Is market liquidity less resilient after the financial crisis? Evidence for US Treasuries

Understanding market liquidity resilience, i.e. the capacity of liquidity to absorb shocks, of United States Treasuries is crucial from a financial stability standpoint. The conventional resilience measure has limitations due to the use of the liquidity level. We propose a new complementary approach to analyze resilience based on liquidity volatility. For this purpose, we focus on the link between returns volatility and liquidity volatility, which is a relatively unexplored field. We fit a bivariate conditional correlation (CC-) GARCH model for the 10-year bond returns and five liquidity indicators from January 2003 to June 2016 to analyze persistence and spillovers between these variables in a parsimonious way. We find that after the crisis, spillovers between liquidity volatility and returns volatility are higher, feedback loops are more likely and volatility persistence is lower, which is consistent with a lower resilience. Our results help to explain recent episodes of high volatility in this market.     

The linkages,persistence, asymmetry in the volatility,the price discovery and efficiency,and the effect of the US subprime mortgage financial crisis on the spot and the futures market’s returns: the case of India

This article examines the effects of persistence, asymmetry and the US subprime mortgage crisis on the volatility of the returns and also the price discovery, efficiency and the linkages and causality between the spot and futures volatility by using various classes of the ARCH and GARCH models, and through the Granger’s causality. We have used two indices: one for spot and the other for futures, for the daily data from 12 June 2000 to 30 September 2013 from Nifty stock indices. We have then tested for ARCH effects, and subsequently employed various models of the ARCH and GARCH conditional volatility. The GARCH(1,1) model is found to be significant, and it implies that the returns are not autocorrelated and have ‘short memory’. It supports the hypothesis of the efficiency of the markets. The negative ‘news’ has more significant effect on volatility, corroborating the ‘leverage impact’ in finance on market volatility. We have also tested the volatility spillover effects. The two methods we employed support the spillover effects and the causality is bidirectional. We also have used the dummy variable for the US subprime mortgage financial crisis and found that they are statistically significant. Indian stock market is thus integrated to the world stock markets.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

The persistence and asymmetric volatility in the Nigerian stock bull and bear markets

This paper considers the persistence and asymmetric volatility at each market phase of the Nigerian All Share Index (ASI). The estimate of the fractional difference parameter is used as a stability measure of the degree of persistence in the level of the series and in the absolute/squared returns, which are used as proxies for the volatility. Both semi-parametric and parametric methods are applied. Forms of Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models, which include fractional integration and asymmetric variants are estimated at each market phase of the stock returns. The results show that the level of persistence differs between the two market phases in both level and squared/absolute return series. Apart from general asymmetry and persistence in Nigerian stocks, each market phase still presents significant persistence and asymmetry.     

Improving GARCH volatility forecasts with regime-switching GARCH

Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime. The resulting Markov regime-switching GARCH model improves on existing variants, for instance by making multi-period-ahead volatility forecasting a convenient recursive procedure. The empirical analysis demonstrates that the model resolves the problem with the high single-regime GARCH forecasts and that it yields significantly better out-of-sample volatility forecasts. First Version Received: November 2000/Final Version Received: August 2001     

On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

股指期货对A股波动性的影响

鉴于GARCH模型适合研究金融时间序列的方差随时间变化的情况,笔者采用该模型研究股指期货的推出能否减少股票市场的波动性。本文选取股指期货推出前后一年的沪深300指数的日收盘价作为原始数据,通过建立GARCH模型就股指期货对股票市场波动性的影响进行了实证研究,结果显示,股指期货的引入在一定程度上降低了我国股票现货市场的波动性,但不显著。     

Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model

Long memory is an important feature of the volatility of financial returns. We document that the recently developed Realized GARCH model (Hansen et al., 2012) is insufficient for capturing the long memory of underlying volatility. We develop a parsimonious variant of the Realized GARCH model by introducing the HAR specification of Corsi (2009) into the volatility dynamics. A comparison of the theoretical and sample autocorrelation functions shows that the new model specification better captures the long memory dynamics of volatility. We calculate the multi-period out-of-sample volatility forecasts for several return series and find that the new model is a significant improvement over the classic Realized GARCH model.     

