Financial market volatility

The volatility of financial markets has attracted a lot of attention in recent years. However, while particular episodes, such as the bond market turbulence in 1994 and considerable exchange rate movements in 1995, may give the impression that markets have become more volatile, there appears to have been no systematic increase in volatility over the last 20 years in major industrialised countries.     

Forecasting volatility: Roles of sampling frequency and forecasting horizon

This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time‐series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high‐frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non‐trading‐time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time‐series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

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.     

The new market for volatility trading

This study analyses the new market for trading volatility; VIX futures. We first use market data to establish the relationship between VIX futures prices and the index itself. We observe that VIX futures and VIX are highly correlated; the term structure of average VIX futures prices is upward sloping, whereas the term structure of VIX futures volatility is downward sloping. To establish a theoretical relationship between VIX futures and VIX, we model the instantaneous variance using a simple square root mean‐reverting process with a stochastic long‐term mean level. Using daily calibrated long‐term mean and VIX, the model gives good predictions of VIX futures prices under normal market situation. These parameter estimates could be used to price VIX options. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 30:809–833, 2010     

Liquidity supply and volatility: futures market evidence

This article examines the provision of liquidity in futures markets as price volatility changes. We find that customer trading costs do not increase with volatility. However, for three of the four contracts studied, the nature of liquidity supply changes with volatility. Specifically, for relatively inactive contracts, customers as a group trade more with each other and less with market makers, on higher volatility days. By contrast, for the most active contract, trading between customers and market makers increases with volatility. We also find that market makers' income per contract decreases with volatility for one of the least active contracts in our sample, but is not significantly affected by volatility for the other contracts. These results are consistent with the idea that, for high‐cost, inactive contracts, market makers react to temporary increases in volatility by raising their bid‐ask spreads significantly, and customers provide increased liquidity through standing limit orders. An implication of our results is that electronic systems, where market maker participation is not required, are able to supply adequate liquidity during volatile periods. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:1–17, 2001     

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。     

Outlook for the grain market

With all the indications suggesting that the current imbalance between supply of and demand for grain will continue for the foreseeable future, and with a steady surplus both keeping prices down and forcing on governments measures to reduce output and stocks, attention is turning to South East Asia, a region which, due to the rapid growth of both its population and per capita income, seems to offer the best prospects of medium and long-term increases in demand. How realistic are the hopes that South East Asia will be able to absorb a substantial part of the world's grain surplus?     

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     

Two Chinese dairy producers enter world＇stop 20

Two Chinese dairy producers, Mengniu and Yili, entered the world＇s top-20 dairy ranking last year, according to an annual report released on June 17.     

中国粮食需求蕴藏巨大商机

中国的粮食产量白1998年创下历史纪录以来一直呈下滑态势，这使得交易员们对向这个全世界人口最多的国家出售谷物的前景充满信心。随着中国为满足国内需求增加了谷物进口，世界市场的谷物价格过去12个月里大幅跳升。但就像中国经济的许多领域一样，这个中国梦一一从庞大的食品需求中赢得巨额利润的前景能否成为现实仍远未可知。     

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     

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.