Price Limits and Beta

Price limits, which restrict daily price changes of a stock within a pre-specified range, make the stochastic properties of observed returns deviate from those of true returns, and hence lead to a biased estimates of the market model parameters. To investigate the impacts of price limits on the market model parameters, especially on beta, the restricted regression analysis is performed as well as the two-pass regression analysis used in examining the intervalling effect bias on beta. Empirical results suggest that, when prices are observed within a pre-specified bound, the estimates of beta using ordinary least squares substantially understate the true beta and suffer more from the intervalling effect bias. However, the delay effect of price limits on the adjustment of a security's price does not last too long, that is, remaining information is reflected on the subsequent day's stock prices very rapidly.     

Pricing the US residential asset through the rent flow: A cross-sectional study

The paper explores how the standard consumption-CAPM fares in pricing housing returns and regional rental income streams in a cross-section of regions. In particular, we estimate the Euler equations associated with the gross housing returns inclusive of price appreciations and rents jointly for several metropolitan areas of the US. The representative agent has a Constant Relative Risk Aversion (CRRA) utility. The rent growth is allowed to depend on the business cycle. When biannual data from 1978 to 2007 is used, the parameter estimates are reasonable, and the model is not rejected. Large standard errors indicate uninformative estimates. The implied price rent ratio time series averages are similar to the data; however the model misses the boom-bust pattern in the prices. The model significantly understates the average and the variance of the price appreciations. Results are robust to allowing housing consumption directly in the utility function or using the Epstein–Zin–Weil utility.     

Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices

This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state-dependent arrival intensity. We discuss how to perform likelihood-based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously.     

A new paradigm for forecasting security returns in a market regulated by price limits

Numerous futures markets in the US and many stock markets around the world set a “limit” price before each trading session, based on the settlement price at the end of the previous trading day. Price limits are boundaries set by market regulators to restrict large daily fluctuations in the price of securities. Once the return limit is triggered, traders cannot observe the equilibrium return that would have prevailed in the absence of such regulation. We develop an innovative approach for forecasting security returns (and prices) in a market regulated by price limits. Our forecasting model allows for multiple limit-hits. The model is robust, straightforward and easy for practitioners to use. A few numerical predictions are provided for hypothetical securities, and for seven traded futures contracts.     

Dual Class IPOs ARE Underpriced Less Severely

We analyze a sample of dual and single class initial public offerings (IPOs) to investigate whether empirical estimates of underpricing determinants are consistent across alternative measures of firm size and alternative techniques intended to account for underwriter price stabilization efforts. We find that results from long‐standing methods for estimating underpricing relations are generally robust to one's choice of size proxy and are consistent with estimates obtained from censored regressions of first‐day returns and from least squares regressions of longer horizon initial returns. We also confirm an existing finding in the literature that dual class IPOs endure less underpricing than do single class firms.     

Housing Price Dynamics in Time and Space: Predictability,Liquidity and Investor Returns

It is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set—all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.     

EXCESS RETURNS OF INDEX REPLACEMENT STOCKS: EVIDENCE OF LIQUIDITY AND SUBSTITUTABILITY

Excess returns of S&P index replacement stocks are attributed to price pressures and imperfect substitutes in previous research. However, parameter estimates are biased by the use of pre-announcement returns; replacements are characterized by rising stock prices. Using a future estimation period to avoid this bias, we find excess returns do not reverse. Further, we find no relation between excess returns and the amount of stock closely held or the size of index funds. The evidence supports efficient market assumptions: the stock market is liquid and stocks are close substitutes.     

Price limit bands,asymmetric volatility and stock market anomalies: Evidence from emerging markets

I investigate the effects of imposing different bands of price limits on stock returns and volatility in the Egyptian (EGX), Thai (SET) and Korean (KRX) stock exchanges. In addition, the paper examines whether the switch from narrow price limits (NPL) to wider price limits (WPL) structurally alters volatility and the day of the week anomaly. Using the extended EGARCH and PARCH asymmetric volatility models, I found that the switch from NPL to WPL structurally altered both asymmetric volatility and the day of the week anomaly in the EGX, SET and KRX. I argue that the price discovery mechanism is disrupted due to the switch as closing prices do not fully reflect all information arrived in the market when prices hit the limits and that is reflected on volatility and market efficiency.     