Hedging and diversification across commodity assets

ABSTRACT

We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.     

TRADING VOLUME,VOLATILITY, AND GARCH EFFECTS IN THE SOUTH KOREAN WON/US DOLLAR EXCHANGE MARKET: EVIDENCE FROM CONDITIONAL QUANTILE ESTIMATION*

Under the MDH, this paper investigates the asymmetry in the positive relationship between unexpected volume and volatility, and whether the unexpected volume series as a proxy for the rate of information arrival absorbs the GARCH effects. This is achieved by applying a quantile regression approach to the won/dollar exchange market with reliable data on trading volumes. Interestingly, the results show that in a freely floating exchange rate system, the positive relationship increases as exchange rate returns are higher. Contrary to previous studies, despite a significantly positive relationship, the inclusion of volumes alone does not reduce volatility persistence at medium or high levels of returns. In addition, the reform of the South Korean exchange rate system had an impact on the relationship, which occurred in response to a financial crisis.     

Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility

This paper extends the GARCH model to a wide class of nonstationary processes by proposing a semiparametric GARCH model for simultaneous modelling of conditional heteroskedasticity, slow scale change and periodicity in the volatility of high-frequency financial returns. A data-driven algorithm is developed for estimating the model. An approximate significance test of daily periodicity and the use of Monte Carlo confidence bounds for the scale function are proposed. The practical performance of the proposal is investigated in detail using some German stock price returns. It is shown that the various volatility components are all significant. Asymptotic properties of the proposed estimators are investigated.     

Dynamic hedging strategy in incomplete market: Evidence from Shanghai fuel oil futures market

This paper introduces a new incomplete index and establishes a new optimal hedging model. We find that when the market micro-noise is perfectly negatively correlated with the return of futures market, market incompleteness depends on the relative level of noise volatility. Especially when noise volatility is less than the futures market yield, noise volatility will be offset by return volatility. As a result, complete optimal hedging model emerges. As an aside, it is interesting to note that as different conditional variances derived from different volatility models being applied, the hedge performance tends to be basically consistent with subtle difference: DCC–GARCH model is more likely to execute the hedging with 1:1 ratio, while other multivariate GARCH models would give a hedging ratio with greater probability less than 1:1 and is less likely to be a perfect hedge. Therefore, we believe that a simpler econometric model might produce better empirical results.     

亚洲金融危机后亚洲各国汇市的波动特征改变了吗

亚洲金融危机首先爆发在各国的汇市上。危机过后多年,亚洲汇市的波动与危机前的状况有实质性改变吗?本文运用GARCH模型比较了亚洲各国及地区(韩国、泰国、台湾地区、新加坡、日本、印度、马来西亚和中国)汇市波动变化并进行了排序。实证结果表明,亚洲危机后各国汇市波动的方差扩大,冲击的影响在汇市上持续时间也有所延长。其原因是亚洲各国汇市的联动增加了。为了确保在第三国市场的份额,各国都在频繁调整和干预本国汇市。     

On the origin of high persistence in GARCH-models

We show that the (Baillie and Chung, 2001) minimum distance estimates of the GARCH (1,1) model induce spurious persistence in the volatility when there are structural changes in the mean of the process.     

Asymmetry, realised volatility and stock return risk estimates

In this paper we estimate minimum capital risk requirements for short and long positions with three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes. We also address the problem of the extremely high estimated persistence of the GARCH model to generate observed volatility patterns by including realised volatility as an explanatory variable into the model??s variance equation. The results suggest that the inclusion of realised volatility improves the GARCH forecastability as well as its ability to calculate accurate minimum capital risk requirements and makes it quite competitive when compared with asymmetric conditional heteroscedastic models such as the GJR and the EGARCH.     

