دانلود مقاله ISI انگلیسی شماره 19525
عنوان فارسی مقاله

معافیت و تداوم : علل اقتصاد کلان در نوسانات بازار سهام

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
19525 2006 27 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Breaks and persistency : macroeconomic causes of stock market volatility
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Econometrics, Volume 131, Issues 1–2, March–April 2006, Pages 151–177

کلمات کلیدی
نوسانات بازار سهام - نوسانات اقتصاد کلان - حافظه بلند مدت - هم انباشتگی جزء به جزء - تغییرات ساختاری -
پیش نمایش مقاله
پیش نمایش مقاله معافیت و تداوم : علل اقتصاد کلان در نوسانات بازار سهام

چکیده انگلیسی

In the paper we study the relationship between macroeconomic and stock market volatility, using S&P500 data for the period 1970–2001. We find evidence of a twofold linkage between stock market and macroeconomic volatility. Firstly, the break process in the volatility of stock returns is associated with the break process in the volatility of the Federal funds rate and M1 growth. Secondly, two common long memory factors, mainly associated with output and inflation volatility, drive the break-free volatility series. While stock market volatility also affects macroeconomic volatility, the causality direction is stronger from macroeconomic to stock market volatility.

مقدمه انگلیسی

Why does stock market volatility change over time? This questions was asked by Schwert (1989) at the end of the 1980s. His goal was to explain the time-varying stock return volatility by means of the time-varying volatility of macroeconomic and financial variables. The basic conclusion of the paper was that “the amplitude of the fluctuations in aggregate stock volatility is difficult to explain using simple models of stock valuation”. Schwert (1989) also found mixed results with respect to the direction of causality between return volatility and the volatility of macroeconomic and financial variables. He found that: (a) inflation volatility predicts stock volatility but only for the sub-period 1953–1987 and stock volatility does not predict inflation volatility, (b) money growth volatility predicts stock volatility in various sub-samples and stock volatility predicts money growth volatility from 1920 to 1952, (c) industrial production volatility weakly explains the volatility of stock returns, while stock volatility helps to predict industrial production volatility in two sub-samples. Overall his results point to a positive linkage between macroeconomic volatility and stock market volatility, with the direction of causality being stronger from the stock market to the macroeconomic variables. Moreover, the level of macroeconomic volatility explains less than half of the volatility of stock returns. In some periods the ratio is even lower: in 1929–1939 the volatility of macroeconomic variables increased but not by a factor of three as in the case of stock return volatility. Finally, he found evidence that stock market uncertainty is higher during recessions than expansions.1 A weakness in Schwert (1989) is that it does not accurately model the persistence properties of volatility and it ignores the potential downward bias affecting the estimates, due to the use of noisy volatility proxies. In fact, since Schwert's study there have been many advances in the theoretical and empirical understanding of econometric models for time-varying volatility. Many studies have focused on the causes of persistence of volatility of asset returns, pointing to the presence of structural change, long memory, or both. For instance, Hamilton and Susmel (1994) have found that the conditional variance process of the US stock market can be described by a switching regime model with three persistent states. The interpretation of the authors is that the high volatility state was triggered by general business downturn. These findings have largely been confirmed by So et al. (1998) and Hamilton and Lin (1996), while Kim and Kim (1996) have suggested that the switch to the high volatility state may be due to an increased volatility in the fad component of the returns, rather than to an increase in the volatility of fundamentals. Evidence of switching regimes in the conditional variance process have been also found for some European countries by Morana and Beltratti (2002). The alternative explanation of long range dependence has been also proposed to account for persistence of the conditional variance process (see for instance Ding et al., 1993; Baillie et al., 1996, Bollerslev and Mikkelsen, 1996 and Andersen and Bollerslev, 1997), with long memory being the consequence, for instance, of the cross-sectional aggregation of a large number of volatility components or news information arrival processes with different degrees of persistence (Granger, 1980 and Andersen and Bollerslev, 1997). While some recent contributions have cast doubts on the hypothesis that long memory is a real feature of the data generating process of the volatility of financial returns (Granger and Hyung, 2004 and Mikosch and Starica, 1998), other authors have suggested that both long memory and structural change characterize the structure of financial returns volatility (Morana and Beltratti, 2004 and Lobato and Savin, 1998). In this paper, we provide further evidence on the economic causes of volatility persistence for stock market returns. In particular, we study the relationship between S&P500 returns volatility and the volatility of some macroeconomic factors over the period 1970–2001. Improving on Schwert (1989), we take into account recent evidence about the stochastic process followed by volatility, and allow for both long memory and structural breaks. This allows us to study the relations among breaks in the series and among break-free series. Moreover, in order to account for the presence of observational noise, we extend the nonlinear log periodogram estimator of Sun and Phillips (2003) to the multivariate case and develop a semiparametric noise filtering approach for perturbed long memory processes. Finally, and contrary to what previously done in the literature, we follow a structural approach. We exploit the long-run properties of the volatility processes investigated and identify the cointegration space and the sources of persistent volatility dynamics. We believe there are merits in the proposed analysis since we provide evidence of short- and long-term linkages, which could not be determined or disentangled by using a single component approach. An econometric analysis which does not account for multiple components of volatility cannot disentangle meaningful relations among the volatility series in our sample.2 However, as it will be shown in the paper, accurately modelling the persistence properties of the series and accounting for observational noise allows to draw much different conclusions concerning the linkages between macroeconomic and stock market volatility. Particularly important for justifying our approach, we show that the relations between volatility of the variables change depending on the specific component under investigation. Our main results are the following. First, we find evidence that the process describing volatility of the US market is characterized by both long memory and structural change. We confirm an important result obtained by Campbell et al. (2001), that is that the post 1995 period has not witnessed an increase in overall market volatility, but we qualify it because we show that the length of time spent in the high volatility regime is unusual compared to the past. Second, the break process in stock market volatility can be related to the break process in the volatility of macroeconomic factors, Federal funds rate and M1 growth in particular, with the causality direction of this linkage being stronger from macroeconomic volatility to stock market volatility. Third, fractional cointegration analysis, carried out on the break-free log variance processes, points to the existence of three cointegrating vectors, linking output growth, money growth, stock market, the Federal funds rate, and inflation volatility. Fourth, on the basis of the variance decomposition analysis, we find that the two common long memory factors driving the five processes are largely explained by output and inflation volatility. Fifth, our decomposition suggests that a 1% stock market volatility increase is determined by a 0.85% increase in the nonpersistent component and a 0.15% in the persistent component. Sixth, the findings point to the existence of causality linkages which are stronger from macroeconomic volatility to stock market volatility than the other way around: macroeconomic volatility contributes to both persistent and nonpersistent stock market volatility fluctuations, albeit the bulk of stock market volatility fluctuations are largely associated with idiosyncratic “financial” shocks; stock market volatility exercises only a limited influence on macroeconomic volatility. The rest of the paper is organized as follows. In Section 2 we introduce the econometric methodology, and in Section 3 we investigate the time series properties of the data and the relationship between macroeconomic and stock market volatility. Finally, in Section 4 we conclude.

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