Time to wealth goals in capital accumulation

This paper considers the problem of investment of capital in risky assets in a dynamic capital market in continuous time. The model controls risk, and in particular the risk associated with errors in the estimation of asset returns. The framework for investment risk is a geometric Brownian motion model for asset prices, with random rates of return. The information filtration process and the capital allocation decisions are considered separately. The filtration is based on a Bayesian model for asset prices, and an (empirical) Bayes estimator for current price dynamics is developed from the price history. Given the conditional price dynamics, investors allocate wealth to achieve their financial goals efficiently over time. The price updating and wealth reallocations occur when control limits on the wealth process are attained. A Bayesian fractional Kelly strategy is optimal at each rebalancing, assuming that the risky assets are jointly lognormal distributed. The strategy minimizes the expected time to the upper wealth limit while maintaining a high probability of reaching that goal before falling to a lower wealth limit. The fractional Kelly strategy is a blend of the log-optimal portfolio and cash and is equivalently represented by a negative power utility function, under the multivariate lognormal distribution assumption. By rebalancing when control limits are reached, the wealth goals approach provides greater control over downside risk and upside growth. The wealth goals approach with random rebalancing times is compared to the expected utility approach with fixed rebalancing times in an asset allocation problem involving stocks, bonds, and cash.     

Modelling implied volatility with OLS and panel data models

The paper performs an empirical estimation of time-varying volatility using OLS regression. Error Components, and Dummy Variable models, by regressing the implied volatility on time to maturity, the strike price and a dummy. Both the daily OLS equations and the panel data model provide more accurate estimates of Black and Scholes option prices than the bench-mark standard deviation of log returns. FT-SE 100 Index European options are used for empirical analysis.     

The Underpricing and Excess Returns of Initial Public Offerings in Taiwan Based on Noisy Trading: A Stochastic Frontier Model

Initial public offerings (IPOs) are typically offered at prices lower than the transaction price in the early aftermarket. With a stochastic frontier model, we measured the fair offer price of an IPO and then the deliberate IPO underpricing and the market misvaluation based on the estimated fair offer price. Our results show that IPOs are deliberately underpriced. The extent of noisy trading leading to significantly higher market transaction prices explains the excess IPO returns. We conclude that initial IPO returns result primarily from the noisy trading activities instead of the deliberate IPO underpricing.     

Oil and stock market activity when prices go up and down: the case of the oil and gas industry

We examine the asymmetric effects of daily oil price changes on equity returns, market betas, oil betas, return variances, and trading volumes for the US oil and gas industry. The responses of stock returns associated with negative changes in oil prices are higher than that associated with positive changes in oil prices. Stock risk measured by market beta is influenced more due to oil price decreases than due to oil price increases. On the other hand, oil risk exposures (oil betas) and return variances are more influenced by oil price increases than oil price decreases. The results of our study indicate that oil and gas firm returns, market betas, oil betas, return variances respond asymmetrically to oil price changes. We also find that relative changes in oil prices along with firm-specific factors such as firm size, ROA, leverage, market-to-book ratio (MBR) are important in determining the effects of oil price changes on oil and gas firms’ returns, risks, and trading volumes.     

Accounting information and the valuation of Seasoned Equity Offerings (SEOs)

This study investigates the association between publicly available information disclosed in the SEO prospectus and offer prices of SEOs, as well as the association between this type of publicly available information and stock returns subsequent to an SEO after controlling for self-selection bias. The empirical evidence shows that disclosure of the planned uses of the SEO proceeds reveals value-relevant information which has been incorporated by the underwriters in setting the offer prices. Control for self-selection bias appears necessary to obtain unbiased estimates in the regression model explaining the determinants of offer price in SEOs.     

