MODELING ROMANIAN EXCHANGE RATE EVOLUTION WITH GARCH, TGARCH, GARCH- IN MEAN MODELS

In this paper we analyze the return of exchange rate in order to test and analyze the best models which are capable of forecasting accurately there evolution. We apply the GARCH family models on the exchange rate return in order to obtain the best models for there volatility. Financial time series often exhibit abnormal characteristics, such as: serial correlation, non-stationarity, heteroskedasticity, asymmetric and are leptokurtic. Due to these characteristics autoregressive models such as autoregressive (AR), moving average (MA) and autoregressive integrated moving-average (ARIMA) are unable to capture the evolution of financial series, to represent the special characteristic of financial a hole new range of models where developed : generalized autoregressive conditional heteroskedasticity (GARCH), which are taking into account the heteroskedasticity of the errors term. The GARCH model allows for lags in the autoregressive term and in the variance term incorporates lags of the previous variance and also for the errors. The GARCH family has expanded in the last years in order to incorporate for asymmetry (Threshold GARCH, TGARCH) and risk (GARCH -in Mean). We analyze the evolution of exchange rate for: Euro/RON, dollar/RON, yen/RON, British pound/RON, Swiss franc/RON for a period of five years from 2005 till 2011, we observe that in the analyzed period there are 2 sub-periods: 2005-2007 in which the RON appreciated constantly, and 2007-2011 in which the trend is depreciation for RON in respect to all the five currencies and the volatility was sensible higher than in the previous period. We obtain the returns on exchange rate by using the following transformation r=log(curs_t)-log(curs_t-1); the five analyzed series display an leptokurtic and asymmetric behavioral. Using the GARCH, TGARCH and GARCH-in Mean models, we explicit the evolution of volatility throw this period, choosing the best model using the following : minimizing the value of the sum of squared errors, Akaike and Bayesian Information Criterion.exchange rate, GARCH, TGARCH, AIC, BIC.

LINKING MONEY SUPPLY WITH THE GROSS DOMESTIC PRODUCT IN ROMANIA

Evolution of money supply and gross domestic product are in a close relationship, inthis paper we analysis this relationship in order to construct a function which will explicit thisconnection for Romania. Evolution of gross domestic product is one with a seasonal component sofrom the data series we will be eliminating seasonality with the X-12 ARIMA method. Analyzing thedata of money supply (M3) and of GDP over ten years through the Augmented Dickey-Fuller weobtained that both series are non-stationary. Applying the co-integration analysis method Engle-Granger we conclude that the two series have a cointegration relationship between them. We willpropose a model explanation of the link between the two sets of data type, a DVAR model.money supply (M3), GDP, seasonality, stationarity, cointegration, DVAR.

THE PERFORMANCE OF INVESTMENT FUNDS IN ROMANIA IN THE CONTEXT OF CRISIS

Romania as an emergent country has an less develop stock markets which is strongly connected with the markets from Western Europe and the USA, so the Romanian Stock Market was one of the first market touch by the wave of financial crises. We analize thstock market, EMH , investment fond, active investment, passive investment

How does assets-liabilities management affects the profitability of banks?

The economic crisis has affected the stability of the financial institutions (banks) and the instability from the banking sector affected the real economy. Some banks were affected more than others and in this paper we analyze the stability and profitability of banks from the point of the asset-liability management.  Assets-liabilities management (ALM) is the management of risk at bank level, the structure of the assets and liabilities of the banks may show which are the differences between the "good banks" and "bad banks". The main goal of this paper is to analyze the asset-liability management in banks for the 2004-2011 period, using a panel of over 30 banks. The analysis is carried using the canonical correlations  (Hotelling, 1936), while in the case of the simple correlation we test for a linear dependency between two variables, canonical correlation test the interdependence between two sets of variables (the structure of assets and liabilities

An Econometric model for the evolution of the Romanian Interbank Bid Rate (ROBID) in the context of the international financial crisis

The paper presents the econometric modeling of overnight inter-banking interest rates (ROBID) in our country, the analyzed period is between 1999-2010. The international financial crises had a great impact on the level of inter-banking interest rates after 2007 and it reflects the new level of risk for the Romanian system banking. The econometric model used in modeling the interest rates is an autoregressive moving average (ARMA) model, the ARMA model is typically applied to time series data; the paper propose several ARMA models, applies econometric tests and based on them the analyzed series (the inter-banking interest rates) forecast will be made.ROBID, ARIMA model, financial crisis, forecast.

