THE EFFECTS OF THE ANNOUNCEMENTS OF DIVIDEND INCREASES ON STOCK RETURN VOLATILITY: THE EVIDENCE FROM THE OPTIONS MARKET

This paper examines the impact of the announcements of dividend increases on the volatility of underlying stock returns implied by option prices, and analyses whether the impact is related to the label associated with the dividend increase. The results suggest that the announcements of labelled dividend increases are accompanied by a decrease in implied volatility, while the announcements of unlabelled increases in dividends are associated with no change in implied volatility. These results are consistent with the hypothesis that signal implicit in the announcements of dividend increases provides noisy information about the firm's 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.     

Oil prices implied volatility or direction: Which matters more to financial markets?

We examine the impact of oil price uncertainty on US stock returns by industry using the US Oil Fund options implied volatility OVX index and a GJR-GARCH model. We test the effect of the implied volatility of oil on a wide array of domestic industries’ returns using daily data from 2007 to 2016, controlling for a variety of variables such as aggregate market returns, market volatility, exchange rates, interest rates, and inflation expectations. Our main finding is that the implied volatility of oil prices has a consistent and statistically significant negative impact on nine out of the ten industries defined in the Fama and French (J Financ Econ 43:153–193, 1997) 10-industry classification. Oil prices, on the other hand, yield mixed results, with only three industries showing a positive and significant effect, and two industries exhibiting a negative and significant effect. These findings are an indication that the volatility of oil has now surpassed oil prices themselves in terms of influence on financial markets. Furthermore, we show that both oil prices and their volatility have a positive and significant effect on corporate bond credit spreads. Overall, our results indicate that oil price uncertainty increases the risk of future cash flows for goods and services, resulting in negative stock market returns and higher corporate bond credit spreads.     

The Effect of Volatility Changes on the Level of Stock Prices and Subsequent Expected Returns

This paper estimates volatility changes in daily returns to the Dow Jones Industrial Average over the sample period 1897 through 1988. This allows a direct investigation of the reaction of the level of stock prices and subsequent expected returns to these estimated changes in volatility. We provide empirical evidence consistent with relatively large and systematic revisions in stock prices and subsequent expected returns to volatility changes. However, there appears to be an asymmetry in the market's reaction to volatility increases as opposed to volatility decreases. A majority of our volatility changes cannot be associated with the release of significant economic information.     

Forecasting value‐at‐risk in oil prices in the presence of volatility shifts

Recent evidence suggests shifts (structural breaks) in the volatility of returns causes non‐normality by significantly increasing kurtosis. In this paper, we endogenously detect significant shifts in the volatility of oil prices and incorporate this information to estimate Value‐at‐Risk (VaR) to accurately forecast large declines in oil prices. Our out‐of‐sample performance results indicate that the model, which incorporates both time varying volatility (without making any distributional assumptions) and shifts in volatility, produces more accurate VaR forecasts than several benchmark methods. We make a timely contribution as the recent more frequent occurrences of unexpected large oil price declines has gained significant attention because of its substantial impact on the financial markets and the global economy.     

Differences in beliefs and currency risk premiums

This paper studies the importance of heterogeneous beliefs for the dynamics of asset prices. We focus on currency markets, where the absence of short-selling constraints allows us to perform sharper tests of theoretical predictions. Using a unique data set with detailed information on foreign-exchange forecasts, we construct an empirical proxy for differences in beliefs. We show that this proxy has a strong effect on the implied volatility of currency options beyond the volatility of macroeconomic fundamentals. We document that differences in beliefs impact also on the shape of the implied volatility smile, on the volatility risk-premiums, and on future currency returns.     

Market imperfections and the information content of implied and realized volatility

The information content of option implied volatility and realized volatility under market imperfections are studied in the context of GARCH modeling and volatility forecasts of Taiwan stock market (TAIEX) returns. Consistent with most studies, we find that the Taiwan implied volatility index (TVIX) calculated from the TAIEX option prices contains most of the information, and that White's [White, H., 2000. A reality check for data snooping. Econometrica 68, 1097–1126] reality check test cannot reject the null hypothesis that the TVIX provides the best forecast. Possibly due to market imperfections, however, the incremental information content of realized volatility as well as daily returns cannot be ruled out. Finally, we also find that the information is found only in the most recent TVIX, indicating information is being efficiently impounded on the TAIEX option prices. This finding suggests that appropriately designed derivative products can alleviate the problems caused by market imperfections.     

Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options

We build a new class of discrete-time models that are relatively easy to estimate using returns and/or options. The distribution of returns is driven by two factors: dynamic volatility and dynamic jump intensity. Each factor has its own risk premium. The models significantly outperform standard models without jumps when estimated on S&P500 returns. We find very strong support for time-varying jump intensities. Compared to the risk premium on dynamic volatility, the risk premium on the dynamic jump intensity has a much larger impact on option prices. We confirm these findings using joint estimation on returns and large option samples.     

Modelling S&P 100 volatility: The information content of stock returns

Theoretical models that relate volatility to the quantity of information are extended to a multi-asset setting and it is deduced that stock returns may or may not have incremental information when modelling index volatility, depending on the sources of information that move stock prices. The first empirical study that can help resolve this theoretical uncertainty is presented. A detailed analysis of the daily volatility of the S&P 100 index from 1984 to 1998 shows there is some incremental volatility information in the returns from the 100 shares that define the index. This evidence is obtained from ARCH models that incorporate leverage effects, dummy variables for the 1987 crash and aggregate measures of stock return volatility. Significant differences between estimated volatilities are found for various stock measures and sub-periods.     

Monetary policy and commodity markets: Unconventional versus conventional impact and the role of economic uncertainty

This study explores the impact of both conventional and unconventional monetary policies in the US and the Euro area on the mean and volatility of certain commodity prices. The analysis considers the prices of eight commodities, i.e. oil, natural gas, gold, silver, aluminium, copper, platinum, and nickel, while the methodology employs the EGARCH-X modelling approach. The empirical findings clearly document that (i) the direction of the impact of both conventional and unconventional monetary policy on commodity returns and commodity volatility is similar and (ii) the impact from unconventional monetary policy on both commodity returns and volatility is relatively more pronounced, while these findings hold valid, irrespective of the geographical region and commodity type. Further investigation of the disparity on the size of the impact through the prism of economic uncertainty reveals that unconventional monetary policy has a stronger effect on economic uncertainty, thereby offering an indirect channel of monetary policy transmission on commodity markets.     

Transmission of Stock Returns and Volatility between the U.S. and Japan: Evidence from the Stock Index Futures Markets

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan. This revised version was published online in August 2006 with corrections to the Cover Date.     

Fundamentalists,chartists and asset pricing anomalies

This paper investigates whether excess volatility of asset prices and serial correlations of stock monthly returns may be explained by the interactions between fundamentalists and chartists. Fundamentalists forecast future prices cum dividends through an adaptive learning rule. In contrast, chartists forecast future prices based on the observation of past price movements. Numerical simulations reveal that the interplay of fundamentalists and chartists robustly generates excess volatility of asset prices, volatility clustering, trends in prices (i.e. positive serial correlations of returns) over short horizons and oscillations in prices (i.e. negative serial correlations of returns) over long horizons, often observed in financial data. Moreover, we find that the memory of the learning rule plays a key role in explaining the above-mentioned stylized facts. In particular, we establish that excess volatility of asset prices; volatility clustering and autocorrelation of returns at different horizons emerge when fundamentalists have short memory. However, volatility clustering as well as short-run and long-run dependencies, observed in financial time series, are more pronounced when fundamentalists have longer memory.     

Intraday and interday volatility in the Japanese stock market

This paper characterizes the volatility in the Japanese stock market based on a 4-year sample of 5-min Nikkei 225 returns from 1994 through 1997. The intradaily volatility exhibits a doubly U-shaped pattern associated with the opening and closing of the separate morning and afternoon trading sessions on the Tokyo Stock Exchange. This feature is consistent with market microstructure theories that emphasize the role of private and asymmetric information in the price formation process. Meanwhile, readily identifiable Japanese macroeconomic news announcements explain little of the day-to-day variation in the volatility, confirming previous findings for US equity markets. Furthermore, by appropriately filtering out the strong intradaily periodic pattern, the high-frequency returns reveal the existence of important long-memory interdaily volatility dependencies. This supports recent results stressing the importance of exploiting high-frequency intraday asset prices in the study of long-run volatility properties of asset returns.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.     

