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.     

The Joint Cross Section of Stocks and Options

Stocks with large increases in call (put) implied volatilities over the previous month tend to have high (low) future returns. Sorting stocks ranked into decile portfolios by past call implied volatilities produces spreads in average returns of approximately 1% per month, and the return differences persist up to six months. The cross section of stock returns also predicts option implied volatilities, with stocks with high past returns tending to have call and put option contracts that exhibit increases in implied volatility over the next month, but with decreasing realized volatility. These predictability patterns are consistent with rational models of informed trading.     

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.     

On the dynamic dependence and investment performance of crude oil and clean energy stocks

This paper examines the directional spillover between crude oil prices and stock prices of technology and clean energy companies. The study uses the daily data over the period from May 2005 to April 2015. The estimated results exhibit following empirical regularities. First, it appears that technology stocks play vital role in the return and volatility spillovers of renewable energy stocks and crude oil prices. Second, technology (PSE) and clean energy indices (ECO) are the dominant emitters of return and volatility spillovers to the crude oil (WTI) prices. Third, the time and event-dependent movements are well captured by the directional spillover approach. Fourth, the application of directional spillover method seems to be more advantageous than MGARCH models as it not only establishes the inter-variables return and volatility spillovers but also helps in identifying direction of spillover through calculation of pairwise net spillovers. Last, the dynamic hedging results suggest that clean energy index can provide a profitable hedging opportunity in combination with crude oil futures than technology index. Many new findings further discussed and analysed.     

State transformation of information spillover in asset markets and effective dynamic hedging strategies

This paper aims to develop a strategy to effectively and dynamically hedge risk by considering regime transitions of spillover effects between assets. We take six assets (stocks, bonds, real estate, currency exchange, crude oil, and gold) that are commonly used to construct investment portfolios as examples and analyze asset price data between September 2002 and January 2022. In doing so, we aim to examine the information spillover of different asset prices in both bear and bull market environments to determine whether state transformation affects dynamic hedging effectiveness. Using Markov-Switching Factor-Augmented Vector Autoregression (MS-FAVAR), we construct a regimen-switching model using variables from finance and economic conditions as endogenous variables to define the state transformations of the information spillover. Empirical results reveal that the MS-FAVAR model highlights changes in information spillover during a financial crisis/economic recession. Using dynamic-weighted hedging portfolios constructed with different indicators, we find that hedging effectiveness and volatility vary depending on the state of information spillover between different asset markets and that bear markets significantly impacted hedging effectiveness. Results also show that the panic sentiment (the fear index) explains the probability of a bear market. It is suggested that the state transformation of information spillovers should be monitored periodically, and hedging portfolios should be dynamically adjusted (bear or bull market) with shifting fear sentiment.     

International capital asset pricing: Evidence from options

We study the risk dynamics and pricing in international economies through a joint analysis of the time-series returns and option prices on three equity indexes underlying three economies: the S&P 500 Index of the United States, the FTSE 100 Index of the United Kingdom, and the Nikkei-225 Stock Average of Japan. We develop an international capital asset pricing model, under which the return on each equity index is decomposed into two orthogonal jump-diffusion components: a global component and a country-specific component. We apply separate stochastic time changes to the two components so that stochastic volatility can come from both global and country-specific risks. For each economy, we assign separate market prices for the two return risk components and the two volatility risk components. Under this specification, we obtain tractable option pricing solutions. Model estimation reveals several interesting insights. First, global and country-specific return and volatility risks show different dynamics. Global return movements contain a larger discontinuous component, and global return volatility is more persistent than the country-specific counterparts. Second, investors charge positive prices for global return risk and negative prices for volatility risk, suggesting that investors are willing to pay positive premiums to hedge against downside global return movements and upside volatility movements. Third, the three economies contain different risk profiles and also price risks differently. Japan contains the largest idiosyncratic risk component and smallest global risk component. Investors in the Japanese market also price more heavily against future volatility increases than against future market downfalls.     

