Day trading and stock price volatility

When an investor buys and sells the same stock on the same day, he is said to have made a day trade. Using the trading records of Finnish traders, this paper examines whether day trading is related to volatility of stock prices. I find a strong positive time-series relation between the number of day trades by individual investors and intraday volatility among heavily day traded stocks. This effect is robust after controlling for a previously documented volume–volatility relation. The result suggests that the joint hypothesis of price pressure and volatility induced day trading dominates the liquidity effects of day trading.     

Forecasting multivariate realized stock market volatility

We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of lagged volatility, lagged returns and other forecasting variables. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it results in a positive-definite estimated covariance matrix. We apply the model to the covariance matrix of size-sorted stock returns and find that two factors are sufficient to capture most of the dynamics.     

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

Music and the market: Song and stock volatility

Popular music may presage market conditions because people contemplating complex future economic behavior prefer simpler music, and vice versa. In comparing the annual average beat variance of the songs in the U.S. Billboard Top 100 since its inception in 1958 through 2007 to the standard deviation of returns of the S&P 500 for the same or the subsequent year, a significant negative correlation is observed. Furthermore, the beat variance appears able to predict future market volatility, producing 2.5 volatility points of profit per year on average.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

The day of the week effect on stock market volatility

This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings shown that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are observed on Friday and Wednesday, respectively. Further investigation of sub-periods reinforces our findings that the volatility pattern across the days of the week is statistically different.(JEL G10, G12, C22)     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Risk and return in the Tehran stock exchange

This paper analyzes market index returns in the Tehran stock exchange (TSE) within the context of three variants of the Capital Asset Pricing Model: the static international; the constant-parameter intertemporal; and a Markov-switching intertemporal CAPM, which allows for time-varying degree of integration with regional and international equity markets. We find that TSE returns are CAPM-efficient at monthly frequency with respect to several international market indices. Moreover, we find evidence in support of international integration of the TSE with respect to international markets. In addition, we conduct an extensive investigation for the direction of causality between TSE returns, international market index returns, and those in neighboring countries.     

论证券交易所的公司化改革

在全球证券交易所竞争日趋激烈,证券市场结构变革加速发展的背景下,我国的证券交易所也面临着体制转轨和迎接竞争的双重挑战。本文从公司制与会员制的比较入手,深入探讨交易所公司化的动因,并阐述了与公司化改革的相关问题。     

A semiparametric GARCH model for foreign exchange volatility

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily returns data, the semiparametric volatility model outperforms the GJR model as well as the more commonly used GARCH(1,1) model in terms of goodness-of-fit, and forecasting, by correcting overgrowth in volatility.     

Asymmetric loss functions and the rationality of expected stock returns

We combine the innovative approaches of Elliott, Komunjer, and Timmermann (2005) and Patton and Timmermann (2007) with a block bootstrap to analyze whether asymmetric loss functions can rationalize the S&P 500 return expectations of individual forecasters from the Livingston Surveys. Although the rationality of these forecasts has often been rejected, earlier studies have relied on the assumption that positive and negative forecast errors of identical magnitudes are equally important to forecasters. Allowing for homogenous asymmetric loss, our evidence still strongly rejects forecast rationality. However, if we allow for variation in asymmetric loss functions across forecasters, not only do we find significant differences in preferences, but also we can often no longer reject forecast rationality. Our conclusions raise serious doubts about the homogeneous expectations assumption often made in asset pricing, portfolio construction and corporate finance models.     

