A quantile-based Time at Risk: A new approach for assessing risk in financial markets

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.     

A multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in Tehran stock market

Employing the multifractal detrended fluctuation analysis (MF-DFA), the multifractal properties of trading behavior of individual and institutional traders in the Tehran Stock Exchange (TSE) are numerically investigated. Using daily trading volume time series of these two categories of traders, the scaling exponents, generalized Hurst exponents, generalized fractal dimensions and singularity spectrum are derived. Furthermore, two main sources of multifractality, i.e. temporal correlations and fat-tailed probability distributions are also examined. We also compare our results with data of S&P 500. Results of this paper suggest that for both classes of investors in TSE, multifractality is mainly due to long-range correlation while for S&P 500, the fat-tailed probability distribution is the main source of multifractality.     

A Classification of Conformal Vector Fields on the Tangent Bundle

Ukrainian Mathematical Journal - Let (M,g) be a Riemannian manifold and let TM be its tangent bundle equipped with a Riemannian (or pseudo-Riemannian) lift metric derived from g. We give a...     

Network analysis of a financial market based on genuine correlation and threshold method

A financial market is an example of an adaptive complex network consisting of many interacting units. This network reflects market’s behavior. In this paper, we use Random Matrix Theory (RMT) notion for specifying the largest eigenvector of correlation matrix as the market mode of stock network. For a better risk management, we clean the correlation matrix by removing the market mode from data and then construct this matrix based on the residuals. We show that this technique has an important effect on correlation coefficient distribution by applying it for Dow Jones Industrial Average (DJIA). To study the topological structure of a network we apply the removing market mode technique and the threshold method to Tehran Stock Exchange (TSE) as an example. We show that this network follows a power-law model in certain intervals. We also show the behavior of clustering coefficients and component numbers of this network for different thresholds. These outputs are useful for both theoretical and practical purposes such as asset allocation and risk management.     

Comparing emerging and mature markets during times of crises: A non-extensive statistical approach

One of the important issues in finance and economics for both scholars and practitioners is to describe the behavior of markets, especially during times of crises. In this paper, we analyze the behavior of some mature and emerging markets with a Tsallis entropy framework that is a non-extensive statistical approach based on non-linear dynamics. During the past decade, this technique has been successfully applied to a considerable number of complex systems such as stock markets in order to describe the non-Gaussian behavior of these systems. In this approach, there is a parameter q$q$, which is a measure of deviation from Gaussianity, that has proved to be a good index for detecting crises. We investigate the behavior of this parameter in different time scales for the market indices. It could be seen that the specified pattern for q$q$ differs for mature markets with regard to emerging markets. The findings show the robustness of the stated approach in order to follow the market conditions over time. It is obvious that, in times of crises, q$q$ is much greater than in other times. In addition, the response of emerging markets to global events is delayed compared to that of mature markets, and tends to a Gaussian profile on increasing the scale. This approach could be very useful in application to risk and portfolio management in order to detect crises by following the parameter q$q$ in different time scales.     

Different methods to calculate heat transfer coefficient in a double-tube heat exchanger: A comparative study

An experimental study performed to compare the results of different methods used in the literature for the calculation of heat transfer coefficient in double-tube heat exchangers. Then, a new fundamental approach was proposed which used the temperature profile and the local heat transfer coefficients. In this method, the heat transfer coefficient has been calculated for the total length of the heat exchanger including developing and fully developed regions. Numerous experimentations have been conducted in a double-tube heat exchanger. A significant difference observed between the results obtained from the suggested approach and those of the previous methods.     

Experimental study of the effect of drag reducing agent on heat transfer and pressure drop characteristics

The effect of the addition of polymeric drag reducing agent on water flow heat transfer and pressure drop was studied experimentally. Polyacrylamide (PAM) aqueous solutions at different concentrations (up to 100 ppm) at fully developed turbulent flow were used in a double tube heat exchanger. In contrast to previous studies, drag reduction due to using PAM was negligible. The addition of PAM, even at low concentrations, caused significant reduction in heat transfer. Results showed that increasing PAM concentration lowered the amount of heat transfer. Maximum heat transfer reduction of 25% occurred at the concentration of 100 ppm for PAM aqueous solution.     

Convergence of fundamentalists and chartists’ expectations: An alarm for stock market crash

We construct a network of the Tehran stock market based on the cross-correlation of trading volume of stocks both for fundamentalists and chartists. In order to investigate the dynamics of expectations of fundamentalists and chartists over time we introduced a homogeneity coefficient. Our results show that in the Tehran Stock Exchange (TSE) which is an emerging market, chartists in comparison with fundamentalists more strongly believe the stocks’ co-movements. We also found that in a bull market (booming period), the optimism of fundamentalists and chartists about the similarity of stocks’ performance diverge from each other while in a bear market (recession period) both groups of traders have approximately same level of pessimism about the simultaneous collapse of stock prices.     

Comparing the structure of an emerging market with a mature one under global perturbation

In this paper we investigate the Tehran stock exchange (TSE) and Dow Jones Industrial Average (DJIA) in terms of perturbed correlation matrices. To perturb a stock market, there are two methods, namely local and global perturbation. In the local method, we replace a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series, whereas in the global method, we reconstruct the correlation matrix after replacing the original return series with Gaussian-distributed time series. The local perturbation is just a technical study. We analyze these markets through two statistical approaches, random matrix theory (RMT) and the correlation coefficient distribution. By using RMT, we find that the largest eigenvalue is an influence that is common to all stocks and this eigenvalue has a peak during financial shocks. We find there are a few correlated stocks that make the essential robustness of the stock market but we see that by replacing these return time series with Gaussian-distributed time series, the mean values of correlation coefficients, the largest eigenvalues of the stock markets and the fraction of eigenvalues that deviate from the RMT prediction fall sharply in both markets. By comparing these two markets, we can see that the DJIA is more sensitive to global perturbations. These findings are crucial for risk management and portfolio selection.