurtosis and their interactions by adding new parameters to the density function as a relevant source of information.
This research, for the first time, employs the modified Gram-Charlier density function (mGC), including the second and third moments (skewness and kurtosis) and their interactions for modeling the risk of distribution of the daily losses of the TEPIX. Moreover, the performance of alternative models based on different specifications of Gram-Charlier is evaluated in terms of the accuracy of risk forecasting measures using modern backtesting tests. For this purpose, the Value-at-Risk criteria and Median Shortfall measure, implied for the first time in the present study, are used. The sample includes the daily series of the TEPIX index covering the period from May 20, 2008, to August 22, 2023. Focusing on the right-tail of the TEPIX distribution, the loss series is calculated as a negative of log differences of prices. The models are estimated by R and MATLAB software. Modeling the losses is done through a two-step estimation process according to the following steps. Step 1: the ARMA(1,1)-GARCH(1,1) model is estimated  using the quasi-maximum likelihood (QML) approach by assuming the Gaussian distribution for error terms. Step 2: the modified Gram-Charlier expansion and alternative specifications are estimated using standardized residuals extracted from the previous step. Different specifications of the Gram-Charlier density density fit using the maximum likelihood method. For in-sample fitting of the model, the estimation window size is chosen to be W=2656 observations and the step is chosen to be one new observation. The remaining 1000 observations are used for out-of-sample forecasts.
The empirical findings from the in-sample fitting of the ARMA(1,1)-GARCH(1,1) model, under the Gram-Charlier densities for the innovations, indicate that skewness and kurtosis and their interactions are economically and financially significant. The results of the Backtesting for both 99%-VaR and 99%-MS confirm the out-of-sample forecasting performance of the Gram-Charlier density specification incorporating the skewness parameter, especially for the tails, compared to other specifications that have been taken into account in this research.
Overall, the results show that the parameter related to the asymmetry of distribution alone can be a valuable source of information to the market participants by providing accurate risk measures. The empirical findings have practical implications for designing strategies for managing risk and decision-making in times of market instability. The novelity of this study is the application of a semi non-parametric approach to evaluate the risk forecasting of the TEPIX index. The previous studies have mainly modeled the return series based on parametric and non-parametric distributions. Therefore, the empirical findings of the present study provide a novel application for risk management in the Tehran Stock Exchange. The empirical findings can have useful implications for stabilizing the financial markets.

The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is a vital component of Iran's capital market. This study aims to fill this gap by examining the information flow dynamics among 39 major industries from March 27, 2010, to June 21, 2023. Effective transfer entropy is employed to quantify the intensity of information flow between industry indices. Sequence of information matrices are constructed using rolling one-week windows over one-year periods. Given the occurrence of critical events during the research period, their influence on information flow dynamics is analyzed using Frobenius distance-based k-nearest neighbor networks, Influence Strength analysis, and threshold networks. The findings reveal that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable throughout most periods. Furthermore, critical events significantly impact information flow dynamics, with abnormal values of Influence Strength associated with market volatility and major events. Additionally, the dominant source of information in the information flow network changes over time, highlighting the transient nature of industry dominance within the network.

The Tehran Stock Exchange and its industries have contributed to the risk of tail events occurring in recent years, such as the U.S. and European sanctions against Iran and the COVID-19 pandemic. Industries affected by these crises have become more connected. Crisis episodes have raised risk spread within the financial system. Whenever an impulse hits an industry, it is quickly transmitted to other industries through a contagion mechanism, which ultimately causes fluctuations in the whole system. Stress situations suggest that promoting financial stability should be a priority for regulators and academic researchers. For this purpose, analyzing the systemic risk contagion and tail risk interconnectedness among industries is important to enhance the stock market’s safety and design the related macroprudential policies. The stock market plays a vital role in the financial system by allocating resources and facilitating price discovery. However, Tehran Stock Exchange is a completely immature market, which greatly increases the possibility of risk contagion to the entire market. For example, the collapses of stock price bubbles in the Tehran Stock Exchange over the past two decades resulted in huge losses in the net worth of investors and was followed by a recession. The financial turmoil highlights the importance of measuring systemic risk in the stock market.Interconnections among industries in a network imply that a shock in one industry may lead to risk transmission between related industries, causing risk contagion to the entire stock market. Therefore, investigating the risk interrelationships between industries and identifying the systematically linked industries is essential to effectively predict the systemic risks and control risk spread in the stock market.This study analyzes the risk interdependence among the main industries listed on the Tehran Stock Exchange. In particular, the risk contagion effects among industries are analyzed under extreme market conditions.