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.     

The effect of growth opportunities on the market reaction to dividend cuts: evidence from the 2008 financial crisis

We consider returns from rebalanced and buy and hold portfolios consisting of the same stocks. Theoretical properties are derived using Jensen’s inequality and the Hölder’s Defect Formula. Simulations are used to confirm theory and to investigate ambiguous cases where theory is silent. Rebalancing decreases total return volatility, while buy and hold produces greater expected return. Results are more opaque with respect to Sharpe Ratios and expected geometric means. Our empirical tests are based on portfolios composed of the risk-free asset, CRSP market value returns and returns from five Fama–French industries. While rebalancing reduces volatility and momentum effect, our tests largely favor the buy and hold strategy due to the high relative returns enjoyed by stocks vis-a-vis the risk-free asset. Transactions cost for rebalancing the portfolio are economically negligible.     

A Time-Series Model of Stock Returns with a Positive Short-Term Correlation and a Negative Long-Term Correlation

We study portfolio stock return behavior that exhibits both a positive autocorrelation over short horizons and a negative autocorrelation over long horizons. These autocorrelations are more significant in small size portfolios. Among various forms of temporary components in stock prices, an AR(2) component is the simplest model compatible with this pattern of returns, which yields an ARMA(2,2) model of stock returns. We show that the significance of this model is that it requires the presence of feedback trading, which is a form of irrational trades, and the market's slow adjustment to the market fundamentals, which is consistent with recent modelings of stock prices. We find that the variation of the temporary component becomes greater as the firm size gets smaller. This implies that the deviation from the market fundamentals is larger in small size portfolios than in large size portfolios.     

Idiosyncratic volatility and the pricing of poorly-diversified portfolios

This article examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk–return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.     

Trading Behavior and the Unbiasedness of the Market Reaction to Dividend Announcements

This article examines the price formation process during dividend announcement day, using daily closing prices and transactions data. We find that the unconditional positive excess returns, first documented by Kalay and Loewenstein (1985) , are higher for small-firm and low-priced stocks. Price volatility and trading volume also increase during this period. Examination of trade prices relative to the bid-ask spread and volume of trades at bid and asked prices shows that the excess returns cannot be attributed to measurement errors or to spillover effects of tax-related ex-day trading. Rather, the price behavior is related to the absorption of dividend information.     

Microstructure and reverse stock splits

In this paper we show that, similar to NYSE/AMEX stocks, NASDAQ stocks exhibit significant ex date returns for reverse stock splits. Although the 10-day cumulative return after the ex date is close to –10%, this does not violate market efficiency, because the average bid-ask spread for the reverse split stock is at least double this return. We also document that these large negative returns are mostly due to a drop in the ask price while bid prices barely change at all. Furthermore, the ex date returns are negatively related to trading volume.These results suggest that there is abnormal selling and a significant buildup of market makers' inventories near the ex date. To reduce the inventory buildup, market makers lower ask prices to induce buying by investors, resulting in the observed negative returns. Lowering bid prices, an alternative strategy for reducing inventories, is not attractive to market makers due to competitive factors and the reduction of commissions associated with a smaller number of transactions. Notably, selling investors have no incentives to sell their stocks early to avoid the observed negative ex date return, since this return is largely an ask price phenomenon and does not represent realized returns to sellers.     

On the relationship between concentration of prospect theory/mental accounting investors,cointegration, and momentum

We examine the role of cointegration between stock prices and their estimated fundamental values in return momentum. We find that the positive relationship between capital gains overhang and future stock returns in Grinblatt and Han (2005) is significantly stronger among the “non-cointegrated” group of stocks as compared with the “cointegrated” group of stocks. Further, for the cointegrated stocks, the slower the speed of adjustment to the cointegrating equilibrium, the greater (smaller) is the future return of stocks with unrealized capital gains (losses). These findings are robust to various firm characteristics including firm size, book-to-market ratio, past returns, idiosyncratic volatility, dispersion in analysts’ earnings forecasts, turnover, individual investor ownership, and industry returns.     

