5 resultados para Dynamic Conditional Correlation

em University of Connecticut - USA


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The effects of exchange rate risk have interested researchers, since the collapse of fixed exchange rates. Little consensus exists, however, regarding its effect on exports. Previous studies implicitly assume symmetry. This paper tests the hypothesis of asymmetric effects of exchange rate risk with a dynamic conditional correlation bivariate GARCH(1,1)-M model. The asymmetry means that exchange rate risk (volatility) affects exports differently during appreciations and depreciations of the exchange rate. The data include bilateral exports from eight Asian countries to the US. The empirical results show that real exchange rate risk significantly affects exports for all countries, negative or positive, in periods of depreciation or appreciation. For five of the eight countries, the effects of exchange risk are asymmetric. Thus, policy makers can consider the stability of the exchange rate in addition to its depreciation as a method of stimulating export growth.

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Exchange rate movements affect exports in two ways -- its depreciation and its variability (risk). A depreciation raises exports, but the associated exchange rate risk could offset that positive effect. The present paper investigates the net effect for eight Asian countries using a dynamic conditional correlation bivariate GARCH-M model that simultaneously estimates time varying correlation and exchange rate risk. Depreciation encourages exports, as expected, for most countries, but its contribution to export growth is weak. Exchange rate risk contributes to export growth in Malaysia and the Philippines, leading to positive net effects. Exchange rate risk generates a negative effect for six of the countries, resulting in a negative net effect in Indonesia, Japan, Singapore, Taiwan and a zero net effect in Korea and Thailand. Since the negative effect of exchange rate risk may offset, or even dominate, positive contributions from depreciation, policy makers need to reduce exchange rate fluctuation along with and possibly before efforts to depreciate the currency.

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In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

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The current international integration of financial markets provides a channel for currency depreciation to affect stock prices. Moreover, the recent financial crisis in Asia with its accompanying exchange rate volatility affords a case study to examine that channel. This paper applies a bivariate GARCH-M model of the reduced form of stock market returns to investigate empirically the effects of daily currency depreciation on stock market returns for five newly emerging East Asian stock markets during the Asian financial crisis. The evidence shows that the conditional variances of stock market returns and depreciation rates exhibit time-varying characteristics for all countries. Domestic currency depreciation and its uncertainty adversely affects stock market returns across countries. The significant effects of foreign exchange market events on stock market returns suggest that international fund managers who invest in the newly emerging East Asian stock markets must evaluate the value and stability of the domestic currency as a part of their stock market investment decisions.

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This paper explores the dynamic linkages that portray different facets of the joint probability distribution of stock market returns in NAFTA (i.e., Canada, Mexico, and the US). Our examination of interactions of the NAFTA stock markets considers three issues. First, we examine the long-run relationship between the three markets, using cointegration techniques. Second, we evaluate the dynamic relationships between the three markets, using impulse-response analysis. Finally, we explore the volatility transmission process between the three markets, using a variety of multivariate GARCH models. Our results also exhibit significant volatility transmission between the second moments of the NAFTA stock markets, albeit not homogenous. The magnitude and trend of the conditional correlations indicate that in the last few years, the Mexican stock market exhibited a tendency toward increased integration with the US market. Finally, we do note that evidence exists that the Peso and Asian financial crises as well as the stock-market crash in the US affect the return and volatility time-series relationships.