Sharif Digital Repository

The present study aims to calculate the core inflation percentage using a SVAR model. Core inflation which is defined in policy-making, includes that component of measured inflation that has no (medium- to) long-run impact on real output but rather includes monetary and demand shocks. To this end, the VAR model is defined with 4 endogenous variables, namely production, inflation, money supply and exchange rate in addition to 2 exogenous variables, which are dummy (due to attracting the uneven behavior of exchange rate) and energy prices. Core inflation is produced using the results of the model estimation and the monetary and demand shocks. The results show that the core inflation usually... 

Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and... 

Time series of asset returns display specific regularities such as bell-shaped distribution, leptokurticity and volatility clustering. Economists have made continuous efforts to develop models that explain these patterns and can be used to predict the return and the risk of holding an asset. These efforts can be classified into 3 eras: Bachelier Era (1900-1960) in which the random walk model was developed for speculative prices. Mandelbrot Era (1960-1980) in which the normality assumption was replaced with the Pareto-Levy family of distributions which are flexible enough to justify leptokurticity and infinite variance. And finally, the Dynamic Volatility era in which the focus was on... 

In this study, the effect of oil price shocks on stock markets is examined for Iran and Norway as two oil-exporting countries. To do this, an unrestricted Vector Auto-regression model is applied based on monthly data from April 2001 to March 2011. The variables used in the model are Brent oil price shocks, interest rate, consumer price index and stock returns. To find the relationship between oil price shocks and stock markets and determine the effects of oil shocks on stock markets, impulse-response analysis and variance decomposition are employed. To compare the effects of positive and negative oil price shocks, a Chi-square test is used. The proposed model is applied on different types of... 

Evolving Informational Efficiency in financial markets is of a significant role in the financial economic development. The present research is an endeavour to test the efficiency improvement in Tehran stock exchange by a GARCH-M model which is estimated by Kalman Filter technique. To conduct the estimation and tests, we have used daily data (over 1384-1388) on the return of Bank and Financial intermediaries, Metal groups and the main index of TSE. The findings show that the information inefficiency hypothesis can not be rejected, although any noticeable improvements are not observed in the inefficiency trend of the financial time series, the level of inefficiency trends are rather different... 

Forecasting has a crucial role for decision makers in economics and finance and is frequently used by firms, government institutions and professional economists. Academic studies in macroeconomics modeling and economic forecasting have been historically concentrated on models with few variables. But in practice, a decision maker has a large amount of information in the form of variables which has some predictive content for the target variable. One way to handle the large-scale information in forecasting is to use forecast combination methods. These methods generally combine the simple forecasts of some target variable while the forecasts are weighted according to their relative accuracy,... 

Volatility is the most important components in numerous finance applications. So, the methods of volatility forecast with reasonable accuracy require a deep attention.In this thesis with considering several distributions for error term, GARCH forecasting performance is evaluated on the intra- day data of "Foolad" stock returns by two loss functions of "MAE" and "HMAE". This evaluation is done in three forecast horizons, 1 day, 5 days and 20 days. Finally, the result of this study is as follows. GARCH (1, 1) forecast model with skewed t- student error distribution has the minimum value in the both loss functions for 1 day and 5 day forecast horizons. Also GARCH (1, 1) forecast model with t-... 

The recent researches show that Value at Risk estimations are biased and is calculated conservatively. Bao and Olah (2004) proved that the bias of an ARCH(1) model for VaR can be formulated in to two parts: bias due to return Misspecification ( ) and bias due to estimation error ( ). Using a GARCH(1,1) and quasi maximum likelihood estimation method, this research intends to find an analytical framework for the two source of biases. We generate returns from Normal and t-student distributions, then estimate the GARCH(1,1) under Normal and t-student assumptions. Our findings reveal that equals to zero for the Normal likelihood function, but . Also, and are not zero for the t-student... 

Due to the significance of exchange rates in economic policy making, different patterns have been proposed so as to explain the behavior, provide ways to model and deliver tools to forecast different exchange rates. Using a novel approach, this thesis tries to investigate the behavior of exchange rates by identifying time series nature of exchange rates, and performing nonlinear test for daily data between years 2003 to2006. In this study, we try to model and forecast the daily exchange rates by the use of Artificial Neural Networks (ANN). We also compare the results with ARIMA model outputs based on measures for forecasting accuracy. 80 percent of the daily data, that is, 1160 days from... 

In this project we have worked on the new architecture of the auction based resource scheduling, from the bidders point of view. The performance of different bidding strategies for the resources which participate in reverse auction system has been investigated; our main parameter for evaluating different bidding strategies is the amount of the profit gained by resources which follow such strategies. The main historical bidding strategies are created based on two famous predictors ES and AUTO-REGRESSION. In addition a game theory approach has been proposed. We have shown that our bidding algorithm (based on the sequential game model) reaches to an equilibrium point if all the bidders follow... 

Flow Mediated Dilation (FMD) is a non-invasive method for endothelial function assessment providing an index extracted from ultrasonic B-mode images. Although utilized in the research community, the difficulty of its application and high cost of ultrasonic device prevent it from being widely used in clinical settings. In this study we show that substituting the ultrasonic device with more easily handled and low cost photoplethysmography and electrocardiography is possible. We introduce new indices based on the photoplethysmogram (PPG) and electrocardiogram (ECG) and show that they are correlated with the ultrasound-based FMD index. To this end, conventional ultrasound FMD test was carried... 

Prediction of groundwater level fluctuations is absolutely necessary for the proper manag ement of these precious resources. There are different methods to predict hydrological time series such as groundwater level. Linear models inefficiency in predicting nonlinear and n on-stationary time series cause researchers widely use artificial intelligence techniques such as Artificial Neural Networks (ANN), Fuzzy Inference System (FIS), Genetic Algorithm (GA) and their hybrid models such as Artificial Neural – Fuzzy Inference System (ANFIS). These smart models are capable to simulate nonlinear and non-stationary time series in suitable accu-racy using by a few time and data. By emersion of Wavelet... 

Hydrologic models are the best tools to reduce hydrological uncertainties in rivers runoff estimation. Considering structure of model and calibration of parameters, there are several approaches to extend. In this study, we try to simulate daily river runoff by using two different approaches and then compare the results of them with each other. In first approach, IHACRES model is used to simulate rainfall- runoff, which is a conceptual model based on rainfall, runoff and evapotranspiration or temperature data. This model uses conceptual part to estimate effective rainfall and also it uses a transformation function to transform effective rainfall to output flow. This model works in daily time...