Co-dependence of extreme events in high frequency FX returns

In this paper, we investigate extreme events in high frequency, multivariate FX returns within a purposely built framework. We generalize univariate tests and concepts to multidimensional settings and employ these novel techniques for parametric and nonparametric analysis. In particular, we investigate and quantify the co-dependence of cross-sectional and intertemporal extreme events. We find evidence of the cubic law of extreme returns, their increasing and asymmetric dependence and of the scaling property of extreme risk in joint symmetric tails.     

The high returns to low volatility stocks are actually a premium on high quality firms

Recent empirical research shows that low volatility stocks outperform high volatility stocks around the world. This study documents that the volatility effect is associated with the quality of the firm using a large sample of international stocks. First, adding a quality factor to the Fama–French model contributes to the explanation of the volatility effect. Furthermore, the negative volatility–return relation is shown to be stronger and significant only among high quality firms which are profitable and have stable cash flows. Second, a fundamental investment strategy that goes long high quality firms and short low quality firms performs like a volatility strategy and cannot be explained by common asset pricing models. However, a low–high volatility factor adds to the explanation of the return difference between high and low quality stocks as volatility and quality strategies have a common component.     

Specification tests of asset pricing models using excess returns

In this paper, we discuss the impact of different formulations of asset pricing models on the outcome of specification tests that are performed using excess returns. We point out that the popular way of specifying the stochastic discount factor (SDF) as a linear function of the factors is problematic because (1) the specification test statistic is not invariant to an affine transformation of the factors, and (2) the SDFs of competing models can have very different means. In contrast, an alternative specification that defines the SDF as a linear function of the de-meaned factors is free from these two problems and is more appropriate for model comparison. In addition, we suggest that a modification of the traditional Hansen–Jagannathan distance (HJ-distance) is needed when we use the de-meaned factors. The modified HJ-distance uses the inverse of the covariance matrix (instead of the second moment matrix) of excess returns as the weighting matrix to aggregate pricing errors. Asymptotic distributions of the modified HJ-distance and of the traditional HJ-distance based on the de-meaned SDF under correctly specified and misspecified models are provided. Finally, we propose a simple methodology for computing the standard errors of the estimated SDF parameters that are robust to model misspecification. We show that failure to take model misspecification into account is likely to understate the standard errors of the estimates of the SDF parameters and lead us to erroneously conclude that certain factors are priced.     

Equilibrium moment restrictions on asset returns: normal and crisis periods

Empirically, the covariance between stock returns varies with their volatility. We seek a robust theoretical explanation of this. With minimal assumptions, we model stochastic properties of equilibrium returns which result from the interaction between inter-temporal traders and noisy, price-sensitive short-term traders. The inter-temporal traders can have arbitrary investment rules, preferences and information. In all cases we find a set of restrictions between second moments of equilibrium returns. With two assets there is also a bound on the correlation between asset returns. Estimation with second moments of global stock returns supports our theoretical framework. Higher volatility in at least one market can increase comovement among markets. With globalization, covariances between two stock markets can also affect covariances between two other stock markets. We also find that the changes in trader behavior between normal and crisis periods lead to changes in the moment restrictions between asset returns.     

Using daily stock returns: The case of event studies

This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies. Daily data generally present few difficulties for event studies. Standard procedures are typically well-specified even when special daily data characteristics are ignored. However, recognition of autocorrelation in daily excess returns and changes in their variance conditional on an event can sometimes be advantageous. In addition, tests ignoring cross-sectional dependence can be well-specified and have higher power than tests which account for potential dependence.     

Asymmetric and leptokurtic distribution for heteroscedastic asset returns: The SU-normal distribution

This paper proposes the S_U-normal distribution to describe non-normality features embedded in financial time series, such as: asymmetry and fat tails. Applying the S_U-normal distribution to the estimation of univariate and multivariate GARCH models, we test its validity in capturing asymmetry and excess kurtosis of heteroscedastic asset returns. We find that the S_U-normal distribution outperforms the normal and Student-t distributions for describing both the entire shape of the conditional distribution and the extreme tail shape of daily exchange rates and stock returns. The goodness-of-fit (GoF) results indicate that the skewness and excess kurtosis are better captured by the S_U-normal distribution. The exceeding ratio (ER) test results indicate that the S_U-normal is superior to the normal and Student-t distributions, which consistently underestimate both the lower and upper extreme tails, and tend to overestimate the lower tail in general.     

Equilibrium asset pricing and the cross section of expected returns

Annals of Finance - In a mean-variance framework with a representative agent, any linear model for the cross section of expected returns can be supported as an equilibrium as long as the market...     

Normality of stock returns with event time clocks

We introduce a different way to measure time using event clocks, with which we can observe a normal distribution of intraday stock returns. Most finance studies employ a ‘default’ time measurement that uses a calendar clock. Cumulative evidence from prior literature shows that returns with a calendar clock follow a distribution with an excess kurtosis and a heavier tail, relative to a normal distribution. We examine the distribution of intraday stock returns using different clocks. We find that returns do not follow a normal distribution with a traditional calendar clock, but do follow a normal distribution when event clocks are applied.     

