VOLATILITY SPILLOVERS BETWEEN THE BLACK MARKET AND OFFICIAL MARKET FOR FOREIGN CURRENCY IN GREECE

The issue of volatility spillovers between the black and official exchange markets for U.S. dollars in Greece for 1975–89 is examined. A vector error correction‐bivariate EGARCH model is developed and estimated to capture potential asymmetric effects of innovations and volatility. During the period under investigation, reciprocal spillovers are found between the black and official exchange markets for dollars. Furthermore, spillovers are asymmetric in that bad news in one market has a greater effect on the volatility of the other market than good news. Additionally, the size of spillover effects is greater from the official market to the black market. Finally, the removal of the foreign exchange controls in January 1986 made the volatility of the official exchange rate higher and changed the nature of volatility spillovers between the two markets. JEL Classification: F31, F32     

中国股票市场波动性研究：模型选择及实证

以日收盘数据计算出的市场日收益率作为研究的基础数据,利用准极大似然估计方法QML估计三种ARCH类模型(GARCH、T-GARCH和E-GARCH)对中国股市波动性与稳定性的运行效果进行了实证研究的结果,得出EGARCH(1,1)模型是最优的拟合模型。运用最优模型实证发现中国股市不仅波动性很大,而且波动是不对称的。     

The impact of news on the volatility of ESG firms

This study provides one of the first empirical investigations of asymmetric volatility for environmental, social, and governance (ESG) investing. Using the Morgan Stanley Capital International (MSCI) indices as proxies for ESG test assets, this study investigates volatility risk for the highest ESG-rated firms through an empirical analysis in assessing how good news and bad news impact the risk of ESG firms. The analysis provides empirical evidence in support of the hypothesis that the impact of news on the volatility of ESG firms is larger for bad news, compared to good news. Employing an EGARCH framework, the analysis also finds that, in response to bad news, the observed volatility increases for small size ESG firms is lower compared to large and mid-cap ESG firms. The findings provide evidence of a slow response by small size firms to news in an ESG context. In modeling the conditional volatility of the ESG test assets, the analysis also provides evidence of higher persistence in the conditional volatility dynamics for small size ESG firms.     

Asymmetric Effects of Interest Rate Changes on Stock Prices

This study examines the stock price adjustment process around announcements of changes in the federal funds rate target in the 1990s using an asymmetric autoregressive exponential GARCH model (ASAR‐EGARCH). We find that target change announcements convey new information to the stock market. Risk aversion increases before the announcement of a rate change, and especially before the announcement of a joint target and discount rate change. The volatility estimates suggest that such joint rate changes send a clearer signal to the stock market about monetary policy objectives relative to unilateral target changes. Our findings are consistent with overreaction in the wake of bad news (rate hikes), and point to a shift in volatility from before to after the rate change announcement since the adoption of the immediate disclosure policy of the Federal Open Market Committee in February 1994.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH

Abstract

The impact of short run price trending on the conditional volatility is tested empirically. A new family of conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described. Modern microeconomic theory often suggests the connection between the past behaviour of time series, the subsequent reaction of market individuals, and thereon changes in the future characteristics of the time series. Results reveal important properties of these models, which are consistent with stylized facts found in financial data sets. They can also be employed for model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the leverage effect in the conditional variance, in fitting the news impact curve and in fitting the volatility estimates from high frequency data. In addition, we show that the leverage effect is dependent on the current trend, i.e. it differentiates between bullish and bearish markets. Furthermore, trend effects can account for a significant part of the long memory property of asset price volatilities.     

Do negative and positive equity returns share the same volatility dynamics?

This paper investigates whether positive and negative returns share the same dynamic volatility process. The well established stylized facts on volatility persistence and asymmetric effects are re-examined in light of such dichotomy. To analyze the dynamics of down and up volatilities estimated from daily returns I use a bivariate generalization of the standard EGARCH model. As a robustness check, I also investigate various specifications of down and up realized measures estimated from high-frequency data. The empirical findings point to the existence of a marked diversity in the volatilities of positive and negative daily returns in terms of persistence and sensitivity to good and bad news. A simple forecasting exercise highlights the striking performance of the proposed approach even during the crisis period.     

