A Duration-Dependent Regime Switching Model for an Open Emerging Economy

We employ duration-dependent Markov-switching vector auto-regression (DDMSVAR) methodology to construct an economic cycle model for an emerging economy. By modifying the software codes for DDMSVAR methodology written by Pelagatti (2003), we show how to estimate the economic cycles in an emerging economy where macroeconomic shocks are suddenly observed and their levels are deep. The monthly values of net international reserves, domestic debt, inflation and industrial production in the Turkish economy from January 1989 to July 2007 are used for constructing the empirical analysis. Empirical evidence shows that DDMSVAR model can be successfully used in an emerging economy to estimate the cycles using basic macroeconomic indicators.duration dependent regime switching model, economic cycles, Markov models, Turkish economy

Nonlinear Combination of Financial Forecast with Genetic Algorithm

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH models are combined with Extreme Value Theory Hill by using artificial neural networks with genetic algorithm as the combination platform. By employing daily closing values of the Istanbul Stock Exchange from 01/10/1996 to 11/07/2006, Kupiec and Christoffersen tests as the back-testing mechanisms are performed for forecast comparison of the models. Empirical findings show that the fat-tails are more properly captured by the combination of GARCH with skewed student-t distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of Value-at-Risk models to capture the extreme movements in the markets rather than individual model forecast.Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test

Portfolio Value-at-Risk with Time-Varying Copula: Evidence from the Americas

Model risk in the estimation of value-at-risk is a challenging threat for the success of any financial investments. The degree of the model risk increases when the estimation process is constructed with a portfolio in the emerging markets. The proper model should both provide flexible joint distributions by splitting the marginality from the dependencies among the financial assets within the portfolio and also capture the non-linear behaviours and extremes in the returns arising from the special features of the emerging markets. In this paper, we use time-varying copula to estimate the value-at-risk of the portfolio comprised of the Bovespa and the IPC Mexico in equal and constant weights. The performance comparison of the copula model to the EWMA portfolio model made by the Christoffersen back-test shows that the copula model captures the extremes most successfully. The copula model, by estimating the portfolio value-at-risk with the least violation number in the back-tests, provides the investors to allocate the minimum regulatory capital requirement in accordance with the Basel II Accord.Time-varying Copula; portfolio value-at-risk; Latin American equity markets; portfolio GARCH

Multi-scale Causality between Energy Consumption and GNP in Emerging Markets: Evidence from Turkey

Tests results for causality between energy consumption and economic growth do not have a consensus in the financial economics literature. Empirical evidence varies on the economies examined and methodology employed. This paper proposes a wavelet analysis as a semi- parametric model for detecting multi-scale causality between electricity consumption and growth in emerging economies. Using wavelet analysis we find that in the short run there is feedback relationship between GNP and energy consumption, while in the long run GNP leads to energy consumption. Wavelet correlation between GNP and energy consumption is maximum at 3rd time-scale(5-8 years) and this shows that GNP effects electricity consumption maximally around 5-8 years later in the long-run. We also find that the magnitude of the wavelet correlation changes based on time-scales for GNP and energy consumption and thus indicate that GNP and energy consumption are fundamentally different in the long run.Economic Growth; Energy Consumption; Employment; Wavelets; Causality

Monetary Transmission Mechanism in the New Economy: Evidence from Turkey (1997-2006)

This study aims to test the money base, money supply, credit capacity, industrial production index, interest rates, inflation and real exchange rate data of Turkey during the years 1997 – 2006 through the monetary transmission mechanism and passive money hypothesis using the vector error correction model based causality test. Empirical findings show that the passive money supply hypothesis of the new Keynesian economy is supported in part by accommodationalist views and they do not confirm to the view points of structuralist and liquidity preference theorist. However, according to the monetary transmission mechanism it has been established that long-term money supply only affects general price levels and production is influenced by interest rates in the new economy period for Turkish economy. Empirical findings show that in the new economy period interest transmission mechanism are brought to the fore.Monetary transmission mechanism; money supply endogeneity; Credit; New Keynesian Economy

The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets

This paper proposes a powerful methodology wavelet networks to investigate the effects of international F/X markets on emerging markets currencies. We used EUR/USD parity as input indicator (international F/X markets) and three emerging markets currencies as Brazilian Real, Turkish Lira and Russian Ruble as output indicator (emerging markets currency). We test if the effects of international F/X markets change across different timescale. Using wavelet networks, we showed that the effects of international F/X markets increase with higher timescale. This evidence shows that the causality of international F/X markets on emerging markets should be tested based on 64-128 days effect. We also find that the effects of EUR/USD parity on Turkish Lira is higher on 17-32 days and 65-128 days scales and this evidence shows that Turkish lira is less stable compare to other emerging markets currencies as international F/X markets effects Turkish lira on shorten time scale.F/X Markets; Emerging markets; Wavelet networks; Wavelets; Neural networks

Further Evidence on Defence Spending and Economic Growth in NATO Countries

The main purpose of this paper is to analyze the causal relationships between defence spending and economic growth using the Toda–Yamamoto approach to Granger causality test in the case of selected NATO countries for the period of 1949-2006. NATO countries spend biggest proportion of defence spending in the world. Granger causality test on defence-growth issue employed by number of scholars but this paper is firstly used Toda–Yamamoto approach to granger causality to analyze relationship between defence spending and growth. The results show that unidirectional causality exists in seven NATO countries while for five countries no causal relationships were found. On the other hand, Turkey differs from other countries in that the relationship is bilateral.defence spending, Turkish economy, Granger causality, NATO, economic growth, Toda–Yamamoto approach

Estimating the Effects of Interest Rates on Share Prices Using Multi-scale Causality Test in Emerging Markets: Evidence from Turkey

This paper examines the impacts of changes in interest rates on stock returns by using wavelet analysis with Granger causality test. Financial time series in non-coherent markets should be analyzed by advanced methods capturing complexity of the markets and non-linearities in stock returns. As a semi-parametric method, wavelets analysis might be superior to detect the chaotic patterns in the non-coherent markets. By using daily closing values of the ISE 100 Index and compounded interest rates, it is proven that and starting with 9 days time-scale effect interest rate is granger cause of ISE 100 index and the effects of interest rates on stock return increases with higher time-scales. This evidence shows that bond market has significant long-term effect on stock market for Turkey and traders should consider long-term money markets changes as well as short-term changes.Interest rates; Emerging markets; Wavelets; Stock returns; Multi-scale Granger causality

The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey

The purpose of this study is to test predictive performance of Asymmetric Normal Mixture Garch (NMAGARCH) and other Garch models based on Kupiec and Christoffersen tests for Turkish equity market. The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen test where Garch with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results show that none of the model including NMAGARCH outperforms other models in all cases as trading position or confidence intervals and these results shows that volatility model should be chosen according to confidence interval and trading positions. Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires.Garch; Asymmetric Normal Mixture Garch; Kupiec Test; Christoffersen Test; Emerging markets

Filtered Extreme Value Theory for Value-At-Risk Estimation

Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) h-step ahead forecasting RMSE (70 days), number of exception and h-step ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fat-tails in the stock returns than parametric value-at-risk models do. Besides increase in conditional quantile decreases h-step ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting.Value at-Risk; Filtered Expected shortfall; Extreme value theory; emerging markets