Price volatility,international market links,and their implications for regulatory policies

Conclusions The October 1987 stock market crash spawned an abundance of research papers, as scholars attempted to explain what seemed at the time, and to some extent remains, an inexplicable event.Except for the period immediately around the crash, there is only meager evidence that international linkages across markets have become tighter over time. Yet the crash was worldwide in scope, and its similarity across countries was uncanny. Just on the face, this international similarity puts doubt to such explanations as particular macroeconomic events in one country, failure of a given country's market system, or simultaneous changes in underlying fundamentals (which were quite different across countries).Assigning the origination of the crash to one country cannot be entirely ruled out, however, because of the possibility of a non-fully revealing equilibrium contagion process of the type suggested by King and Wadhwani (1988). Such a process would allow a world-wide crash to begin by a particular news event or even by a market mistake in one country. Evidence in favor of this process is that international correlations of returns increased dramatically during the crash period. However, this increase is consistent with other explanations, such as transaction costs hindering international arbitrage except during periods of high volatility.Was the crash the bursting of a bubble? Some evidence seems to support this proposition: for example, in the majority of countries, the pre-crash period displayed significant serial dependence in stock returns, dependence that was definitely not present in the post-crash period. However, further work is necessary to ascertain whether this measured serial dependence is unusual relative to what one would have expected to find, even in a perfectly random process, by choosing a sample period that happened to culminate in a random peak. Ross (1987) shows that such ex post sample period selection will induce upward bias in estimates of serial dependence. Cross-country tests failed to detect this bias, but there are several ambiguities in the tests that will have to be resolved in future work.The crash is history. What implications, if any, does it have for regulatory policy? Is there evidence that popular regulations or rules would have mitigated the crash, or that they would decrease price volatility in general? There is very little evidence in favor of the efficacity of margin requirements, price limits, or transactions taxes. Despite a large number of empirical studies, no one has provided evidence that margin requirements have an impact on volatility. There has been at least one recent paper claiming the contrary, but a careful examination of its methods have uncovered enough problems to cast those results into doubt.As for price limits, there must be a very short-term impact on measured volatility, for the measured market price at a trading halt is likely to understate the direction of movement. Yet even for daily data, the cross-country evidence is slim that price limits reduce volatility, and there is no evidence at all that they work over periods as long as a week. In other words, trading halts caused by limits seem to have no effect on true volatility.Transaction taxes are inversely but insignificantly correlated with volatility across countries, and the effect is too questionable for taxes to be used with confidence as an effective policy instrument.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Information and volatility links in the foreign exchange market

We apply the trading model of Fleming et al (1998 ). to a number of currency markets. The model posits that two markets can have common volatility structures as a result of receiving common information and from cross‐hedging activity where a position in one currency is used to hedge risk in a position taken in another. Our results imply that the model is effective in identifying common information flows and volatility spillovers in the currency markets and that some of these effects are lost when simply examining raw correlations. A series of specification tests of the 21 bivariate systems that are examined provides support for the trading model in the foreign exchange context.     

The determination and international transmission of stock market volatility

This paper extends the literature on low-frequency analysis of the causes and transmission of stock market volatility. It uses end-monthly data on stock market returns, interest rates, exchange rates, inflation, and industrial production for five countries (Britain, France, Germany, Japan, and the US) from July 1973 to December 1994. Efficient portfolios of world, European, and Japanese/US equity are first constructed, the existence of multivariate cointegrating relationships between them is demonstrated, and the transmission of conditional volatility between them is described. The transmission of conditional volatility from world equity markets and national business cycle variables to national stock markets is then modeled. Among the main findings are: first, world equity market volatility is caused mostly by volatility in Japanese/US markets and transmitted to European markets, and second, changes in the volatility of inflation are associated with changes of the opposite sign in stock market volatility in all markets where a significant effect is found to exist. To the extent that the volatility of inflation is positively related to its level, this implies that low inflation tends to be associated with high stock market volatility.     

