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The impact of subprime mortgage crisis to long-run and short-run volatility components of Indonesian and Malaysian equity markets
Abstract
This study investigates the long-run and short-run movements of various stock market volatilities using a volatility decomposition methodology (Ding and Granger, 1996; Engle and Lee, 1999). We studied the impact of 2007–2008 subprime mortgage crisis on both the transitory and permanent volatility components in terms of two empirical stylized facts, the leverage effect and volatility persistence. In order to do so, the long spanning data are separated into three different periods. For the former stylized fact, the Crisis Impact on the leverage effect is mainly temporary with no long-run effect to the stock markets. This finding explained that the leverage effect is mostly difficult to adjust in the short-run transitory volatility during the crisis periods. However with proper risk management and long term strategies, most of the market participants are able to anticipate and handle this news impact in the long-run. For the latter stylized fact, the crisis has slightly increased the volatility persistence in all the markets. From the viewpoint of heterogeneous market hypothesis, the higher intensity of volatility persistence implied that the stock markets are less informationally efficient.
... However, a surge in subprime loans in the early 2000s led to poor loan performance, exacerbating the crisis (Immergluck, 2015). Research (Cheong et al., 2012) and Amanda (2021) confirm the impact of the subprime mortgage crisis on the Indonesian stock market, and exchange rate volatility followed stock price fluctuations, with the Federal Reserve implementing Quantitative Easing (QE) to counter the recession (Dinata and Oktora, 2020) (See Fig. 2). 09-01-2008, 2,830 28-10-2008, 1,111 05-01-2007, 9,050 27-11-2008, 12 Furthermore, Viphindrartin et al. (2021) revealed the link between U.S. interest rates, money supply, and Indonesian GDP growth, while rising U.S. inflation correlated with decreased Indonesian GDP. ...
Numerous scholars have conducted studies on the conventional financial cycle, yet there is a lack of scholarly discourse on the distinct Islamic financial cycle. However, Islamic finance plays a significant role within a nation’s economic framework, particularly in regions with a predominantly Islamic population. This study integrates variables from Islamic banking, the Islamic capital market, and macroeconomics, covering the period from 2009 to 2022. The research employs the MSVAR methodology to identify and analyze transitions between distinct regimes. The findings indicate that the duration of the deceleration regime phase is longer than the accelerated regime phase, lasting approximately 9 years versus 5 years. Furthermore, the study reveals that the six variables examined significantly impact Indonesia’s economic growth, with each variable displaying a unique response to shocks.
... They also found that the half-life of volatility in an emerging market was shorter than in mature markets, and they emphasized that the impact of nontrading days on volatility was greater in mature markets than in emerging markets. Cheong et al. (2012) investigated the impact of the 2007-2008 subprime crises on long-run and short-run components of volatility on the US, Malaysia and Indonesian equity markets. Cheong divided the stock return data into three periods based on the subprime crisis timeline. ...
The aim of this paper is to investigate the return and volatility behaviors of the Turkish Stock Exchange in response to the mortgage crisis using daily observations for the period June 2004 to June 2014. The data are divided into three sub-periods to allow for the investigation of the behavior of the stock market during each sub-period. We employ the GARCH, EGARCH and GARCH-M models to capture volatility. The results indicate that the subprime crisis both induced a notable increase in volatility and changed the relationship between risk and expected return on the Turkish Stock Exchange.
... Dooley & Huttchison (2009) similarly found evidence that there were transmission effects of subprime crisis to other emerging stock markets, and emerging markets responded very strongly to the deteriorating situation in the US.This was consistent with the evidence provided by Longstaff (2010) regarding the strong contagion across markets from the credit crisis. Singhania & Anchalia (2013) investigated the impact of the 2008 subprime crisis on the Asian stock market and they found that the crisis affected the Asian financial markets,and Japan, China, and India were affected positively by the subprime crisis.There were many other studies in the literature about the effects of the crisis on other countries' stock markets, such as, Al-Rjoub & Azzam (2012) on the Jordanian stock exchange, Cheong et al. (2012) on the US, Malay, and Indonesian stock markets, Thao & Daly (2012) on the Southeast Asian stock markets, and Horta et al. (2008) on the Canada, Japan, Italy, France and the UK stock markets. ...