This paper seeks to employ fractional cointegration methodology to model high and low stock prices, as well as the range series, indicating the difference between high and low stock prices. Additionally, it tries to examine the regime-switching characteristic in the cointegrating relationship between price series.

The study utilizes the Fractionally Cointegrated Vector Autoregressive (FCVAR) approach to explore the cointegrating relationships among six key indices of the Tehran Stock Exchange - TEPIX, First Market index, Second Market index, Industry index, Mali index, and Fara Bourse overall index - across various time frequencies from August 14, 2007, to August 15, 2022. To test for the fractional unit root in each price series, the GPH and ELW methods are also employed. Furthermore, the Qu (2011) method is employed to test the true long memory against spurious long memory on the range series. Finally, the threshold effect in the cointegrating relationship between the high and low price series is analyzed by the two regimes' Self-Exciting Threshold Autoregressive approach (SETAR).

In most indices, the estimated fractional parameter of the range series is lower than that of the high and low stock price indices. Moreover, the high and low prices and the range series are affected by regime changes or a smoothly varying trend. The high and low stock price indices are fractionally cointegrated, in the two levels of stationary and non-stationary ranges. Further, the fractional cointegration approach gives a lower measure of dependency in price range series than the fractionally integrated approach. These findings are robust to different time frequencies, including daily, weekly, and monthly. Finally, the results affirm the time-varying cointegrating relationship between high and low stock prices. Thus, the FCVAR framework should be generalized to adjust according to this characteristic.

The empirical results show that, unlike the return, which is stationary and unpredictable, the range prices have characteristics of the long-memory processes, falling into non-stationary and stationary levels with mean-reverting behavior. Accordingly, one can obtain a non-stationary range-based volatility estimator, which is more efficient than a stationary return-based realized volatility estimator. These results imply that traders, investors, and policymakers could predict the future extreme prices of the market indices from past values and exploit such predictions to design investment strategies. The cointegration between high and low prices of indices implies limited arbitrage opportunities for the investors and traders in the Tehran Stock Exchange and Iran Fara Bourse Co. Furthermore, the efficiency of these markets is shown to be unstable across indices. Still, there are robust long-run relationships between the high and low stock price indices. Thus, to design hedge fund strategies containing combinations of the stocks from these markets, the behavior and long-run relations of the indices must be considered. Moreover, to design portfolio allocation and diversification strategies, one should mix assets that haven’t short-term behavior with indices that show long-term relationships. This conclusion is derived from the notion that mispricing and over-hedging may occur in the absence of cointegrating relationships between price series.

This study investigates the effects of arbitrage asymmetry and limits-of-arbitrage to explain the observed idiosyncratic volatility return premium in the Tehran Stock Exchange. The sample includes 90 companies listed on the Tehran Stock Exchange from 2008 to 2022. For this purpose, a comprehensive index of limits-of-arbitrage has been constructed based on three measures of arbitrage risk: transaction costs, noise trader risk, and information uncertainty. Furthermore, a mispricing measure has been constructed based on three anomalies: asset growth, gross profitability, and momentum. These measures and anomalies capture the main features of the Tehran Stock Exchange. To test the research hypothesis, the three-way portfolio sorting and the Fama-Macbeth cross-sectional regressions conducted. The empirical findings show that the idiosyncratic volatility premium is positive for both overpriced and underpriced stocks. Within the high limits-of-arbitrage stocks, the positive idiosyncratic volatility return premium is higher for overpriced stocks than for underpriced stocks. Consequently, the limits-of-arbitrage and the arbitrage asymmetry can explain the positive idiosyncratic volatility return premium in theTehran Stock Exchange.

This study investigates, for the first time,the herding behavior effect on the excessive market idiosyncratic volatility in the industries listed on the Tehran Stock Exchange. We use daily data of 105 firms in 21 industries for the period 2008 to 2020, which covers the stock price bubbles and nuclear sanctions against Iran. The result sof the modified cross-sectional absolute deviation(CSAD) model show that during such turmoil periods, herding does not exist in the whole market, but is observed in eight industries. Moreover, because of the fear of larg elosses during market turmoil, investors ignore their own information to imitate the decisions of in form edtraders, and, in turn, herding in thirteen industries. Moreover, the findings of the modified GJR-GARCH model highlight that herding has a different effect on volatility across the different industries. In particular, her dingnegatively affect tsthe conditional idiosyncratic volatility in fourteen industries along withthe whole market. Furthermore, because the larger trading volume results in higher informed trading,trading volume turnover egatively affects the conditional idiosyncratic volatility in sixteenin dustries.