Noisy Prices and Inference Regarding Returns

Temporary deviations of trade prices from fundamental values impart bias to estimates of mean returns to individual securities, to differences in mean returns across portfolios, and to parameters estimated in return regressions. We consider a number of corrections, and show them to be effective under reasonable assumptions. In an application to the Center for Research in Security Prices monthly returns, the corrections indicate significant biases in uncorrected return premium estimates associated with an array of firm characteristics. The bias can be large in economic terms, for example, equal to 50% or more of the corrected estimate for firm size and share price.     

Oil and the Stock Market: An Industry Level Analysis

I investigate the impact of daily oil price changes on the stock returns of a wide array of industries. I find that in addition to the stock returns of industries that depend heavily on oil, stock returns of some industries that use little oil also are sensitive to oil prices perhaps because their main customers are impacted by oil price changes. In addition, I present robust estimates of industries’ cost‐side and demand‐side dependence on oil. These measures can serve as reliable benchmarks when classifying industries into oil‐intensive and non–oil‐intensive groups, a distinction widely used in studies and media without any quantitative justification so far. Further, I find that the sensitivity of industries’ returns to oil price changes depends on both the cost‐side and demand‐side dependence on oil and that the relative effects of these factors vary across industries.     

Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normally distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalised assumption of normally distributed financial returns. Thus it is crucial to model distribution tails properly so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by McNeil and Frey in 2000 and combine GARCH-type models with the extreme value theory to estimate the tails of three financial index returns – S&P 500, FTSE 100 and NIKKEI 225 – representing three important financial areas in the world. Our results indicate that EVT-based conditional quantile estimates are more accurate than those from conventional GARCH models assuming normal or Student's t distribution innovations when doing not only in-sample but also out-of-sample estimation. Moreover, these results are robust to alternative GARCH model specifications. The findings of this paper should be useful to investors in general, since their goal is to be able to forecast unforeseen price movements and take advantage of them by positioning themselves in the market according to these predictions.     

Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets

This paper tests the hypothesis that stock returns in emerging stock markets adjust asymmetrically to past information. The evidence suggests that both the conditional mean and the conditional variance respond asymmetrically to past information. In agreement with studies dealing with developed stock markets, the conditional variance is an asymmetrical function of past innovations, rising proportionately more during market declines. More importantly, the conditional mean is also an asymmetrical function of past returns. Specifically, positive past returns are more persistent than negative past returns of an equal magnitude. This behaviour is consistent with an asymmetric partial adjustment price model where news suggesting overpricing (negative returns) are incorporated faster into current prices than news suggesting underpricing (positive returns). Furthermore, the asymmetric adjustment of prices to past information could be partially responsible for the asymmetries in the conditional variance if the degree of adjustment and the level of volatility are positively related.     

The Relation between Stock Market Movements and NYSE Seat Prices

Exchange seat prices are widely reported and followed as measures of market sentiment. This paper analyzes the information content of NYSE seat prices using: (1) annual seat prices from 1869 to 1998, and (2) the complete record of trades, bids and offers for the seat market from 1973 to 1994. Seat market volumes have predictive power regarding future stock market returns, consistent with a model where seat market activity is a proxy for unobserved factors affecting expected returns. We find abnormally large price movements in seats prior to October 1987, consistent with the hypothesis that seat prices capture market sentiment.     

Overconfidence, Arbitrage, and Equilibrium Asset Pricing

This paper offers a model in which asset prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade against mispricing. In equilibrium, expected returns are linearly related to both risk and mispricing measures (e.g., fundamental/price ratios). With many securities, mispricing of idiosyncratic value components diminishes but systematic mispricing does not. The theory offers untested empirical implications about volume, volatility, fundamental/price ratios, and mean returns, and is consistent with several empirical findings. These include the ability of fundamental/price ratios and market value to forecast returns, and the domination of beta by these variables in some studies.