Does Order Flow Commonality Extend Across Trade Sizes and Securities?

We examine commonality in order imbalances across different types of securities and find that the extent of commonality is greater than previously documented. Order imbalances in portfolios of small stocks, large stocks, and closed-end funds have explanatory power for other portfolio returns even in the presence of own order flow. Our analysis of order flow composition reveals commonality in small and medium trades, but not in large trades, across portfolios. The activity from small-size trades is systemic, but not generally associated with returns on other securities. Order imbalances from larger size trades provide more information relevant to stock prices.     

Noise and the Trading Mechanism: the Case of SETS

On 20 October 1997 the London Stock Exchange introduced a new trading system called SETS. This system was to replace the dealer system SEAQ, which had been in operation since 1986. Using the iterative sum of squares test introduced by Inclan and Tiao (1994) , we investigate whether there was a change in the unconditional variance of opening and closing returns, at the time SETS was introduced. We show that for the FTSE‐100 stocks traded on SETS, on the days following its introduction, there was a widespread increase in the volatility of both opening and closing returns. However, no synchronous volatility changes were found to be associated with the FTSE‐100 index or FTSE‐250 stocks. We conclude therefore that the introduction of the SETS trading mechanism caused an increase in noise at the time the system was introduced.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

Does an intertemporal tradeoff between risk and return explain mean reversion in stock prices?

When volatility feedback is taken into account, there is strong evidence of a positive tradeoff between stock market volatility and expected returns on a market portfolio. In this paper, we ask whether this intertemporal tradeoff between risk and return is responsible for the reported evidence of mean reversion in stock prices. There are two relevant findings. First, price movements not related to the effects of Markov-switching market volatility are largely unpredictable over long horizons. Second, time-varying parameter estimates of the long-horizon predictability of stock returns reject any systematic mean reversion in favour of behaviour implicit in the historical timing of the tradeoff between risk and return.     

An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets

Understanding jump risk is important in risk management and option pricing. This study examines the characteristics of jump risk and the volatility forecasting power of the jump component in a panel of high-frequency intraday stock returns and four index returns from Shanghai Stock Exchange. Across portfolio indexes, jump returns on average account for 45% to 64% of total returns when jumps occur. Market systematic jump risk is an important pricing factor for daily returns. The average jump beta is 62% of the average continuous beta for individual stocks. However, the contribution of jump risk to total risk is limited, indicating that statistically significant jumps in the stochastic process of asset price are rare events but have tremendous impacts on the prices of common stocks in China. We further document that accounting for jump components improves the performance of volatility forecasting for some equity and bond portfolios in China, which is confirmed by in-the-sample and out-of-sample forecasting performance analysis.     

The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

The Relative Importance of Stock, Bond and Real Estate Factors in Explaining REIT Returns

This paper examines the link between REIT, financial asset and real estate returns, and tests whether it changed subsequent to the “REIT boom” of the early 1990s. The main focus is on answering the question do REIT returns now better reflect the performance of underlying direct (unsecuritized) real estate? We develop and implement a variance decomposition for REIT returns that separates REIT return variability into components directly related to major stock, bond, and real estate-related return indices, as well as idiosyncratic or sector-specific effects. This is applied to aggregate REIT sector (NAREIT) returns as well as returns to size and property-type based REIT portfolios. Our results show that the REIT market went from being driven largely by the same economic factors that drive large cap stocks through the 1970s and 1980s to being more strongly related to both small cap stock and real estate-related factors in the 1990s. There is also a steady increase over time in the proportion of volatility not accounted for by stock, bond or real estate related factors. We also find that small cap REITs are “more like real estate” compared to larger cap REITs, at least over the 1993–1998 period. We argue that this could be a result of the institutionalization of the ownership of larger cap REITs that took place in the 1990s.