This paper aims to investigate the pricing of idiosyncratic volatility in the Tehran Stock Exchange. Moreover, it examines whether the explanations proposed by previous studies for the idiosyncratic volatility anomaly in the developed markets can be applied to the evidence obtained from the Tehran Stock Exchange?

the sample includes thefirmsthat have positive book values, the non-financial intermediaries, and the firms with the fiscal year ending on the last day of March. Tocalculate idiosyncratic volatility, the firms with trading days less than onece each month are excluded. The final sample consists of 48firmslisted in the Tehran Stock Exchange, over the period from 2008 to 2019. The hypothesis is examined by using the three-factor Fama andFrench (1993) model, the one-way portfolio sorting approach on idiosyncratic volatility, the two-way portfolio sorting on idiosyncratic volatility, and the five risk factor, and Fama and MacBeth (1973) cross-sectional regressions method.

suggests a positive and statistically significant relationship between the idiosyncratic volatility and the future stock returns. This result is persistent after controlling for the Fama-French three risk factors (excess market return, SMB, and HML), the short-term reversal factor, the preference for lottery-type stocks, and the momentum factor (Carhart, 1997).

there is no evidence of the idiosyncratic volatility anomaly in the Tehran Stock Exchange; therefore, the investors should take into account the idiosyncratic volatility in their investment decisions. Furthermore, none of the explanations in the literature can explain the idiosyncratic volatility positive premium on the Tehran Stock Exchange.

the idiosyncratic volatility anomaly was investigated mainly for developed markets and other developing countries. Moreover, the existing explanations about the idiosyncratic volatility anomaly have not yet been examined for the Tehran Stock Exchange. Therefore, this study aims to fill this gap to extend the literature on the emerging Tehran Stock Exchange.

This study is the first to examine the issue of time-varying long-term memory in the Tehran Stock Exchange, using a new efficiency index through a rolling window technique. To test the robustness of the results, this estimation technique is repeated with time windows with 5-day shifts. Furthermore, the wild bootstrap versions of the Automatic Portmanteau test (AQ) and the Automatic Variance Ratio test (AVR) have been performed with 14- and 5-day shifts.

The sample employed in this paper consists of daily observations on the Tehran Stock Exchange Index (TEPIX), covering the period from December 2008 to October 2019, making up a total of 2621 observations. The TEPIX series are collected from the Rahavard Novin software. The hypotheses are analyzed by Excel, EViews, R, and MATLAB software.

The findings show that in the Tehran Stock Exchange the estimated values of the generalized Hurst exponents (GHE) for all windows with 14-day shifts are over the efficiency indicator 0/5. Therefore, a kind of long-term memory exists on the Tehran Stock Exchange. Moreover, a high degree of inefficiency ratio is observed in the market. Furthermore, the Tehran Stock Exchange does not become more efficient over time. Finally, the results from the time windows with 5-day shifts as well as wild bootstrap versions of the AQ and AVR tests with 14- and 5-day shifts indicate that the previously-mentioned empirical results are robust. The findings from the study provide important implications for investors, portfolio managers, and policy-makers.

In this paper, we employ a new generation of multivariate volatility models, i.e. ADCC, GO-GARCH and Copula-GARCH to estimate and investigate the hedging performance for Bahar-Azadi Gold Coins spot markets (GC) and Futures market (GCF), during 27/10/2010 to 21/7/2016. The empirical results show that the hedge ratios estimated from the GO-GARCH model are preferred (most effective) for hedging GC prices with GCF. The results also show that the spot and futures prices tend to co-move in times of market stress. Indeed, those investors who hold diversified portfolios of gold coin and gold coin futures may face significant losses in a bear market. Here a short position on the gold coin futures may be beneficial to gold coin investors because it helps reduce the portfolio’s extreme losses.

One challenging and critical problem in behavior finance is how to establish verifiable models describing the appearance price bubbles.In this paper, Building on Shleifer and Vishny (1979), seminal work, and with assumption of being myopic arbitragers, the aim is to test if existence of rational bubble is due to activity and response of noise traders to noisy information?Using Tehran Stock Exchange data from 2004:M3 to 2015:M6, an autoregressive regime-switching model of stock price dynamics in which the process creates pricing bubbles in one regime while a linear co-integration relationship between dividends and prices prevails in the other, is estimated. Empirical results indicate that the probability of regime-switching depends on exogenous inflation and lagged price. The results emphasize the importance of the impact of noisy information causing the deviation of prices from intrinsic value.