This paper investigates whether 2007-2008 global financial crisis hadan effecton Turkish firms. For this purpose, we investigated the firm-specific factors affecting the stock returns of firms in the BIST-XU100 in the 2004-2009 periods. The period was divided into two sub-samples, namely pre-crisisand during-crisis periods. Moreover, the effects of firm size, market-to-book ratio (MB), and price-to-earnings (PE) ratio on stock return were examined using feasible generalized least-squares (FGLS) and panel-corrected standard errors (PCSE) models. In the literature, the US subprime mortgage crisis meltdown and spillover effects were studied on different countries and different stock markets. In this study, not only crisis effects but also effects of firm-specific factors were considered. Integration levels of the series were investigated by panel unit root tests. The models were used for both sub-samples, and the results of the two models werecompared. The results showed that. Size and MB variables were significant in all the periods and had positive effects on the stock return of the firms, and the global financial crisis hada significant but weak effect on Turkish firms.Keywords.Financial Crisis, Stock Market, FGLS, PCSE.JEL.C23, G01, G20.
This study evaluates the time-varying long memory behaviors of the S&P500 volatility index using dynamic fractional difference parameters. Time-varying fractional difference parameter shows the dynamic of long memory in volatility series for the pre and post subprime mortgage crisis triggered by U.S. The results find an increasing trend in the S&P500 long memory volatility for the pre-crisis period. However, the onset of Lehman Brothers event reduces the predictability of volatility series following by a slight fluctuation of the factional differencing parameters. After that, the U.S. financial market becomes more informationally efficient and follows a non-stationary random process.
Michel Dacorogna, Ulrich Muller, Richard Olsen and Olivier Pictet offer a new definition of efficient markets that takes into account heterogeneity and varying time scales.
This paper investigates if component GARCH models introduced by Engle and Lee(1999) and Ding and Granger(1996) can capture the long-range dependence observed in measures of time-series volatility. Long-range dependence is assessed through the sample autocorrelations, two popular semiparametric estimators of the long-memory parameter, and the parametric fractionally integrated GARCH (FIGARCH) model. Monte Carlo methods are used to characterize the finite sample distributions of these statistics when data are generated from GARCH(1,1), component GARCH and FIGARCH models. For several daily financial return series we find that a two-component GARCH model captures the shape of the autocorrelation function of volatility, and is consistent with long-memory based on semiparametric and parametric estimates. Therefore, GARCH models can in some circumstances account for the long-range dependence found in financial market volatility.
In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived
from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (1962–1985)
and for all subperiods for a variety of aggregate returns indexes and size-sorted portfolios. Although the rejections are
due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or
time-varying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a mean-reverting
model of asset prices.
Daily data and component GARCH (CGARCH) models strongly support a positive risk-return relation, in contrast to previous international results. Long-run volatility appears to be important in determining the conditional equity premium, but the evidence might be spurious.
The book is a collection of essays in honour of Clive Granger. The chapters are by some of the world'leading econometricians, all of whom have collaborated with or studied with (or both) Clive Granger. Central themes of Grangers work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.
In his Nobel Laureate lecture Engle notes that asymmetric volatility has a significant impact on risk. In this article equity market volatility is estimated using an asymmetric power-GARCH model which nests many other popular models. We estimate the magnitude of asymmetric volatility for several emerging and mature markets for three sub-periods. Many mature markets exhibit large magnitudes of asymmetric volatility and several emerging markets do so as well. The magnitude of asymmetry varies by sub-period and is consistent with the suggestion in Campbell and Hentschel (1992) that asymmetry is greater when markets are more volatile.
The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1-B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown to possibly arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.
Variance-ratio methodology is used to test the hypothesis that Latin American emerging equity market prices follow a random walk. The data are monthly index prices in local currency from December 1975 to March 1991 for Argentina, Brazil, Chile, and Mexico. The variance-ratio tests reject the random walk hypothesis. However, runs tests indicate that Latin American equity markets are weak-form efficient. These empirical findings suggest that domestic investors might not be able to develop trading strategies that would allow them to earn excess returns.
Emerging markets efficiency has been widely investigated, with mixed results. However such evidence is only reliable if the methodology adopted accounts for the institutional features of the market. Unlike previous studies this paper corrects for thin trading and incorporates possible non-linear behaviour and regulatory changes. Using Istanbul Stock Exchange data we show that in its early years the exchange was characterised by non-linear behaviour and inefficient pricing. However, regulatory changes encouraged participation, improved information quality and led to prices impounding information more rapidly, suggesting markets become efficient with high trading volume, reliable information and an appropriate institutional framework.