Discount rate changes,stock market returns,volatility, and trading volume: Evidence from intraday data and implications for market efficiency

We examine the effect of discount rate changes on stock market returns, volatility, and trading volume using intraday data. Equity returns generally respond negatively and significantly to the unexpected announcements; however, the effect of expected changes on equity returns is insignificant. Furthermore, our results indicate that equity prices respond to announcements within the trading period/hour after the information release. An indication of a return reversal is too small to cover the full transaction costs. Unexpected discount rate changes also contribute to higher market volatility although the volatility is short-lived. Similarly, unexpected changes in discount rates induce larger trading volume while expected changes do not. Abnormal trading volume occurs only in period t. Our results also support the notion that unexpected changes in the discount rates impact market returns irrespective of the Federal Reserve operating procedures.     

Price limit bands,asymmetric volatility and stock market anomalies: Evidence from emerging markets

I investigate the effects of imposing different bands of price limits on stock returns and volatility in the Egyptian (EGX), Thai (SET) and Korean (KRX) stock exchanges. In addition, the paper examines whether the switch from narrow price limits (NPL) to wider price limits (WPL) structurally alters volatility and the day of the week anomaly. Using the extended EGARCH and PARCH asymmetric volatility models, I found that the switch from NPL to WPL structurally altered both asymmetric volatility and the day of the week anomaly in the EGX, SET and KRX. I argue that the price discovery mechanism is disrupted due to the switch as closing prices do not fully reflect all information arrived in the market when prices hit the limits and that is reflected on volatility and market efficiency.     

Explaining international stock correlations with CPI fluctuations and market volatility

This paper investigates the dynamic correlations among six international stock market indices and their relationship to inflation fluctuation and market volatility. The current research uses a newly developed time series model, the Double Smooth Transition Conditional Correlation with Conditional Auto Regressive Range (DSTCC-CARR) model. Findings reveal that international stock correlations are significantly time-varying and the evolution among them is related to cyclical fluctuations of inflation rates and stock volatility. The higher/lower correlations emerge between countries when both countries experience a contractionary/expansionary phase or higher/lower volatilities.     

Stock market volatility: Identifying major drivers and the nature of their impact

Financial-market risk, commonly measured in terms of asset-return volatility, plays a fundamental role in investment decisions, risk management and regulation. In this paper, we investigate a new modeling strategy that helps to better understand the forces that drive market risk. We use componentwise gradient boosting techniques to identify financial and macroeconomic factors influencing volatility and to assess the specific nature of their influence. Componentwise boosting is capable of producing parsimonious models from a, possibly, large number of predictors and—in contrast to other related techniques—allows a straightforward interpretation of the parameter estimates.Considering a wide range of potential risk drivers, we apply boosting to derive monthly volatility predictions for the equity market represented by S&P 500 index. Comparisons with commonly-used GARCH and EGARCH benchmark models show that our approach substantially improves out-of-sample volatility forecasts for short- and longer-run horizons. The results indicate that risk drivers affect future volatility in a nonlinear fashion.     

Information,market power,and price volatility

We consider demand function competition with a finite number of agents and private information. We show that any degree of market power can arise in the unique equilibrium under an information structure that is arbitrarily close to complete information. Regardless of the number of agents and the correlation of payoff shocks, market power may be arbitrarily close to zero (the competitive outcome) or arbitrarily large (so there is no trade). By contrast, price volatility is always lower than the variance of the aggregate shock across all information structures. Alternative trading mechanisms lead to very distinct bounds as a comparison with Cournot competition establishes.     

Automating the price discovery process: Some international comparisons and regulatory implications

Automated trade execution systems are examined with respect to the degree to which they automate the price discovery process. Seven levels of automation of price discovery are identified, and 47 systems are classified according to these criteria. Systems operating at various levels of automation are compared with respect to age, geographical location, and type of securities traded. Information provided to market participants and asymmetries of information between traders with direct access to the automated market and outside investors also are examined. It is found, for example, that the degree of asymmetric information increases with the level of automation of price discovery. The potential for trading abuses related to prearranged trading, noncompetitive execution, and trading ahead of customers is analyzed for each level of automation. Certain levels of automation widen the opportunities for trading abuses in some respects, but may narrow them in others.     