In the securitization process، by selling the mortgage loans to risk-lover investors، originator can allocates the mortgage loans risk with them. In this case، originator may not have an incentive to screen out borrowers، resulting in the moral hazard problem. This paper، in the principal-agent framework، analysis this agency problem. Investor، to reduce asymmetric information، uses compensation scheme for giving incentives to the originator and by using the Bayesʼ rule، deals with inferring various dimensions of implemented effort؛ and incorporates her joint posterior beliefs of the pooled loans’ credit position data and inferred various dimensions of effort، in to the designing contract problem. The results indicate that the shape of optimal contract is a function of the information content of investors’ observations and inferred knowledge about efforts، which suggests that using additional information to avoid originator’s opportunism، the originator more likely performs the obligated tasks when lending the loans to the applicants.

Bubble is prolonged stock price boom, which is always followed by bust, in the sense that the initial increases in prices are due to the expected increase in the future that attracts new buyers and thus speculators. The main hypothesis of this research is whether the increasing stock prices are driven by market fundamentals or just by speculation. In this paper to investigate the relationship between monetary policy and stock prices, the transmission mechanism is based on linearized rational expectations model with assuming the forward-looking behavior of stock prices, have been considered in the framework of the New Keynesian Academy. Experimental analysis during 2000:M1 to 2010:M2 using GMM procedure, shows that real interest rates have negative effect, and expected output has a positive effect on real stock returns. Also, past stock returns have a positive momentum on current stock prices, which represents the speculation causing the deviation of prices from their intrinsic values.

Economic stabilization is one of the main government objectives in the economy. One of the most destructive and devastating factors that could damage financial markets، are price bubble formations. Thus، bubble creation in stock markets can be considered as a result of investor behaviors، because the market prices mainly reflect investor expectations from firm’s future perspectives. The aim of this paper is to study rational bubbles with the consideration of one of the limitations of arbitrage، Noise Trader Risk، by focusing on rational expectations in the Tehran Stock Exchange. To achieve this aim، by using Panel Data and ARDL approaches، from 2000M3 to 2008M10 appropriate models are estimated. Results indicate that during the underlying period، even with dominance of rational arbitrageur; noise traders have a significant impact in the formation of bubbles. The results emphasize the importance of inflation and price-dividend ratio when assessing investment risk.

Because of the heterogeneity in behavior, in the real world prices may deviate substantially and persistently from their fundamental values. Of course, if these heterogeneous elements play a rather minor role then asset prices and rates of return will be determined mainly by economic fundamentals and rational behavior. By observing actual behavior in the stock market one can seek to isolate profitable trading opportunities which persist for some time. This evidence is referred to as stock market anomalies. In this paper, nonparametric bootstrapping procedure is used to analysis average monthly seasonality returns. Evidence suggests several explanations for abnormal returns during 2000: M3 to 2010:M2. Tax-loss Selling, Window dressing anomalies explain the existence of these distinct patterns of returns. Also, existence of those calendar seasonalities as the most important financial market anomalies is often promoted as a conflict with the efficient market hypothesis.

Housing market in Iran got out of recession in year 1384 and turn into abnormal growth. But following the housing price growth, which continued until 1386, it deals with the slowdown of the housing market and stable prices in the spring of 1387. Afterward, decreasing trend in housing prices continued in the summer, in spite of increase in global housing prices. In this paper, it is investigated that whether these prices increases rooted in fundamental economic factors or is caused by bubbles. In this study, monetary policy transmission mechanism is assumed in a linear model by rational expectations. Using GMM and a forward-looking New-Keynesian model for a closed economy, the real interest rate effect on housing real return has been discussed in period 1 / 1380 to 6 / 1387. The results indicate that during this period, real interest rates has negative effect and the lag of housing real return and GDP have positive effect on housing real return.

Financial markets are considered as a particular important capital market instruments an allocation of financial resources mobilization process. Considering the importance of strategic financial and economic market, when the disruption and diversion occur widely in it, mobilization and allocation of financial resources of the countries are faced with serious problems. One of the factors regenerator these issues is price bubble. Generally, when the price of a share is different from its future expected price, market bubble term is introduced. This paper explores validity of present value model with time – varying expectations, by use of newly developed Momentum Threshold Cointegration test, M-TAR, to investigate if there is any asymmetric adjustment in long-run prices and dividends in Tehran stock Exchange during 2000:M3 to 2008: M10? Empirical results indicate that long-run prices and dividends cointegration relationship doesnt hold for Tehran's stock exchange, which attests to the presence of rational bubbles.