The role of Regime Shifts in the Term Structure of Interest Rates: Further evidence from an Emerging Market

In this paper, we investigate the interrelations among Turkish interest rates with different maturities by using a regime switching Vector Error Correction (VECM) model. We find a long run equilibrium relationship among interest rates with various maturities. Furthermore we conclude that term structure dynamics exhibit significant nonlinearity. Forecasting experiment also reveals that the nonlinear term structure models do fare better than other linear specifications. However, we cannot conclude that interest rate adjustments are made in an asymmetric way in the long run equilibrium.Term Structure of Interest Rates, Regime Switching, Forecasting, Foreacast Evaluation, Cointegration

Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash

A financial network model, where the coded identity of the counterparties of every trade is known, is applied to both stable and crisis periods in a large and liquid overnight repo market in an emerging market economy. We have analyzed the financial crisis by using various network investigation tools such as links, interconnectivity, and reciprocity. In addition, we proposed a centrality measure to monitor and detect the ‘systemically important financial institution’ in the financial system. We have shown that our measure gives strong signals much before the crisis.systemic risk, financial regulation, financial crisis, BASEL III, systemically important financial institution, Turkey, IMF

Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets

We explore the relative weekly stock market volatility forecasting performance of the linear univariate MIDAS regression model based on squared daily returns vis-a-vis the benchmark model of GARCH(1,1) for a set of four developed and ten emerging market economies. We first estimate the two models for the 2002-2007 period and compare their in-sample properties. Next we estimate the two models using the data on 2002-2005 period and then compare their out-of-sample forecasting performance for the 2006-2007 period, based on the corresponding mean squared prediction errors following the testing procedure suggested by West (2006). Our findings show that the MIDAS squared daily return regression model outperforms the GARCH model significantly in four of the emerging markets. Moreover, the GARCH model fails to outperform the MIDAS regression model in any of the emerging markets significantly. The results are slightly less conclusive for the developed economies. These results may imply superior performance of MIDAS in relatively more volatile environments.Mixed Data Sampling regression model; Conditional volatility forecasting; Emerging Markets

Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash

A financial network model, where the coded identity of the counterparties of every trade is known, is applied to both stable and crisis periods in a large and liquid overnight repo market in an emerging market economy. We have analyzed the financial crisis by using various network investigation tools such as links, interconnectivity, and reciprocity. In addition, we proposed a centrality measure to monitor and detect the ‘systemically important financial institution’ in the financial system. We have shown that our measure gives strong signals much before the crisis

The role of Regime Shifts in the Term Structure of Interest Rates: Further evidence from an Emerging Market

In this paper, we investigate the interrelations among Turkish interest rates with different maturities by using a regime switching Vector Error Correction (VECM) model. We find a long run equilibrium relationship among interest rates with various maturities. Furthermore we conclude that term structure dynamics exhibit significant nonlinearity. Forecasting experiment also reveals that the nonlinear term structure models do fare better than other linear specifications. However, we cannot conclude that interest rate adjustments are made in an asymmetric way in the long run equilibrium

The role of Regime Shifts in the Term Structure of Interest Rates: Further evidence from an Emerging Market

In this paper, we investigate the interrelations among Turkish interest rates with different maturities by using a regime switching Vector Error Correction (VECM) model. We find a long run equilibrium relationship among interest rates with various maturities. Furthermore we conclude that term structure dynamics exhibit significant nonlinearity. Forecasting experiment also reveals that the nonlinear term structure models do fare better than other linear specifications. However, we cannot conclude that interest rate adjustments are made in an asymmetric way in the long run equilibrium

Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets

We explore the relative weekly stock market volatility forecasting performance of the linear univariate MIDAS regression model based on squared daily returns vis-a-vis the benchmark model of GARCH(1,1) for a set of four developed and ten emerging market economies. We first estimate the two models for the 2002-2007 period and compare their in-sample properties. Next we estimate the two models using the data on 2002-2005 period and then compare their out-of-sample forecasting performance for the 2006-2007 period, based on the corresponding mean squared prediction errors following the testing procedure suggested by West (2006). Our findings show that the MIDAS squared daily return regression model outperforms the GARCH model significantly in four of the emerging markets. Moreover, the GARCH model fails to outperform the MIDAS regression model in any of the emerging markets significantly. The results are slightly less conclusive for the developed economies. These results may imply superior performance of MIDAS in relatively more volatile environments

Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets

We explore the relative weekly stock market volatility forecasting performance of the linear univariate MIDAS regression model based on squared daily returns vis-a-vis the benchmark model of GARCH(1,1) for a set of four developed and ten emerging market economies. We first estimate the two models for the 2002-2007 period and compare their in-sample properties. Next we estimate the two models using the data on 2002-2005 period and then compare their out-of-sample forecasting performance for the 2006-2007 period, based on the corresponding mean squared prediction errors following the testing procedure suggested by West (2006). Our findings show that the MIDAS squared daily return regression model outperforms the GARCH model significantly in four of the emerging markets. Moreover, the GARCH model fails to outperform the MIDAS regression model in any of the emerging markets significantly. The results are slightly less conclusive for the developed economies. These results may imply superior performance of MIDAS in relatively more volatile environments

Anatomy of a market crash: a market microstructure analysis of the Turkish overnight liquidity crisis

An order flow model, where the coded identity of the counterparties of every trade is known, hence providing institution level order flow, is applied to both stable and crisis periods in a large and liquid overnight repo market in an emerging market economy. Institution level order flow is much more informative than cross sectionally aggregated order flow. The informativeness of institution level order flow increases with financial instability, with considerable heterogeneity in the yield impact across institutions