Asymmetric linkages among the fear index and emerging market volatility indices

This study explores the relationships between changes in the fear index (VIX) and changes in emerging market volatilities i.e., Chinese, Brazilian and the overall emerging volatility index, across their conditional distributions by employing a mixed Quantile regression - Copula methodological approach. Moreover, we analyze whether emerging market volatility indices would respond asymmetrically to positive and negative volatility shocks in the fear index i.e., whether the relationships are asymmetric between the VIX and the emerging market volatilities. Our results confirm that there are strong positive relationships between changes in the VIX and emerging market volatilities, and the linkages tend to be stronger for the upper-parts of the conditional distributions, namely above the median-quantiles up to the extreme-quantiles. In all cases, the nature of the relationship appears to be contemporaneous and on average is three times stronger than their lagged relationship. Further test results reveal that the relationship is highly asymmetric i.e., the effect of a positive shock in the VIX is on average about twice more pronounced than the effect of a negative shock at the extreme-tails of their conditional distributions, a stylized fact that cannot be revealed via conventional estimation methods as OLS. If we compare the effects of positive and negative VIX shocks on emerging market volatilities utilizing QRM, Copulas and OLS, our findings reveal that the effect of a positive shock by the QRM at the 95% quantile is about eight times higher than the one revealed by OLS. An exhaustive robustness analysis is also performed with respect to other volatility measures.     

Sovereign credit ratings, emerging market risk and financial market volatility

This article presents event studies that find a significant effect on dollar bond yield spreads when rating agencies put emerging-market sovereign bonds on review with negative outlook. The finding has two conditional implications. If rating agencies can be turned from late into early warning signals, they would have the potential to dampen boom-bust cycles in emerging-market flows. If rating agencies cannot improve on their reactive approach witnessed in the run-up and aftermath of recent currency crises, regulation and guidelines stipulating a certain rating status for institutional investment will continue to intensify boom-bust cycles. The paper concludes with regulatory suggestions for both outcomes.     

Can exchange seat prices predict financial market volatility?

There is considerable evidence that trading volume and volatility are positively related and that exchange seat prices are largely a function of trading volume. This article examines whether changes in seat prices at the Chicago Board of Trade (where stock index and interest rate futures account for the vast majority of trading volume) are useful in predicting changes in interest rate and stock market volatility. Exponential GARCH and transfer function models are used to demonstrate the power of changes in CBOT seat prices in predicting changes in interest rate and stock market volatility. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1206–1221, 2008     

International financial integration: Stock return linkages and volatility transmission between Vietnam and advanced countries

This paper investigates the interdependence between the Vietnamese stock market and other influential equity markets in terms of return linkage and volatility transmission covering the period including pre, during and post the 2008 Global Financial Crisis. A VAR model is utilized to estimate the conditional return linkage among these indices and a GARCH-BEKK model is employed to investigate the volatility transmission. We find evidence of statistically significant correlation, return spillover and volatility linkage between Vietnamese stock market with other leading equity markets of the US, Hong Kong and Japan. Moreover, we find that during the financial crisis, stock markets become more interrelated.     

The roles of news and volatility in stock market correlations during the global financial crisis

The global financial crisis has again brought the interdependencies of international financial markets to the fore, particularly during times of financial crises. This paper explores the relative roles of news and volatility in explaining the changes in correlations between national stock markets during the global financial crisis. Our results show that the majority of the correlations are more strongly explained by volatility rather than news. However as the global financial crisis evolves the relative role of news grows in importance.     

On time-varying predictability of emerging stock market returns

The two recent studies of Cajueiro and Tabak (2004b) and Hull and McGroarty (2014) investigate the predictability of emerging stock market returns based on the Hurst coefficient—a simple but powerful measure of long-range dependence. Unfortunately, the insights gained in these studies are limited because they (i) present conflicting evidence on the time-varying nature of the estimated Hurst coefficients and (ii) incorrectly equate random walk behaviour with market efficiency. In this note, we revisit the issue of time-varying predictability for a rich sample of 21 emerging markets in the 27-year period from 1988 to 2015. Extending the two aforementioned studies by various alternative fractal estimators of the Hurst coefficient, trend regressions and several robustness checks, our analysis reveals significant downward trends in the local Hurst coefficients of almost all markets. Specifically, we document vanishing predictability over time, which indicates that profitable emerging market investment strategies based on past returns may not continue their good performance in the future. Furthermore, we explicitly point out why a random walk is neither a necessary nor a sufficient condition for rationally determined security prices, and thus signs of predictability (randomness) should not be interpreted as evidence for market inefficiency (efficiency).     

Testing mean reversion in financial market volatility: Evidence from S&P 500 index futures

This article presents a comprehensive study of continuous time GARCH (generalized autoregressive conditional heteroskedastic) modeling with the thintailed normal and the fat‐tailed Student's‐t and generalized error distributions (GED). The study measures the degree of mean reversion in financial market volatility based on the relationship between discrete‐time GARCH and continuoustime diffusion models. The convergence results based on the aforementioned distribution functions are shown to have similar implications for testing mean reversion in stochastic volatility. Alternative models are compared in terms of their ability to capture mean‐reverting behavior of futures market volatility. The empirical evidence obtained from the S&P 500 index futures indicates that the conditional variance, log‐variance, and standard deviation of futures returns are pulled back to some long‐run average level over time. The study also compares the performance of alternative GARCH models with normal, Student's‐ t, and GED density in terms of their power to predict one‐day‐ahead realized volatility of index futures returns and provides some implications for pricing futures options. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1–33, 2008     

