Optimal VWAP trading under noisy conditions

This article proposes an empirically tractable way to incorporate intra-day noise into a VWAP trading rule. In volatile markets, news arrives unexpectedly and rapidly. This should influence a trader’s trading decisions. However, the literature has not incorporated such information into an algorithmic trading framework. Subsequently, this paper presents a Dynamic VWAP (DVWAP) framework that allows informed traders to utilize random news; and thus, improve trade-execution.     

An analytical approximation for pricing VWAP options

This paper proposes a unified approximation method for various options whose pay-offs depend on the volume weighted average price (VWAP). Despite their popularity in practice, very few pricing models have been developed in the literature. Also, in previous works, the underlying asset process has been restricted to a geometric Brownian motion. In contrast, our method is applicable to the general class of continuous Markov processes such as local volatility models. Moreover, our method can be used for any type of VWAP options with fixed-strike, floating-strike, continuously sampled, discretely sampled, forward-start and in-progress transactions.     

Optimal execution with uncertain order fills in Almgren–Chriss framework

The classical price impact model of Almgren and Chriss is extended to incorporate the uncertainty of order fills. The extended model can be recast as alternatives to uncertain impact models and stochastic liquidity models. Optimal strategies are determined by maximizing the expected final profit and loss (P&L) and various P&L-risk tradeoffs including utility maximization. Closed form expressions for optimal strategies are obtained in linear cases. The results suggest a type of adaptive volume weighted average price, adaptive percentage of volume and adaptive Almgren–Chriss strategies. VWAP and classical Almgren–Chriss strategies are recovered as limiting cases with a different characteristic time scale of liquidation for the latter.     

Wavelet decomposition for intra-day volume dynamics

In a follow up to a previous paper where a model was presented for intra-day volume dynamics, we use wavelet decomposition for model parameter estimation. We run Monte-Carlo simulations of the model with these estimated parameters and compare with observed volume curves. This model in its calibrated form can be used for various execution strategies, e.g. in estimation of potential slippage deviations from VWAP benchmarks.     

A generalized volatility bound for dynamic economies

We develop a generalization of the Hansen-Jagannathan (1991) volatility bound that (i) incorporates the serial correlation properties of return data and (ii) allows us to calculate a spectral version of the bound. This generalization enables us to judge whether models match important aspects of the data in the long run, at business cycle frequencies, seasonal frequencies, etc. Our bound permits evaluation of models without requiring their explicit solution in a way that respects the dynamic implications of the fundamental component of the models, namely, the Euler equation that links asset returns to the intertemporal marginal rate of substitution.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

Hedge fund portfolio construction: A comparison of static and dynamic approaches

This article studies the impact of modeling time-varying covariances/correlations of hedge fund returns in terms of hedge fund portfolio construction and risk measurement. We use a variety of static and dynamic covariance/correlation prediction models and compare the optimized portfolios’ out-of-sample performance. We find that dynamic covariance/correlation models construct portfolios with lower risk and higher out-of-sample risk-adjusted realized return. The tail-risk of the constructed portfolios is also lower. Using a mean-conditional-value-at-risk framework we show that dynamic covariance/correlation models are also successful in constructing portfolios with minimum tail-risk.     

Who wins in the money game? The case of KOSPI 200 futures

This study investigates the information advantage of each investor category (i.e., foreigners, individuals, institutions, and the others) in the Korean stock index (i.e., KOSPI 200) futures market. To evaluate the information advantage of each investor category, we calculate futures trading and position gains; review and criticize the Value Weighted Average Price (VWAP) measure; and develop adjusted VWAP measures that are consistent with trading gains. Our main inferences are made based on adjusted VWAP measures. This study analyzes the transaction data of the nine years from January 1998 through 2006. From the perspectives of the adjusted VWAP measures, our findings support the information advantage of foreign investors over domestic (particularly, domestic individual) investors in the KOSPI 200 futures market.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Disagreement and Learning: Dynamic Patterns of Trade

The empirical evidence on investor disagreement and trading volume is difficult to reconcile in standard rational expectations models. We develop a dynamic model in which investors disagree about the interpretation of public information. We obtain a closed‐form linear equilibrium that allows us to study which restrictions on the disagreement process yield empirically observed volume and return dynamics. We show that when investors have infrequent but major disagreements, there is positive autocorrelation in volume and positive correlation between volume and volatility. We also derive novel empirical predictions that relate the degree and frequency of disagreement to volume and volatility dynamics.     

Investor interest, trading volume, and the choice of IPO mechanism in France

This paper investigates the relationship between underpricing and investor interest level prior to and after the IPO date. Empirical tests show a significant 3-day buy-and-hold abnormal return of 19.15%. It is positively related to the share demand-to-offer ratio in the pre-market period and to trading volume in the aftermarket. Despite a high initial underpricing for some book-built issues, the book-building procedure allows for more effective pricing and a lower divergence of opinion among investors in the aftermarket than the auction-like procedure.     