Daily stock market volatility in a sample of emerging market economies is investigated utilizing an asymmetric stochastic volatility (ASV) model which is estimated with Markov Chain Monte Carlo (MCMC) method. The results indicate that the ASV model captures the volatility dynamics in those stock markets successfully. Particularly, it is shown that volatility has a significant persistency and the variability of volatility is higher as compared to advanced economies. The paper also provides evidence for significant negative correlation between shocks to the stock market index and shocks to volatility, the so-called 'leverage effect'. Furthermore, the estimation results show that the persistency in volatility and the variability of volatility are negatively related: higher variability of volatility implies lower persistency in volatility series and vice versa. In addition, persistency in volatility and the magnitude of leverage effect are negatively correlated: high persistency is associated with relatively lower leverage effect.
The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations. Unlike (I(d) processes for the mean, Maximum Likelihood Estimates (MLE) of the FIGARCH parameters are argued to be . The small-sample behavior of an approximate MLE procedure is assessed through a simulation study, which also documents how the estimation of a standard GARCH model tends to produce integrated, or IGARCH, like estimates. An empirical example with daily Deutschmark — U.S. dollar exchange rates illustrates the practical relevance of the new FIGARCH specification.
This paper is concerned with the assertion found in the financial literature that emerging markets are becoming more efficient over time. To verify whether this assertion is true or not, we propose the calculation of the Hurst exponent over time using a time window with 4 years of data. The data used here comprises the bulk of emerging markets for Latin America and Asia. Our empirical results show that this assertion seems to be true for most countries, but it does not hold for countries such as Brazil, The Philippines and Thailand. Moreover, in order to check whether or not these results depend on the short term memory and the volatility of returns common in such financial asset return data, we filter the data by an AR-GARCH procedure and present the Hurst exponents for this filtered data.
This paper investigates the asymmetry and long-memory volatility behavior of the Malaysian Stock Exchange daily data over a period of 1991–2005. The long-spanning data set enable us to examine piecewise before, during and after the economic crisis encountered in the Malaysian stock market. The daily index returns are adjusted for infrequent trading effect and the estimated Hurst's parameter allows us to rank the market efficiency across the periods. The leverage effect, clustering volatility and long-memory behavior of the volatility are fitted by the asymmetry GARCH models and GARCH with the inclusion of realized volatility at the final period. Across the periods, the results show the mixture of symmetry and asymmetry GARCH modeling.
This paper investigates whether there are three distinctive features in financial asset prices, that is, time-varying conditional volatility, jumps and the component factors of volatility. It adopts a component-GARCH-Jump, which can efficiently capture the three features simultaneously. Our results demonstrate that the three features exist in the Taiwan exchange rate. Besides time-varying conditional volatility, our model identifies 172 jumps between 5 January 1988 and 21 March 2003. The empirical evidence shows that the permanent component of the conditional variance is a relatively smooth movement except for a fairly sharp shift which began in 1997. This means that the effect of the Asian crisis shock might very well have exerted not only a transitory jump effect, but also a permanent effect on Taiwan’s exchange rate.
By aggregating simple, possibly dependent, dynamic micro-relationships, it is shown that the aggregate series may have univariate long-memory models and obey integrated, or infinite length transfer function relationships. A long-memory time series model is one having spectrum or order ω-2d for small frequencies ω, d>0. These models have infinite variance for but finite variance for . For d=1 the series that need to be differenced to achieve stationarity occur, but this case is not found to occur from aggregation. It is suggested that if series obeying such models occur in practice, from aggregation, then present techniques being used for analysis are not appropriate.