Stock return variances: The arrival of information and the reaction of traders

Asset prices are much more volatile during exchange trading hours than during non-trading hours. This paper considers three explanations for this phenomenon: (1) volatility is caused by public information which is more likely to arrive during normal business hours; (2) volatility is caused by private information which affects prices when informed investors trade; and (3) volatility is caused by pricing errors that occur during trading. Although a significant fraction of the daily variance is caused by mispricing, the behavior of returns around exchange holidays suggests that private information is the principle factor behind high trading-time variances.     

The Timing of Opening Trades and Pricing Errors

After demonstrating that a zero investment trading strategy that buys stocks with overnight returns below the market average and sells stocks with overnight returns above the market average earns more than 1% monthly profit, I demonstrate that this profit is greater for stocks that start trading more quickly than for other stocks. These results control for trading costs. The resulting pricing errors are a material portion of stock price volatility and suggest that a quick response to overnight information adds non‐information‐based stock volatility to stock prices.     

Feedback Effects and Asset Prices

Feedback effects from asset prices to firm cash flows have been empirically documented. This finding raises a question for asset pricing: How are asset prices determined if price affects fundamental value, which in turn affects price? In this environment, by buying assets that others are buying, investors ensure high future cash flows for the firm and subsequent high returns for themselves. Hence, investors have an incentive to coordinate, which may generate self‐fulfilling beliefs and multiple equilibria. Using insights from global games, we pin down investors' beliefs, analyze equilibrium prices, and show that strong feedback leads to higher excess volatility.     

The effect of monetary policy shocks on stock prices accounting for endogeneity and omitted variable biases

A new high frequency data set is used to estimate the impact of the Fed on the level and volatility of stock prices while accounting for endogeneity and omitted variable biases and potential asymmetries. Results show that after addressing these issues, the effect of policy shocks on the level and volatility of stock returns is higher than previously reported. GARCH findings indicate that the volatility impact is tent-shaped, spiking during policy announcements and declining before and after the release. The level and conditional volatility of stock returns is found to respond asymmetrically to the type of policy shocks (timing versus future path of monetary policy) and the type of policy action (easing versus tightening).     

The Media and the Diffusion of Information in Financial Markets: Evidence from Newspaper Strikes

The media are increasingly recognized as key players in financial markets. I investigate their causal impact on trading and price formation by examining national newspaper strikes in several countries. Trading volume falls 12% on strike days. The dispersion of stock returns and their intraday volatility are reduced by 7%, while aggregate returns are unaffected. Moreover, analysis of return predictability indicates that newspapers propagate news from the previous day. These findings demonstrate that the media contribute to the efficiency of the stock market by improving the dissemination of information among investors and its incorporation into stock prices.     

Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates

This paper specifies a multivariate stochasticvolatility (SV) model for the S & P500 index and spot interest rateprocesses. We first estimate the multivariate SV model via theefficient method of moments (EMM) technique based on observations ofunderlying state variables, and then investigate the respective effects of stochastic interest rates, stochastic volatility, and asymmetric S & P500 index returns on option prices. We compute option prices using both reprojected underlying historical volatilities and the implied risk premiumof stochastic volatility to gauge each model's performance through direct comparison with observed market option prices on the index. Our major empirical findings are summarized as follows. First, while allowing for stochastic volatility can reduce the pricing errors and allowing for asymmetric volatility or leverage effect does help to explain the skewness of the volatility smile, allowing for stochastic interest rates has minimal impact on option prices in our case. Second, similar to Melino and Turnbull (1990), our empirical findings strongly suggest the existence of a non-zero risk premium for stochastic volatility of asset returns. Based on the implied volatility risk premium, the SV models can largely reduce the option pricing errors, suggesting the importance of incorporating the information from the options market in pricing options. Finally, both the model diagnostics and option pricing errors in our study suggest that the Gaussian SV model is not sufficientin modeling short-term kurtosis of asset returns, an SV model withfatter-tailed noise or jump component may have better explanatory power.