股票市场波动性研究述评

创业板市场的波动性研究,对于完善我国证券市场机制、危机风险管理有着重要的意义。本文从研究对象、杠杆效应、长记忆性、模型拟合的角度描述股票市场的一些国内研究成果,即GARCH模型的发展和应用。     

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

This study investigated the volatility linkages between energy and agricultural futures, including possible causes for these comovements, such as external macroeconomic and financial shocks during low and high volatility regimes. A combination of Markov-switching regressions and quadrivariate VAR–DCC–GARCH and VAR–BEKK–GARCH modeling revealed that external shocks have an asymmetric effect on the relationship of these assets with higher cross-correlations reported during high volatility regimes. This comovement effect outweighs the substitution effect between energy and agricultural products. Furthermore, the quadrivariate VAR–BEKK–GARCH model provides strong evidence of a bidirectional price volatility spillover between the agricultural and energy markets during periods of high volatility. Overall, the results suggest that energy futures can be effectively used for hedging in a portfolio comprising agricultural futures (and vice versa), while a combination of macroeconomic and financial index futures can serve as an effective hedging tool in investment portfolios comprising both energy and agricultural commodities.     

论国际金融市场一体化与发展中国家金融风险防范

一、国际金融市场一体化的形成及影响 (一)国际金融市场一体化的形成 20世纪50年代末60年代初以来,随着国际经济的发展、金融技术的进步,尤其是80年代以来世界各国金融自由化(利率自由化、汇率自由化、银行业务自由化、金融市场自由化、资本流动自由化等)措施也直接推动了国际金融市场一体化的进程。各国国内和国外金融市场之间的日益紧密的联系、协调,它们相互影响、相互促进,逐步走向一个统一的金融市场的状态和趋势。     

Modeling the time-varying volatility of the paper–bill spread

The spread between the rates on commercial paper and Treasury bills has received considerable attention in the literature for its role as an indicator of real economic activity. In this paper we empirically examine what happens when the volatility of the spread changes over time. We estimate a nonlinear model that enables us to discern the asymmetric impact of negative and positive shocks to the spread. We find that a positive shock has a larger impact on the volatility of the spread than does a negative shock.     

完善金融市场的自由秩序观

秩序和自由是人类的两个基本需求,二者是对立统一的。制度是形成自由秩序的机制。金融市场的完善就是金融自由和金融秩序的博弈发展过程,是形成金融自由秩序的过程。我国在完善金融市场过程中,面临先发挥自由还是先考虑秩序的两难问题。应该是二者同时发展,但是在不同的时期,根据矛盾主要方面的改变应有所调整。     

Are the dynamic linkages between the macroeconomy and asset prices time-varying?

We estimate a number of multivariate regime switching VAR models on a long monthly US data set for eight variables that include excess stock and bond returns, the real T-bill yield, predictors used in the finance literature (default spread and the dividend yield), and three macroeconomic variables (inflation, industrial production growth, and a measure of real money growth). Heteroskedasticity may be accounted for by making the covariance matrix a function of the regime. We find evidence of four regimes and of time-varying covariances. We show that the best in-sample fit is provided by a four state model in which the VAR(1) component fails to be regime-dependent. We interpret this as evidence that the dynamic linkages between financial markets and the macroeconomy have been stable over time. The four-state model can be helpful in forecasting applications and provides one-step ahead predicted Sharpe ratios.     

Canonical valuation of options in the presence of stochastic volatility

Proposed by M. Stutzer (1996), canonical valuation is a new method for valuing derivative securities under the risk‐neutral framework. It is nonparametric, simple to apply, and, unlike many alternative approaches, does not require any option data. Although canonical valuation has great potential, its applicability in realistic scenarios has not yet been widely tested. This article documents the ability of canonical valuation to price derivatives in a number of settings. In a constant‐volatility world, canonical estimates of option prices struggle to match a Black‐Scholes estimate based on historical volatility. However, in a more realistic stochastic‐volatility setting, canonical valuation outperforms the Black‐Scholes model. As the volatility generating process becomes further removed from the constant‐volatility world, the relative performance edge of canonical valuation is more evident. In general, the results are encouraging that canonical valuation is a useful technique for valuing derivatives. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:1–19, 2005     

Detecting and modeling changing volatility in the copper futures market

Copper futures returns are characterized by negative skewness and excess kurtosis. Research has not yet examined this nonnormality, which contributes to their volatility. To date little attention has been paid to the modeling of these series. Therefore, the purpose of this paper is to (i) detect alternating subperiods of volatility by using a method that uses an iterated cumulative sum of squares (ICSS) algorithm to identify breakpoints in the series; and (ii) compare the ability of five models (the random walk, GARCH, EGARCH, AGARCH, and the GJR model) to capture the volatility within each ICSS identified subperiod. These tests were applied to two copper futures series (open to close and close to close prices). Results indicate that the ranking (in terms of the root mean square error) is similar for both series. That is, the GARCH or EGARCH model rank first and second, depending on the series, followed by the GJR model. AGARCH and the random walk models perform poorly.© 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 79–100, 1999     

Forecasting bitcoin volatility: Evidence from the options market

This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressive–Moving-Average or Heterogeneous Auto-Regressive model forecasts in predicting short-term BTC volatility (1 day ahead), but superior in predicting long-term volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and model-based forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.