In developed equity markets the APARCH model of Ding, Granger and Engle [Ding, Z., Granger, C. and Engle, R., 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance 1, 83–106] has proven to be useful in modelling the leverage and asymmetry effects; power transformations and long memory; and non-normal conditional error distributions that characterise the data. Extending the analysis of Jayasuriya, Shambora and Rossiter [Jayasuriya, S., Shambora, W. and Rossiter, R., 2005. Asymmetric volatility in mature and emerging markets, Working Paper, Ohio University.] to a wider set of emerging markets this paper explores the applicability of the model to emerging markets. The key findings are as follows. First, unlike developed markets where a power term of unity and a conditional standard deviation model appears to be appropriate, emerging markets demonstrate a considerably greater range of power values. Second, unlike developed markets where non-normal conditional error distributions appear to fit the data well, there are a set of emerging markets for which estimation problems arise with a conditional t distribution, and a conditional normal distribution appears to be the preferred option. Third, the degree of volatility asymmetry appears to vary across the set of emerging markets, with the Middle Eastern and African markets having very different volatility asymmetry characteristics to those of the Latin American markets.
Most algorithms for the least-squares estimation of non-linear parameters have centered about either of two approaches. On the one hand, the model may be expanded as a Taylor series and corrections to the several parameters calculated at each iteration on the assumption of local linearity. On the other hand, various modifications of the method of steepest-descent have been used. Both methods not infrequently run aground, the Taylor series method because of divergence of the successive iterates, the steepest-descent (or gradient) methods because of agonizingly slow convergence after the first few iterations. In this paper a maximum neighborhood method is developed which, in effect, performs an optimum interpolation between the Taylor series method and the gradient method, the interpolation being based upon the maximum neighborhood in which the truncated Taylor series gives an adequate representation of the nonlinear model. The results are extended to the problem of solving a set of nonlinear algebraic e
This research investigates long memory financial equity markets using three heuristic methodologies namely a proposed modified variance time-aggregated plot, modified rescaled-range plot and periodogram approaches. The intensity of the long memory process is quantified in terms of Hurst parameter (H). Five Malaysian equity market indices are selected in the empirical studies with the inclusion of pre- and post-drastic economic events. Our empirical results evidenced dissimilar long memory behaviours in the different regimes of significant economic events. It is also found that after the short-memory adjustment, all the equity markets exhibited substantial reductions in long memory estimations.
We find that emerging markets appeared to be somewhat insulated from developments in U.S. financial markets from early 2007 to summer 2008. From that point on, however, emerging markets responded very strongly to the deteriorating situation in the U.S. financial system and real economy. Our regression "event study," focusing on 15 types of news, indicates that a range of financial and real economic news emanating from the US had statistically and economically large impacts on 14 emerging markets and several news events uniformly moved markets. Policy measures taken in emerging markets to insulate themselves from global financial developments proved inadequate in the face of the credit crunch and decline in international trade that followed the Lehman bankruptcy in September 2008.
The authors find support for a negative relation between conditional expected monthly return and conditional variance of monthly return using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, they also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility, whereas negative unanticipated returns result in an upward revision of conditional volatility. Copyright 1993 by American Finance Association.
It seems plausible that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. We develop a formal model of this volatility feedback effect using a simple model of changing variance (a quadratic generalized autoregressive conditionally heteroskedastic, or QGARCH, model). Our model is asymmetric and helps to explain the negative skewness and excess kurtosis of U.S. monthly and daily stock returns over the period 1926–1988. We find that volatility feedback normally has little effect on returns, but it can be important during periods of high volatility.
A ‘long memory’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return ¦rt¦d also has quite high autocorrelation for long lags. It is possible to characterize ¦rt¦d to be ‘long memory’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those based on absolute return can produce this property. A new general class of models is proposed which allows the power δ of the heteroskedasticity equation to be estimated from the data.
Mean reversion in stock index basis changes has been presumed to be driven by the trading activity of stock index arbitragers. The authors propose here instead that the observed negative autocorrelation in basis changes is mainly a statistical illusion, arising because many stocks in the index portfolio trade infrequently. Even without formal arbitrage, reported basis changes would appear negatively autocorrelated as lagging stocks eventually trade and get updated. The implications of this study go beyond index arbitrage, however. The authors' analysis suggests that spurious elements may creep in whenever the price-change or return series of two securities or portfolios of securities are differenced. Copyright 1994 by American Finance Association.
This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.
A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.
A permanent and transitory component model of stock return volatilityEngle R. White H.Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W
- R Engle
- G Lee
Short-sales in Global PerspectiveFabozzi F. Short-Selling: Strategies, Risks and
- A Bris
- W Goetzmann
- N Zhu
Short-Selling: Strategies, Risks and Reward New
- A Bris
- W Goetzmann
- N Zhu
- F Fabozzi