The Informational Role of Option Trading Volume in Equity Index Options Markets

This paper examines the dynamic relations between future price volatility of the S&P 500 index and trading volume of S&P 500 options to explore the informational role of option volume in predicting the price volatility. The future volatility of the index is approximated alternatively by implied volatility and by EGARCH volatility. Using a simultaneous equation model to capture the volume-volatility relations, the paper finds that strong contemporaneous feedbacks exist between the future price volatility and the trading volume of call and put options. Previous option volumes have a strong predictive ability with respect to the future price volatility. Similarly, lagged changes in volatility have a significant predictive power for option volume. Although the volume-volatility relations for individual volatility and volume terms are somewhat different under the two volatility measures, the results on the predictive ability of volume (volatility) for volatility (volume) are broadly similar between the implied and EGARCH volatilities. These findings support the hypothesis that both the information- and hedge-related trading explain most of the trading volume of equity index options.     

Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates

The present paper examines the out-of-sample forecasting performance of four conditional volatility models applied to the European Monetary System (EMS) exchange rates. In order to provide improved volatility forecasts, the four models’ forecasts are combined through simple averaging, an ordinary least squares model, and an artificial neural network. The results support the EGARCH specification especially after the foreign exchange crisis of August 1993. The superiority of the EGARCH model is consistent with the nature of the EMS as a managed float regime. The ANN model performed better during the August 1993 crisis especially in terms of root mean absolute prediction error.     

A cyclical model of exchange rate volatility

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to 1 year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the range-based EGARCH and FIEGARCH models of Brandt and Jones (2006). Not only does the cyclical volatility model provide a very substantial computational advantage over the EGARCH and FIEGARCH models, but it also offers an improvement in out-of-sample forecast performance.     

The impact of option introduction on the conditional return distribution of underlying securities

This paper combines several interesting econometric techniques to examine changes in the conditional return distribution of security returns following option introduction. An EGARCH model is used to characterize the return generating process. An intervention analysis is performed to determine whether the parameters of the EGARCH model shift following initial options listing. This paper finds that the conditional distribution of security returns is unaffected by option introduction. Estimation of a transfer function-noise model also shows that option introduction has no effect on conditional volatility.     

Whence GARCH? A Preference-Based Explanation for Conditional Volatility

We develop a preference-based equilibrium asset pricing modelthat explains low-frequency conditional volatility. Similarto Barberis, Huang, and Santos (2001), agents in our model careabout wealth changes, experience loss aversion, and keep a mentalscorecard that affects their level of risk aversion. A new featureof our model is that when perturbed by unexpected returns, investorsbecome temporarily more sensitive to news. Gradually investorsbecome accustomed to the new level of wealth, restoring priorlevels of risk aversion and news sensitivity. The state-dependentsensitivity to news creates the type of volatility clusteringfound in low-frequency stock returns. We find empirical supportfor our model's predictions that relate the scorecard to conditionalvolatility and skewness.     

中国利率与股市间波动溢出效应的实证研究

采用多变量EGARCH模型分别对中国利率与沪深股市间的波动溢出效应进行的实证研究表明,股票收益率对利率收益率有着显著的短期动态影响;利率与沪深股市间存在着显著的双向波动溢出,除了利率对深圳市场的方向外,其他方向的波动溢出均存在着不对称性.     

Information transmission between the Gulf equity markets of Saudi Arabia and Bahrain

This paper examines whether the emerging Gulf markets of Saudi Arabia and Bahrain in conjunction with the US market exhibit cointegrating relationship. Additionally, the transmission of information and volatility spillover between the Gulf markets is explored using a bivariate EGARCH model. We find that although the markets are not cointegrated, the Gulf markets do share information flows. Specifically, we observe an asymmetric spillover of volatility from the smaller though more liberal and accessible Bahraini market to the larger and less accessible Saudi market. The observed difference in information processing may partly be due to a well-developed Bahraini financial sector that encourages wider participation by international investors who play a significant role in assimilating new information.     

Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

This paper proposes that the dynamics of bond volatility may be understood by studying textual news sentiments. In this new approach, a modified framework is used to understand the atypical characteristics of bond market news. The paper proceeds in two steps. First, a word list of sentiment terms is generated using three sentiment word lists to determine negative and positive news sentiment scores. Second, four measures of volatility are estimated and combined with a nonlinear technique adapted from information theory to understand the correlation and direction of causality between sentiment scores and measures of volatility. This paper shows that sentiments extracted from textual news published in the newspapers can explain bond returns volatility or the quicksilver. The empirical results support that news sentiment is highly correlated with the measures of volatility and that information flows unidirectionally from news to volatility. This study, perhaps the earliest work in text mining to examine the run of causality between news signals and bond return volatility, adapts a nonlinear technique from information theory to describe the nonlinear behavior of Indian debt markets and understand the volatility dynamics of the benchmark bond.     

Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation

The paper is concerned with time series modelling of foreign exchange rate of an important emerging economy, viz., India, with due consideration to possible sources of misspecification of the conditional mean like serial correlation, parameter instability, omitted time series variables and nonlinear dependences. Since structural change is pervasive in economic time series relationships, the paper first studies this aspect of the exchange rate series in detail and finds the existence of four structural breaks. Accordingly, the entire sample period is divided into five sub-periods of stable parameters each, and then the appropriate mean specification for each of these sub-periods is determined by incorporating functions of recursive residuals. Thereafter, the GARCH and EGARCH models are considered to capture the volatility contained in the data. The estimated models thus obtained suggest that return on Indian exchange rate series is marked by instabilities and that the appropriate volatility model is EGARCH. Further, out-of-sample forecasting performance of the model has been studied by standard forecasting criteria, and then compared with that of an AR model only to find that the findings are quite favorable for the former.      

Volatility Clustering, Asymmetry and Hysteresis in Stock Returns: International Evidence

Encompassing a very broad family of ARCH-GARCH models, we show that the AT-GARCH (1,1) model, where volatility rises more in response to bad newsthan to good news, and where news are considered bad only below a certain level, is a remarkably robust representation of worldwide stock market returns. The residual structure is then captured by extending ATGARCH (1,1) to an hysteresis model, HGARCH, where we modelstructured memory effects from past innovations. Obviously, this feature relates to the psychology of the markets and the way traders process information. For the French stock market we show that votalitity is affected differently, depending on the recent past being characterized by returns all above or below a certain level. In the same way a longer term trend may also influence volatility. It is found that bad news are discounted very quickly in volatility, this effect being reinforced when it comes after a negative trend in the stock index. On the opposite, good news have a very small impact on volatility except when they are clustered over a few days, which in this case reduces volatility.     

A Note on the Term Structure of Implied Volatilities for the Yen/U.S. Dollar Currency Option

This paper tests the relationship between short dated and long dated implied volatilities obtained from Tokyo market currency option prices by employing three different volatility models: a mean reverting model, a GARCH model, and an EGARCH model. We document evidence that long dated average expected volatility is higher than that predicted by the term structure relationship during the dramatic appreciation of yen/dollar exchange in the early 1990's. This revised version was published online in August 2006 with corrections to the Cover Date.     

News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.     

上证指数收益率波动的实证分析：基于ARCH族模型

股价指数的收益率序列具有时变波动性、厚尾特征、波动性群集等特点,传统的计量分析无法刻画这些特点。文章利用ARCH族模型,选取2003年1月20日～2013年12月12日上证指数每日收益率共2621个数据对其波动进行定量与定性的分析,结果显示,上证指数日收益率存在高阶的ARCH效应,杠杆效应,波动集聚性特征,条件方差对日收益率有很强的影响,其中EGARCH模型在反映股市波动性方面优于其他模型。