116 research outputs found
Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets
No full textCifter, Atilla/0000-0002-4365-742XThis paper introduces wavelet-based extreme value theory (EVT) for univariate value-at-risk estimation. Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model. In the first stage, wavelets are used as a threshold in generalized Pareto distribution, and in the second stage, EVT is applied with a wavelet-based threshold. This new model is applied to two major emerging stock markets: the Istanbul Stock Exchange (ISE) and the Budapest Stock Exchange (BUX). The relative performance of wavelet-based EVT is benchmarked against the Riskmetrics-EWMA, ARMA-GARCH, generalized Pareto distribution, and conditional generalized Pareto distribution models. The empirical results show that the wavelet-based extreme value theory increases predictive performance of financial forecasting according to number of violations and tail-loss tests. The superior forecasting performance of the wavelet-based EVT model is also consistent with Basel II requirements, and this new model can be used by financial institutions as well. (C) 2011 Elsevier B.V. All rights reserved
Industrial production as a credit driver in banking sector: An empirical study with wavelets
No full textThis paper examines the timescale effects of industrial production on credits volume at banks. By using industrial production in Turkey and credit volumes of Turkish banks from 3/1992-12/2006, this study employs wavelet filters to estimate multi-scale causality for scaled time series. The original data is transformed by the wavelet filter up to 5 time scales. The first wavelet coefficient captures oscillations with a period length 3 to 6 months. Equivalently, the consequent wavelets capture oscillations with a period of 7-12, 13-24, 25-48 and 49-96 months, respectively. The results of multi-scale granger causality test show that the industrial production is effective on credits volume upto 24 months, while the credits volume starts to affect industrial production after 2 years. This paper has originality in presenting multi-scale effects of industrial production as a credit driver by using wavelet analysis with Turkish data. © Alper Ozun, Atilla Cifter, 2007
Estimating the Effects of Interest Rates on Share Prices Using Multi-scale Causality Test in Emerging Markets: Evidence from Turkey
No full textThis 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 Effect of Scale on Productivity of Turkish Banks in the Post-Crises Period: An Application of Data Envelopment Analysis
No full textThe purpose of this paper is to investigate the productivity of Turkish Banks according to the effect of scale in the Post-Crises Period. The data used in this study covers the period from 2002:1 to 2004:3. We applied Data Envelopment Analysis (DEA), which is a non-parametric linear programming-based technique for measuring relative performance of decision-making units (DMUs). We calculated DEA as constant & variable return-to-scale based on output oriented Malmquist Index. Although the scale effect can be measured with DEA scale efficiency measurement, we used scale indicators as input variables in order to find out not only scale efficiency but also scale affect directly. We applied DEA by using financial ratios (Athanassopoulos and Ballantine, 1995; Yeh, 1996) and branch & personel number indicators. This study uses five input variables as i) branch numbers, ii) personnel number per branch, iii) share in total assets, iv) share in total loans, v) share in total deposits; and five output variables as i) net profit-losses/total assets (ROA), ii) net profit-losses/total shareholders equity (ROE), iii) net interest income/total assets, iv) net interest income/ total operating income, and v) noninterest income/total assets. We find that difference in efficiency is mainly from technical efficiency rather than scale efficiency in the post-crises period. The other finding reveals that efficiency approximate between selected banks and supporting that advantage of scale economies can be lost in Turkish banking. Overall, the results confirm that Turkish banking has U shaped Scale Efficiency on selected profitability ratios. The application of this paper based on other financial ratios with decreasing and increasing return-to-scale DEA is left to future research.Turkish Banks; Return to Scale; Scale Efficiency; Profit Efficiency; Data Envelopment Analysis
The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey
No full textThe 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
Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey
No full textIn this paper, we investigate the relationship between industrial production and sectoral credit defaults (non-performing loans ratio) cycle by wavelet network analysis in Turkey over the period January 2001-November 2007. We use feedforward neural network based wavelet decomposition to analyze the contemporaneous connection between industrial production cycles and sectoral credit default cycles at different time scales between 2 and 64 months. The main findings for Turkey indicates that industrial production cycles effect the sectoral credit default cycles at different time scales and thus indicate that the creditors should consider the multiscale sectoral cycles in order to minimize credit default rates. (C) 2009 Elsevier B.V. All rights reserved
The Monetary Transmission Mechanism in the New Economy
No full textThe Monetary Transmission Mechanism in the New Economy: Evidence from Turkey (1997-2006) This study aimed 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. These were tested through the monetary transmission mechanism and passive money hypothesis, using the vector error correction model-based causality test. Empirical findings showed that the passive money supply hypothesis of the new Keynesian economy is supported in part by accommodationalist views and differs from those of structuralist and liquidity preference theories. However, the monetary transmission mechanism has established that long-term money supply only affects general price levels, while production is influenced by interest rates in the new period of the Turkish economy. Empirical findings show that in this new period, interest transmission mechanisms are at the forefront
Filtered Extreme Value Theory for Value-At-Risk Estimation
No full textExtreme 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
Stock returns, inflation, and real activity in developing countries: A Markov-switching approach
No full textThis paper empirically investigates the relationship between real stock
returns, inflation, and real activity using the Markov-switching dynamic
regression (MS-DR) approach. The MS-DR allows multiple structural breaks in
the estimation, and we can check regression coefficients separately in the
recession and expansion periods. We selected two major developing countries
(Mexico and South Africa) in order to reduce location bias. We use real stock
returns, expected inflation, unexpected inflation, and real GDP growth in the
estimations, and the ARFIMA model is used for unexpected inflation. The
empirical results show that the relationship between real stock returns and
inflation is negative only in the recession period. This regime-dependency is
also tested with Eugene F. Fama’s (1981) proxy effect hypothesis, and it is
found that the stock returns respond differently to inflation in a regime
according to the regime-dependent proxy effect hypothesis. These findings
suggest that the negative relationship puzzle in the empirical finance
literature can be explained with the regime-dependency effect
Modeling Long-Term Memory Effect in Stock Prices: A Comparative Analysis with GPH Test and Daubechies Wavelets
No full textLong-term memory effect in stock prices might be captured, if any, with alternative models. Though Geweke and Porter-Hudak (1983) test model the long memory with the OLS estimator, a new approach based on wavelets analysis provide WOLS estimator for the memory effect. This article examines the long-term memory of the Istanbul Stock Index with the Daubechies-20, Daubechies-12, the Daubechies-4 and the Haar wavelets and compares the results of the WOLS estimators with that of OLS estimator based on the Geweke and Porter-Hudak test. While the results of the GPH test imply that the stock returns are memoryless, fractional integration parameters based on the Daubechies wavelets display that there is an explicit long-memory effect in the stock returns. The research results have both methodological and practical crucial conclusions. On the theoretical side, the wavelet based OLS estimator is superior in modeling the behaviours of the stock returns in emerging markets where nonlinearities and high volatility exist due to their chaotic natures. For practical aims, on the other hand, the results show that the Istanbul Stock Exchange is not in the weak-form efficient because the prices have memories that are not reflected in the prices, yet.Long-term memory; Wavelets; Stock prices; GPH test
- …