Learning from experience and trading volume

We build a model where trading allows inexperienced agents to discern useful information sources. Upon losing money by trading on invalid information sources, investors learn from their experience and switch to alternative sources. Such activity leads to initial expected losses but later profitable trades. Trading activity is found to be increasing in the mass of such agents. Volume is greatest in firms with uncertain cash flows. Further, a greater number of information sources implies greater volume. This is consistent with the explosive growth in volume accompanying the growth of the internet, which presumably increases the number of heterogeneous information sources.     

Asymmetric Volume‐Return Relation and Concentrated Trading in LIFFE Futures

This study demonstrates that intraday volume and return on LIFFE interest rate and currency futures exhibit an asymmetric volume‐return relationship characterised by significantly larger volume associated with negative returns than with non‐negative returns. This finding is unlike the stylised asymmetric relation often observed in equity markets, where the volume on price rise is larger than the volume on price decline. The asymmetric relationship in LIFFE futures is also found to be dynamic as the direction of asymmetry can reverse during the day. It has been argued in the past that a costly short sale restriction that requires a higher transaction cost on a short position than on a long position is responsible for the asymmetric effect in equity markets. Since such a restriction is absent in futures markets, they should not exhibit any asymmetric volume behaviour. Based on the results of this research, the costly short sale hypothesis is rejected. An alternative explanation of the asymmetric relation observed in futures is presented based on recent information models that take into consideration asymmetrically‐informed traders, their dispersion of beliefs, quality and quantity of the information signal, and how the traders process it. The paper also confirms a strong U‐shape trading pattern in 15‐minute volume, but no such pattern is identified in intraday returns.     

How much of the diversification discount can be explained by poor corporate governance?

We investigate whether the diversification discount occurs partly as an artifact of poor corporate governance. In panel data models, we find that the discount narrows by 16% to 21% when we add governance variables as regression controls. We also estimate Heckman selection models that account for the endogeneity of diversification and dynamic panel generalized method of moments models that account for the endogeneity of both diversification and governance. We find that the diversification discount persists even with these controls for endogeneity. However, in selection models the discount disappears entirely when we introduce governance variables in the second stage, and in dynamic panel GMM models the discount narrows by 37% when we include governance variables.     

Pricing the term structure with linear regressions

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.     

A unique “T + 1 trading rule” in China: Theory and evidence

Unique to the world, China adopts a “T + 1 trading rule”, which prevents investors from selling stocks bought on the same day. We develop a dynamic price manipulation model to study the effects of the “T + 1 trading rule”. Compared to the “T + 0 trading rule”, which allows investors to buy and sell the same stocks during the same day, we show that the “T + 1 trading rule” reduces the total trading volume and price volatility, and improves the trend chasers’ welfare when trend-chasing is strong. An empirical test using data on China’s B-share stock market supports the model’s theoretical predictions.     

INTRADAY STEALTH TRADING: WHICH TRADES MOVE PRICES DURING PERIODS OF HIGH VOLUME?

Research documents a U-shaped intraday pattern of returns. We examine which trade sizes drive the U-shaped pattern and find that intraday price changes from larger trades exhibit a U-shaped pattern whereas price changes from smaller trades show a reverse U-shaped pattern. We argue that price changes from smaller trades are higher during the middle of the day because informed investors break up their trades to disguise their information when intraday volume is low. Price changes from larger trades are likely higher at the beginning and end of the day because high volume allows informed investors to increase their trade size without revealing their information to the market.     

CONCENTRATED OPENING VOLUME: MARKET CLOSURE OR STRATEGIC TRADING?

The NYSE extended its trading hours on September 30, 1985, by opening at 9:30 a.m. rather than at 10:00 a.m. Whereas the market closure models predict that the extension would result in lower relative volume and return variability at the open, the strategic trading models predict that the opening volume and return variability would remain relatively the same. Evidence around the extension suggests that the relative opening volume and return variability declined initially but increased gradually to their pre‐extension levels. This evidence favors the strategic trading model explanation of the heightened opening volume.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

ADAPTIVE MESH MODELING AND BARRIER OPTION PRICING UNDER A JUMP‐DIFFUSION PROCESS

The computational burden of numerical barrier option pricing is significant, even prohibitive, for some parameterizations—especially for more realistic models of underlying asset behavior, such as jump diffusions. We extend a binomial jump diffusion pricing algorithm into a trinomial setting and demonstrate how an adaptive mesh may fit into the model. Our result is a barrier option pricing method that employs fewer computational resources, reducing run times substantially. We demonstrate that this extension allows the pricing of options that were previously computationally infeasible and examine the parameterizations in which use of the adaptive mesh is most beneficial.