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

金融危机后中国股市波动性结构变化测度及其原因分析

以2005年4月至2010年4月我国沪深300指数为研究对象,使用调整后的EGARCH模型,对金融危机前后中国股市的波动性进行研究。结果显示:金融危机发生后中国股市的波动性明显减弱———这与美国股市明显不同,且波动性结构发生了显著性变化,表现为美国股市对中国股市的影响减弱、中国股市波动的持久性增强等。最后,对产生这些变化的原因进行了理论分析,指出金融危机发生后贸易保护主义抬头、刺激性的宏观经济政策出台是中国股市波动性结构变化的可能原因。     

Responses of international stock markets to oil price surges: a regime-switching perspective

We propose an enhanced regime-switching model to investigate the relationships between oil price surges and stock market cycles in five oil-dependent countries. Our model accounts for the joint effects of the West Texas Intermediate (WTI) and Brent oil markets and simultaneously captures asymmetry, volatility persistence and regime shifts contained in the underlying financial data. We find that stock market returns strongly exhibit a regime-switching behaviour, but they react differently to the increases in the price of oil. More precisely, the conditional volatility of studied stock markets during the bear market phases is found to be less affected by oil price surges than during the bull market phases. Whether the effects of oil shocks are positive or negative depends greatly on the degree of reliance on imported oil, the share of the cost of oil in the national income and the degree of improvement in energy efficiency of a given country. Finally, the relatively opposite effects of the WTI and Brent oil markets suggest the potential of substitution between them as well as the necessity of a diversification strategy of oil supply sources.     

CROSS-COUNTRY EVIDENCE ON OUTPUT GROWTH VOLATILITY: NONSTATIONARY VARIANCE AND GARCH MODELS

This paper revisits the issue of conditional volatility in real gross domestic product (GDP) growth rates for Canada, Germany, Italy, Japan, the United Kingdom, and the United States. Previous studies find high persistence in the volatility. This paper shows that this finding largely reflects a nonstationary variance. Output growth in the six countries became noticeably less volatile over the past few decades. In this paper, we employ the modified iterated cumulative sum of squares (ICSS) algorithm to detect structural change in the variance of output growth. One structural break exists in each of the six countries after identifying outliers and mean shifts in the growth rates. We then use generalized autoregressive conditional heteroskedasticity (GARCH) specifications, modeling output growth and its volatility with and without the break in volatility. The evidence shows that the time-varying variance falls sharply in Canada and Japan, and disappears entirely in Germany, Italy, the United Kingdom and the United States, once we incorporate the break in the variance equation of output for the six countries. That is, the integrated GARCH (IGARCH) effect proves spurious and the GARCH model demonstrates misspecification, if researchers neglect a nonstationary variance. Moreover, we also consider the possible effects of our more correct measure of output volatility on output growth as well as the reverse effect of output growth on its volatility. The conditional standard deviation possesses no statistical significance in all countries, except a significant negative effect in Japan. The lagged growth rate of output produces significant negative and positive effects on the conditional variances in Germany and Japan, respectively. No significant effects exist in Canada, Italy, the United Kingdom, and the United States.     

Structural breaks and GARCH models of stock return volatility: The case of South Africa

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests applied to daily returns of the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH(1,1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. However, based on the out-of-sample forecasting exercise, we find that even though there structural breaks in the volatility, there are no statistical gains from using competing models that explicitly accounts for structural breaks, relative to a GARCH(1,1) model with expanding window. This could be because of the fact that the two identified structural breaks occurred in our out-of-sample, and recursive estimation of the GARCH(1,1) model is perhaps sufficient to account for the effect of the breaks on the parameter estimates. Finally, we highlight that, given the point of the breaks, perhaps what seems more important in South Africa, is accounting for leverage effects, especially in terms of long-horizon forecasting of stock return volatility.