人民币汇率波动对NFDI波动的影响及其检验

本文通过对我国NFDI资本流动性及其波动性的计算,检验了人民币/美元汇率波动对NFDI波动性的关系。通过实证分析表明,人民币汇率的波动将导致NFDI资本流动的明显不稳定。在理论研究的基础上,引申出其政策含义：在国际资本流动不断加大的今天,我们必须维护人民币/美元汇率的稳定,这样对于有效引导NFDI的资本流动和维护金融稳定都具有重要的意义。     

Testing the forward volatility unbiasedness hypothesis in exchange rates under long-range dependence

This paper analyses the unbiasedness hypothesis between spot and forward volatility, using both the actual and the continuous path of realised volatility, and focusing on long-memory properties. For this purpose, we use daily realised volatility with jumps for the USD/EUR exchange rate negotiated in the FX market and employ fractional integration and cointegration techniques. Both series have long-range dependence, and so does the error correction term of their long-run relationship. Hence, deviations from equilibrium are highly persistent, and the effects of shocks affecting the long-run relationship dissipate very slowly. While for long-term contracts, there is some empirical evidence that the forward volatility unbiasedness hypothesis does not hold – and, thus, that forward implied volatility is a systematically downward-biased predictor of future spot volatility – for short-term contracts, the evidence is mixed.     

Shock and volatility spillovers among equity sectors of the Gulf Arab stock markets

The major objectives of this study are twofold. The first objective is to examine the dynamic volatility and volatility transmission in a multivariate setting using the VAR(1)–GARCH(1,1) model for three major sectors, namely, Service, Banking and Industrial/or Insurance, in four Gulf Cooperation Council (GCC)’s economies (Kuwait, Qatar, Saudi Arabia and UAE). The second is to use the models’ results to compute and analyze the optimal weights and hedge ratios for two-sector portfolio holdings, comprised of the three sectors for each country. The results suggest that past own volatilities matter more than past shocks and there are moderate volatility spillovers between the sectors within the individual countries, with the exception of Qatar. Moreover, the values for ratios of hedging long positions with short positions in the GCC sectors are smaller than those for the US equity sectors. The optimal portfolio weights favor the Banking/financial sector for Qatar, Saudi Arabia and UAE and the Industrial sector for Kuwait.     

International foreign exchange agreements and nominal exchange rate volatility: a GARCH application

For eight major national currencies, this study estimates, and tests several hypotheses with, a t-distribution GARCH model of daily spot nominal exchange rate changes. The sample period covered is June 1, 1982 through September 30, 1992. By using likelihood ratio and parameter stability tests, it finds that for most of the currencies considered, both the conditional means and variances of unexpected exchange rate changes experienced statistically significant structural breaks across the five subperiods that are associated with four episodes of international foreign-exchange policy coordination. The study also finds that the orderings of the GARCH-estimated unconditional standard deviations roughly match the orderings of the sample standard deviations across the five subperiods. An explanation is provided for what underlying factors contributed to these structural shifts.     

Return distributions and the day-of-the-week effects in the stock exchange of Thailand

The Presence of the day-of-the week effect has been documented in equity markets throughout the world. Most of the studies reporting this pervasive pattern have relied on the OLS methodology. More recently, using more robust methodology, some inconclusive results have been report regarding the said anomaly. This study examines the day-of-the-week effect in the Securities Exchange of Thailand using OLS as well as GARCH models. We examine the aggregate stock index, SET, as well as its ten industry-classified indices over a 15-year period starting in 1980. Our investigation finds persisting day-of-the-week effects irrespective of the methodology employed. The findings are in direct contrast with earlier suggestions that the day-of-the-week anomalies are exaggerated by rraditional treatments to the data.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

International stock exchange listing and the reduction of political risk

The impending 1997 assimilation into the People's Republic of China made the 1980s a challenging period for Hong Kong, one in which Kong Kong-based companies relocated their corporate domiciles to other countries as political insurance. International listing, however, is another way for companies to reduce political risk. This paper examines the post-1985 effects of Hong Kong-based company stocks being quoted on London's International Stock Exchange. We document that there is no price effect for these companies after the first trading day and a significant increase in the average trading volume surrounding the event day. This may be attributed to the London market makers who build up their stakes before the listing and who unwind their positions in the Hong Kong market after the event day. We also find that there is, on average, a decrease in systematic risk of these companies after being traded on the London market. Furthermore, the evidence shows that there is an increase in systematic risk for companies that are not listed in London after 4 June 1989, however, not for those London-listed Hong Kong companies. The results constitute evidence to support the inference that there is a reduction of local political risk for internationally listed companies.     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.