جنگ با تروریسم و تاثیر آن بر نوسانات طولانی مدت بازارهای مالی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|14506||2008||26 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Review of Financial Analysis, Volume 17, Issue 1, 2008, Pages 1–26
In this article, we analyze how the U.S.' declaration of the war on terror and the subsequent invasion of Iraq has impacted long-term volatility of stock markets around the world. In doing so, we utilize two statistical techniques: wavelet-based variance analysis and a semi-parametric fractional autoregressive (SEMIFARIMA) model. Our sample comprises stock and commodity indices worldwide for the sample period January 2000–June 2006. Specifically, we consider four geographic regions: the Americas, Africa/Middle East, Europe, and Asia/Pacific. We conclude that political instability in the Middle East had its greatest impact on the volatility of financial markets around the beginning of the Iraq war, and it mostly hit developed markets (e.g., United States, United Kingdom, and Japan). Thereafter, for most sampled indices, volatility has exhibited a decreasing trend to reach eventually levels even lower than that observed at the beginning of our sample. An exception is Egypt's CMA and the Dow Jones AIG all commodities. We think that the latest political conflicts in the Middle East and their impact on the price of oil may be the most likely driving force of such volatility in those two indices. Specifically, among Egypt's main export products are petroleum and petroleum products.
Measuring volatility of financial returns has been the center of attention of several studies in the past two decades (see, for instance, the survey article by Poon & Granger, 2003). Within this literature, a subject that has become particularly relevant in light of recent worldwide events is the long-term effect of political instability on the fluctuations of stock markets. In particular, capturing permanent volatility shifts has been the focus of recent studies, which have concentrated on the Asian crisis and 9/11, among other themes. For instance, Hammoudeh and Li (2008) examine sudden changes in volatility for five Gulf area Arab stock markets by means of Inclan and Tiao (1994)'s iterative cumulative sum of squares (ICSS) algorithm,1 and analyze their impacts on the estimated persistence of volatility. Their study finds that most Gulf Arab stock markets are more sensitive to major global events than to local and regional factors. The 1997 Asian crisis, the collapse of oil prices in 1998 after the crisis, the adoption of the price band mechanism by OPEC in 2000, and the 9/11 attack are found to have consistently affected the Gulf markets. Fernandez (2006a) in turn analyzes whether the Asian crisis and the terrorist attacks of 9/11 caused permanent volatility shifts in the stock markets around the globe. She focuses on eight MSCI stock indices that comprise developed and emerging economies, and test for the presence of volatility breakpoints by the ICSS algorithm and wavelet-variance analysis. Her estimation results show that the number of shifts detected by the two methods decreases considerably by filtering out the data for both conditional heteroskedasticity and serial correlation. In particular, for filtered returns, the ICSS algorithm fails to find any volatility shifts over 1997–2002, whereas wavelet analysis finds evidence of volatility breakpoints at the lower scales of the data (i.e., short-term dynamics). Moreover, she concludes that there were no dramatic changes in the magnitude of the standard deviation of filtered returns at different time scales and across the sample period. An advantage of wavelet-variance analysis over the ICSS algorithm is that it makes it possible to detect variance shifts across different time scales. That is, it enables us to distinguish between variance shifts at the high- and low-frequency components of the data (i.e., short- and long-term dynamics, respectively). Wavelets have been applied in several studies of the fields of economics and finance from the mid-1990's onwards. Early studies in this area are Ramsey, Usikov, and Zaslavsky (1995) and Ramsey and Zhang, 1996 and Ramsey and Zhang, 1997, which concentrate on stock markets and foreign exchange rate dynamics. More recent contributions have dealt with the permanent income hypothesis, the relation between futures and spot prices, the estimation of systematic risk of an asset in the context of the domestic and international version of the capital asset pricing model (CAPM), seasonality filtering of time series, time and scale dependency of intraday Asian spot exchange rates, heterogeneous trading in commodity markets, structural breakpoints in volatility and wavelet-based computation of value at risk, among other themes (e.g., Connor and Rossiter, 2005, Fernandez, 2005, Fernandez, 2006b, Fernandez and Lucey, 2007, Gençay et al., 2001, Gençay et al., 2003, Gençay et al., 2005, In and Kim, 2006, Karuppiah and Los, 2005, Lin and Stevenson, 2001, Ramsey and Lampart, 1998 and Whitcher, 2004). Two survey articles on the use of wavelets in economics and finance are provided by Ramsey, 1999 and Ramsey, 2002. The focus of this article is to study whether long-term (i.e., unconditional) volatility of worldwide stock markets has undergone permanent shifts due to the current political instability in the Middle East, primarily