The relationship between asset growth and the cross-section of stock returns

There is a large body of literature examining the association between stock characteristics and the cross-section of stock returns in international markets. Recently, Cooper et al. (2008) reported a strong association between total asset growth and stock returns in the US. In this paper, we show that an asset-growth effect also exists in the Australian equity market. Of particular interest, it is present amongst the largest Australian stocks. Over the 1983-2007 period, an equally-weighted portfolio of low-growth Big stocks outperforms a portfolio of high-growth Big stocks by an average 1% per month, equating to nearly 13% per annum. At an individual stock level of analysis, the asset-growth effect remains even after controlling for other variables whose association with the cross-section of returns is well known. Finally, we explicitly test whether asset growth is a priced risk factor using the common two-stage cross-sectional regression methodology. We find no evidence to support a risk-based explanation, thereby lending credence to Cooper et al.’s (2008) suggestion that the asset-growth effect is attributable to mispricing.     

Buy high,sell low: How listed firms price asset transfers in related party transactions

We examine a sample of 254 related party and arms’ length acquisitions and sales of assets in Hong Kong during 1998–2000. Our analysis shows that publicly listed firms enter deals with related parties at unfavourable prices compared to similar arms’ length deals. Firms acquire assets from related parties by paying a higher price compared to similar arms’ length deals. In contrast, when they sell assets to related parties, they receive a lower price than in similar arms’ length deals. With the exception of audit committees, corporate governance characteristics have limited impact on transaction prices. Firms with audit committees on their boards pay lower prices to related parties for acquisitions and receive higher prices from related parties from divestments.     

Buy and sell dynamics following high market returns: Evidence from China

We provide a closer look at the trading dynamics which may give rise to the positive relationship between market trading volume and its lagged returns. Chinese market turnover increases sharply with past day returns. A comprehensive dataset which facilitates the tracing of trading activities among different groups of investors reveals that when previous market returns are high, investors with larger (smaller) average trade size increase their buy (sell) volume. Our findings indicate an important role of differing responses to market information among different classes of investors (e.g. different priors) in explaining this recently documented phenomenon.     

Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios

This paper presents an option positioning that allows us to infer forward variances from option portfolios. The forward variances we construct from equity index options help to predict (i) growth in measures of real economic activity, (ii) Treasury bill returns, (iii) stock market returns, and (iv) changes in variance swap rates. Our yardstick for measuring predictive ability is both individual and joint parameter statistical significance within a market, as well as across a set of markets.     

Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns

This paper utilizes the most flexible skewed generalized t (SGT) distribution for describing petroleum and metal volatilities that are characterized by leptokurtosis and skewness in order to provide better approximations of the reality. The empirical results indicate that the forecasted Value-at-Risk (VaR) obtained using the SGT distribution provides the most accurate out-of-sample forecasts for both the petroleum and metal markets. With regard to the unconditional and conditional coverage tests, the SGT distribution produces the most appropriate VaR estimates in terms of the total number of rejections; this is followed by the nonparametric distribution, generalized error distribution (GED), and finally the normal distribution. Similarly, in the dynamic quantile test, the VaR estimates generated by the SGT and nonparametric distributions perform better than that generated by other distributions. Finally, in the superior predictive test, the SGT distribution has significantly lower capital requirements than the nonparametric distribution for most commodities.     

Monotonicity in asset returns: New tests with applications to the term structure,the CAPM,and portfolio sorts

Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model (CAPM) implies higher expected returns for stocks with higher betas; and standard asset pricing models imply that the pricing kernel is declining in market returns. The full set of implications of monotonicity is generally not exploited in empirical work, however. This paper proposes new and simple ways to test for monotonicity in financial variables and compares the proposed tests with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.     

Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio

This study proposes a new price impact ratio as an alternative to the widely used Amihud’s (2002) Return-to-Volume ratio. We demonstrate that the new price impact ratio, which is deemed Return-to-Turnover ratio, has a number of appealing features. Using daily data from all stocks listed on the London Stock Exchange over the period 1991–2008, we provide overwhelming evidence that this ratio, while being unequivocal to construct and interpret, is also free of a size bias. More importantly, it encapsulates the stocks’ cross-sectional variability in trading frequency, a relatively neglected but possibly important determinant of stock returns given the recently observed trends in financial markets. Overall, our findings argue against the conventional wisdom that there is a simple direct link between trading costs and stock returns by strongly suggesting that it is the compound effect of trading frequency and transaction costs that matters for asset pricing, not each aspect in isolation.     

The frequency and size of price changes: evidence from non-parametric estimations

The majority of price setting models predict a negative correlation between the frequency and size of price changes. Using a unique micro-level price data from Slovakia, we find that a negative correlation between frequency and size of price changes holds only for more rigid prices. On the other hand, less rigid prices such as gasoline prices exhibit positive correlation in line with Rotemberg's pricing model.     

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.