Liquidity and Asset Pricing: Evidence on the Role of Investor Holding Period

We use data on actual holding periods for all investors in a stock market over a 10-year period to investigate the links between holding periods, liquidity, and asset returns. Microstructure measures of liquidity are shown to be important determinants of the holding period decision of individual investors. Average holding periods differ across different investor types. Turnover is an imperfect proxy for holding period. While both turnover and spread are related to stock returns, holding period is not.Market microstructure; Holding period; duration

Market Liquidity as a Sentiment Indicator

We build a model that helps explain why increases in liquidity - such as lower bid-ask spreads, a lower price impact of trade, or higher share turnover - predict lower subsequent returns in both firm-level and aggregate data. The model features a class of irrational investors, who underreact to the information contained in order flow, thereby boosting liquidity. In the presence of short-sales constraints, unusually high liquidity is a symptom of the fact that the market is currently dominated by these irrational investors, and hence is overvalued. This theory can also explain how managers might successfully time the market for seasoned equity offerings (SEOs), by simply following a rule of thumb that involves issuing when the SEO market is particularly liquid. Empirically, we find that: i) aggregate measures of equity issuance and share turnover are highly correlated; yet ii) in a multiple regression, both have incremental predictive power for future equal-weighted market returns.

Long Memory on the German Stock Exchange

In this study, the contributors present the results of their investigations into the long-memory properties of trading volume and the volatility of stock returns (given by absolute returns and alternatively by square returns). Their database is daily stock data of German companies in the DAX segment of the German Stock Exchange. The purpose of these investigations is the calculation of memory parameters and to determine whether there exists the same degree of long memory for trading-volume and return-volatility data. Calculations are performed on daily results from January 1994 to November 2005 and in three sub-periods: January 1994 to December 1997, January 1998 to December 2001, and January 2002 to November 2005.DAX 30; trading volume; univariate and bivariate long memory

Beta lives - some statistical perspectives on the capital asset pricing model

This note summarizes some technical issues relevant to the use of the idea of excess return in empirical modelling. We cover the case where the aim is to construct a measure of expected return on an asset and a model of the CAPM type is used. We review some of the problems and show examples where the basic CAPM may be used to develop other results which relate the expected returns on assets both to the expected return on the market and other factors

Value-at-Risk and Expected Shortfall when there is long range dependence.

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long-term contracts. On the other hand, recent focus is on whether long memory can affect the measurement of market risk in the context of Value-at- Risk (V aR). In this paper, we evaluate the Value-at-Risk (V aR) and Expected Shortfall (ESF) in financial markets under such conditions. We examine one equity portfolio, the British FTSE100 and three stocks of the German DAX index portfolio (Bayer, Siemens and Volkswagen). Classical V aR estimation methodology such as exponential moving average (EMA) as well as extension to cases where long memory is an inherent characteristics of the system are investigated. In particular, we estimate two long memory models, the Fractional Integrated Asymmetric Power-ARCH and the Hyperbolic-GARCH with different error distribution assumptions. Our results show that models that account for asymmetries in the volatility specifications as well as fractional integrated parametrization of the volatility process, perform better in predicting the one-step as well as five-step ahead V aR and ESF for short and long positions than short memory models. This suggests that for proper risk valuation of options, the degree of persistence should be investigated and appropriate models that incorporate the existence of such characteristic be taken into account.Backtesting, Value-at-Risk, Expected Shortfall, Long Memory, Fractional Integrated Volatility Models

A non-arbitrage liquidity model with observable parameters for derivatives

We develop a parameterised model for liquidity effects arising from the trading in an asset. Liquidity is defined via a combination of a trader's individual transaction cost and a price slippage impact, which is felt by all market participants. The chosen definition allows liquidity to be observable in a centralised order-book of an asset as is usually provided in most non-specialist exchanges. The discrete-time version of the model is based on the CRR binomial tree and in the appropriate continuous-time limits we derive various nonlinear partial differential equations. Both versions can be directly applied to the pricing and hedging of options; the nonlinear nature of liquidity leads to natural bid-ask spreads that are based on the liquidity of the market for the underlying and the existence of (super-)replication strategies. We test and calibrate our model set-up empirically with high-frequency data of German blue chips and discuss further extensions to the model, including stochastic liquidity

Large dynamic covariance matrices: Enhancements based on intraday data

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return

Optimal sticky prices under rational inattention

This paper presents a model in which price setting firms decide what to pay attention to, subject to a constraint on information flow. When idiosyncratic conditions are more variable or more important than aggregate conditions, firms pay more attention to idiosyncratic conditions than to aggregate conditions. When we calibrate the model to match the large average absolute size of price changes observed in micro data, prices react fast and by large amounts to idiosyncratic shocks, but prices react only slowly and by small amounts to nominal shocks. Nominal shocks have strong and persistent real effects. We use the model to investigate how the optimal allocation of attention and the dynamics of prices depend on the firms’ environment. JEL Classification: E3, E5, D8rational inattention, real effects of nominal shocks, sticky prices

The Effects of Securities Class Action Litigation on Corporate Liquidity and Investment Policy

The risk of securities class action litigation alters corporate savings and investment policy. Firms with greater exposure to securities litigation hold significantly more cash in anticipation of future settlements and other related costs. The result is due to firms accumulating cash in anticipation of lawsuits and not a consequence of plaintiffs targeting firms with high cash levels. The market value of cash is significantly lower for firms exposed to litigation risk. Corporate investment decisions are also affected by litigation risk, as firms reduce capital expenditures in response. Our results are robust to endogeneity concerns and possible spurious temporal effects

Sample Kurtosis, GARCH-t and the Degrees of Freedom Issue

Econometric modeling based on the Student’s t distribution introduces an additional parameter — the degree of freedom. In this paper we use a simulation study to investigate the ability of (i) the GARCH-t model (Bollerslev, 1987) to estimate the true degree of freedom parameter and (ii) the sample kurtosis coefficient to accurately determine the implied degrees of freedom. Simulation results reveal that the GARCH-t model and the sample kurtosis coefficient provide biased and inconsistent estimates of the degree of freedom parameter. Moreover, by varying ó2, we find that only the constant term in the conditional variance equation is affected, while the other parameters remain unaffected.Student’s t distribution, Degree of freedom, Kurtosis coefficient, GARCH t model