caused by the invasion of Iraq and the ongoing Israeli–Palestinian conflict. To that end, we resort to wavelet-variance analysis and to a semi-parametric version of a fractional autoregressive (SEMIFAR) model. As stated earlier, wavelet-variance analysis enables us to test for the presence of structural breaks in variance at different time horizons. In turn the SEMIFAR specification, which is discussed in detail in Beran and Ocker (1999), makes it possible to estimate the trend component of volatility non-parametrically, and see how this evolves over time. In order to infer daily volatility, we resort to one of Garman and Klass (1980)'s estimators, which is computed on the basis of the daily opening, highest, lowest, and closing prices. Our data set comprises stock and commodity indices worldwide for the sample period January 2000–June 2006 at a daily frequency. We focus on four geographic regions: the Americas, Africa/Middle East, Europe, and Asia/Pacific. The indices considered are AMEX Major Market Index (US), IPC (Mexico), TASE 100 (Israel), CMA (Egypt), KSE (Pakistan), FTSE 100 (UK), DAX (Germany), CAC 40 (France), IBEX 35 (Spain), BSE Sensex (India), Kospi (South Korea), Nikkei 225 (Japan), ASX all ordinaries (Australia), Jakarta composite (Indonesia), and two commodities indices: PHLX Gold and Silver and Dow Jones AIG commodity index (DJAIG). Our findings show that the greatest impact of such political instability was around the beginning of the Iraq war, and that the major international stock markets were those which became relatively more volatile around that time. Thereafter, volatility in most stock markets analyzed has exhibited a decreasing trend to reach eventually levels even lower than at the beginning of our sample period (January 2000). An exception is Egypt's CMA and the Dow Jones AIG all commodities. In other words, volatility worldwide has primarily experienced transitory increments during 2000–June 2006, which can be associated with volatility clustering. In addition, we do not observe in general more volatile financial markets than at the beginning of 2000. This article is organized as follows. Section 2 provides a brief background on wavelets and refers to a wavelet-based statistic to detect variance shifts at different time scales. In addition, the semi-parametric fractional autoregressive model (SEMIFAR) and the Garman–Klass volatility estimate are discussed. Section 3 focuses on the empirical analysis, which is divided into two parts. We first test for variance homogeneity in the raw returns series. We next construct standardized residuals and test for variance homogeneity on those. To that end, we use two different filters: one controls for conditional heteroskedasticity and serial correlation, and the other one also allows for the presence of long memory and asymmetric effects in volatility. After testing for the presence of unconditional variance shifts, we concentrate on those indices which show evidence of the existence of such breaks. In doing so, we compute a rolling wavelet-variance estimate, which enables us to see how volatility has evolved over time at the different time scales of the data. We also analyze the behavior of the trend component of volatility by using a SEMIFAR model. Finally, Section 4 presents a summary of our main findings.
نتیجه گیری انگلیسی
In this article, we have focused on the impact of the political instability in the Middle East, following the invasion of Iraq, on the volatility of financial markets worldwide. To that end, we use two different statistical tools to identify firstly the presence of unconditional volatility shifts, and to quantify secondly the extent to which the level of volatility has changed over time. Specifically, we resort to wavelet-variance analysis and to a semi-parametric fractional autoregressive (SEMIFAR) model. The latter is fitted to a volatility estimate proposed by Garman and Klass. Our empirical analysis can be summarized as follows. We first test for variance homogeneity in the raw returns series and the standardized residuals obtained from two filter procedures. The first filter is an AR(1)-GARCH(1,1) model, which controls for conditional heteroskedasticity and inertia in the return series. The second filter is an AR(1)-FIEGARCH(1,1), which also allows for the presence of long memory and asymmetric effects in volatility. As reported in previous studies, by filtering the data, the null hypothesis of variance homogeneity tends to be accepted quite more often than otherwise. That implies that in most cases there are no permanent variance shifts but primarily volatility clustering (i.e., correlated conditional heteroskedasticity). After testing for the presence of unconditional variance shifts, we concentrate on those indices that show evidence of the existence of such breaks, even after filtering the raw returns. In doing so, we compute a rolling wavelet-variance estimate, which enables us to see how volatility has evolved over time at the different scales of the data. We also analyze the behavior of the trend component of volatility by using a SEMIFAR model. We conclude that volatility worldwide has primarily experienced transitory increments during 2000–June 2006, which can be primarily associated with volatility clustering. In addition, we do not observe in general more volatile financial markets than at the beginning of 2000.