Stock market cycles, financial liberalization and volatility

In this paper we analyze the cycles of the stock markets in four Latin American and two Asian countries, and we compare their characteristics. We divide our sample in two subperiods in order to account for differences induced by the financial liberalization processes of the early 1990s. We find that cycles in emerging countries tend to have shorter duration and larger amplitude and volatility than in developed countries. However, after financial liberalization Latin American stock markets have behaved more similarly to stock markets in developed countries whereas Asian countries have become more dissimilar. Concordance of cycles across markets has increased significantly over time, especially for Latin American countries after liberalization.     

FX volatility forecasts and the informational content of market data for volatility

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of ‘pure’ time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from ‘backing out’ volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the ‘mixed’ models incorporating market data for currency volatility perform best most of the time.     

Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market

This study examines the Chinese implied volatility index (iVIX) to determine whether jump information from the index is useful for volatility forecasting of the Shanghai Stock Exchange 50ETF. Specifically, we consider the jump sizes and intensities of the 50ETF and iVIX as well as cojumps. The findings show that both the jump size and intensity of the 50ETF can improve the forecasting accuracy of the 50ETF volatility. Moreover, we find that the jump size and intensity of the iVIX provide no significant predictive ability in any forecasting horizon. The cojump intensity of the 50ETF and iVIX is a powerful predictor for volatility forecasting of the 50ETF in all forecasting horizons, and the cojump size is helpful for forecasting in short forecasting horizon. In addition, for a one-day forecasting horizon, the iVIX jump size in the cojump is more predictive of future volatility than that of the 50ETF when simultaneous jumps occur. Our empirical results are robust and consistent. This work provides new insights into predicting asset volatility with greater accuracy.     

Banks recapitalization policies in Japan and their impact on the market

The following measures to boost banks’ capital were implemented from December 1997 through March 1998 in Japan: (1) Banks were given the option to choose the cost method in the valuation of their equity portfolios. (2) Reevaluation of bank property assets at current market values was introduced. (3) Public funds were injected into the banking system. This article examines whether or not the announcements of these measures have had any impact on stock prices. We find: (1) The announcement of the introduction of the cost method in the valuation of equity portfolios had a limited effect on stock prices. (2) The announcement of reevaluation of property assets had a relatively strong impact on stock prices. (3) The aim of capital injection by the government might be interpreted by investors in general as an attempt to favor weak banks rather than to bolster the capital base of strong banks.     

Modeling asset market volatility in a small market:: Accounting for non-synchronous trading effects

The use of GARCH modeling in empirical finance has so far to a great extent been restricted to larger asset markets. This paper considers whether the GARCH framework can be used on a smaller, less liquid market. In particular, selected stocks on the Vancouver Stock Exchange, a smaller market in Canada, are examined. Modeling return volatility in the standard GARCH framework and returns as autoregressive fails to remove significant serial correlation in the mean. The results indicate that once the parameters are adjusted for non-synchronous trading effects, GARCH can also be successful in modeling stochastic volatility on smaller markets. Persistence in both the mean and variance are eliminated with these adjustments. In addition, for some stocks, volumes add explanatory power for explaining return volatility.     

Trading volume,time-varying conditional volatility,and asymmetric volatility spillover in the Saudi stock market

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.     

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2010年7月21日,美国总统奥巴马签署了《多德-弗兰克华尔街改革和消费者保护法案》,该法案正式生效。文章对该法案的内容进行了解析,详细介绍了该法案在促进金融稳定、重塑银行业监管格局、加强对大型银行的监管、强化金融消费者权益保护、完善银行业及金融市场基础设施监管等核心内容方面的立法宗旨和具体措施,并进一步阐述了该法案对完善我国金融监管的重要借鉴意义。     

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