From Discrete to Continuous: Modeling Volatility of the Istanbul Stock Exchange Market with GARCH and COGARCH

The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Index by ARMA and GARCH models and then take a step further into the analysis from discrete modeling to continuous modeling. Through applying unit root and stationary tests on the log return of the index, we found that log return of ISE100 data is stationary. Best candidate model chosen was found to be AR(1)~GARCH(1,1) by AIC and BIC criteria. Then using the parameters from the discrete model, COGARCH(1,1) was applied as a continuous model

Continuous Modeling of Foreign Exchange Rate of USD versus TRY

This study aims to construct continuous-time autoregressive (CAR) model and continuous-time GARCH (COGARCH) model from discrete time data of foreign exchange rate of United States Dollar (USD) versus Turkish Lira (TRY). These processes are solutions to stochastic differential equation Lévy-driven processes. We have shown that CAR(1) and COGARCH(1,1) processes are proper models to represent foreign exchange rate of USD and TRY for different periods of time February 2002- June 2010Continuous modeling; Continuous AR; COGARCH; USD/TRY

From Discrete to Continuous: Modeling Volatility of the Istanbul Stock Exchange Market with GARCH and COGARCH

The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Index by ARMA and GARCH models and then take a step further into the analysis from discrete modeling to continuous modeling. Through applying unit root and stationary tests on the log return of the index, we found that log return of ISE100 data is stationary. Best candidate model chosen was found to be AR(1)~GARCH(1,1) by AIC and BIC criteria. Then using the parameters from the discrete model, COGARCH(1,1) was applied as a continuous model.ISE100,IMKB100,GARCH Modeling,COGARCH Modeling,discrete modeling,continuous modeling

Testing Non-Linear Dynamics, Long Memory and Chaotic Behaviour of Energy Commodities

This paper contains a set of tests for nonlinearities in energy commodity prices. The tests comprise both standart diagnostic tests for revealing nonlinearities. The latter test procedures make use of models in chaos theory, so-called long-memory models and some asymmetric adjustment models. Empirical tests are carried our with daily data for crude oil, heating oil, gasoline and natural gas time series covering the period 2010-2015. Test result showed that there are strong nonlinearities in the data. The test for chaos, however, is weak or nonexisting. The evidence on long memory (in terms of rescaled range and fractional differencing) is somewhat stronger altough not very compelling

From Discrete to Continuous: Modeling Volatility of the Istanbul Stock Exchange Market with GARCH and COGARCH

The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Index by ARMA and GARCH models and then take a step further into the analysis from discrete modeling to continuous modeling. Through applying unit root and stationary tests on the log return of the index, we found that log return of ISE100 data is stationary. Best candidate model chosen was found to be AR(1)~GARCH(1,1) by AIC and BIC criteria. Then using the parameters from the discrete model, COGARCH(1,1) was applied as a continuous model

Continuous time modeling of interest rates: An empirical study on the Turkish short rate

We proposed a continuous time ARMA known as CARMA(p,q) model for modeling the interest rate dynamics. CARMA(p,q) models have an advantage over their discrete time counterparts that they allow using Ito formulas and provide closed-form solutions for bond and bond option prices. We demonstrate the capabilities of CARMA(p,q) models by using Turkish short rate. The Turkish Republic Central Bank’s benchmark bond prices are used to calculate short-term interest rates between the period of 15.07.2006 and 15.07.2008. ARMA(1,1) model and CARMA(1,0) model are chosen as best suitable models in modeling the Turkish short rate

Continuous time modeling of interest rates: An empirical study on the Turkish short rate

We proposed a continuous time ARMA known as CARMA(p,q) model for modeling the interest rate dynamics. CARMA(p,q) models have an advantage over their discrete time counterparts that they allow using Ito formulas and provide closed-form solutions for bond and bond option prices. We demonstrate the capabilities of CARMA(p,q) models by using Turkish short rate. The Turkish Republic Central Bank’s benchmark bond prices are used to calculate short-term interest rates between the period of 15.07.2006 and 15.07.2008. ARMA(1,1) model and CARMA(1,0) model are chosen as best suitable models in modeling the Turkish short rate

Wavelet Based Analysis Of Major Real Estate Markets

Wavelet coherence of time series provide valuable information about dynamic correlation and its impact on time scales. Here, we analyze the wavelet coherence of major real estate markets data. Our paper is the first to link co-movement in terms of wavelet coherence. Here we consider USA, Canada, Japan, China and Developed Europe real estate market prices as time series.Wavelet coherence results reveal interconnected relationships between these markets and how these relationships vary in the time-frequency space. These relationships allow us to build VARMA models of real estate data which yield forecast results with small errors

Measurements of the microscopic properties contributing to photorefraction in barium titanate.

http://archive.org/details/measurementsofmi00unalN