Testing the Presence of Structural Break in the Euro Exchange Rate Variance

AbstractWe investigate the presence of structural breaks in the euro exchange rate conditional variance from the GARCH models and how does it influence the Value at Risk metrics. We use daily exchange rate for the euro/dollar, euro/£, euro/CHF, euro/yen for the 01:1999 - 09:2013 in order to test the influence of structural breaks on the GARCH models. The structural break in the series mean and variance are identified using the PELT algorithm, the structural breaks dates are captured using dummy variables in the GARCH models, the selection of models is done using the informational criterion [Akaike, Schwarz, Log-likelihood]. We find evidence of structural breaks in the unconditional variance of euro exchange rate return series over the 1999-2013 period, we test the performance of the structural GARCH(1,1) models versus the GARCH(1,1) and find that structural GARCH models outperforms when forecasting exchange rate return volatility in real time

Backtesting value at risk models in the presence of structural breaks on the Romanian and Hungarian stock

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NEW INTERNATIONAL FINANCIAL REGULATION: NECESSITY OR REQUIRED BY CRISIS

The global economic and financial crisis showed the limits faced by the international financial system. International financial regulations in general, and especially the banking sector regulations, should be refined and adapted to build a stronger and stable international financial system. We analyze the main trends in international regulations: the proposed amendments on capital requirements, the introduction of a global standard for liquidity and indebtedness, the winding-up directive, as well as their impact on the Romanian financial system.international regulations, global standard, winding-up directive

MODELING ROMANIAN EXCHANGE RATE EVOLUTION WITH GARCH, TGARCH, GARCH- IN MEAN MODELS

In this paper we analyze the return of exchange rate in order to test and analyze the best models which are capable of forecasting accurately there evolution. We apply the GARCH family models on the exchange rate return in order to obtain the best models for there volatility. Financial time series often exhibit abnormal characteristics, such as: serial correlation, non-stationarity, heteroskedasticity, asymmetric and are leptokurtic. Due to these characteristics autoregressive models such as autoregressive (AR), moving average (MA) and autoregressive integrated moving-average (ARIMA) are unable to capture the evolution of financial series, to represent the special characteristic of financial a hole new range of models where developed : generalized autoregressive conditional heteroskedasticity (GARCH), which are taking into account the heteroskedasticity of the errors term. The GARCH model allows for lags in the autoregressive term and in the variance term incorporates lags of the previous variance and also for the errors. The GARCH family has expanded in the last years in order to incorporate for asymmetry (Threshold GARCH, TGARCH) and risk (GARCH -in Mean). We analyze the evolution of exchange rate for: Euro/RON, dollar/RON, yen/RON, British pound/RON, Swiss franc/RON for a period of five years from 2005 till 2011, we observe that in the analyzed period there are 2 sub-periods: 2005-2007 in which the RON appreciated constantly, and 2007-2011 in which the trend is depreciation for RON in respect to all the five currencies and the volatility was sensible higher than in the previous period. We obtain the returns on exchange rate by using the following transformation r=log(curs_t)-log(curs_t-1); the five analyzed series display an leptokurtic and asymmetric behavioral. Using the GARCH, TGARCH and GARCH-in Mean models, we explicit the evolution of volatility throw this period, choosing the best model using the following : minimizing the value of the sum of squared errors, Akaike and Bayesian Information Criterion

ASSETS AND LIABILITIES DEPENDENCE: EVIDENCE FROM AN EUROPEAN SAMPLE OF BANKS

In this paper we analyzed the correlation between asset and liabilities using the canonical correlation method, in the case of correlation we analyze the interdependence between two variables, by using canonical correlation analyses we study the interdependence between two groups of variables, X consisting of p variables and Y with q variables from which the best linear combination can be constructed to maximize the correlation between X and Y. While on the financial markets the relation between variables may be linear or non-linear and although canonical correlation analyses only the linear combination of variables it is a more efficient tool than then simple correlation.The asset group which we analyze is composed of different types of loans, derivatives and other earning assets, while in the group of liabilities we have deposits (short and long term), interest bearing liabilities and trading liabilities. We find that the assets and liabilities in the banking sector are directly linked. In the context of the global financial crisis (2007-2008) and the afterwards financial recession this direct correlation between assets and liabilities created a vicious cycle in which the losses from assets had a direct impact on the liabilities which also influenced the levels of assets.The behavior of different variables is important, especially in the financial markets, mainly due to the structure of financial markets. The banking sector and the systemic risk associated with it can affect the financial system and even the whole economy so the study of the correlation of assets and liabilities may give us insights on the causes of the financial crises. We use a panel of fifty-nine European banks for the 2004-2011 period and we analyses the correlation between assets and liabilities. We find that there exists a direct and strong connection between different classes of assets held by banks and the structure of liabilities. The impact of the economic crisis on the banking sector has shown that this kind of connection between the structure of assets and liabilities is not the best choice because a negative fluctuation in assets generates a negative impact on the structure of liabilities. The direct connection between assets and liabilities amplifies the systemic risk of the banking sector and can also have an impact on other markets due to their spillover effects