Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

This paper examines the role of nonfundamentals‐based sentiment in house price dynamics, including the well‐documented volatility and persistence of house prices during booms and busts. To measure and isolate sentiment's effect, we employ survey‐based indicators that proxy for the sentiment of three major agents in housing markets: home buyers (demand side), home builders (supply side), and lenders (credit suppliers). After orthogonalizing each sentiment measure against a broad set of fundamental variables, we find strong and consistent evidence that the changing sentiment of all three sets of market participants predicts house price appreciation in subsequent quarters, above and beyond the impact of changes in lagged price changes, fundamentals, and market liquidity. More specifically, a one‐standard‐deviation shock to market sentiment is associated with a 32–57 basis point increase in real house price appreciation over the next two quarters. These price effects are large relative to the average real price appreciation of 71 basis points per quarter observed over the full sample period. Moreover, housing market sentiment and its effect on real house prices is highly persistent. The results also reveal that the dynamic relation between sentiment and house prices can create feedback effects that contribute to the persistence typically observed in house price movements during boom and bust cycles.     

Price dispersion in OTC markets: A new measure of liquidity

In this paper, we model price dispersion effects in over-the-counter (OTC) markets to show that, in the presence of inventory risk for dealers and search costs for investors, traded prices may deviate from the expected market valuation of an asset. We interpret this deviation as a liquidity effect and develop a new liquidity measure quantifying the price dispersion in the context of the US corporate bond market. This market offers a unique opportunity to study liquidity effects since, from October 2004 onwards, all OTC transactions in this market have to be reported to a common database known as the Trade Reporting and Compliance Engine (TRACE). Furthermore, market-wide average price quotes are available from Markit Group Limited, a financial information provider. Thus, it is possible, for the first time, to directly observe deviations between transaction prices and the expected market valuation of securities. We quantify and analyze our new liquidity measure for this market and find significant price dispersion effects that cannot be simply captured by bid-ask spreads. We show that our new measure is indeed related to liquidity by regressing it on commonly-used liquidity proxies and find a strong relation between our proposed liquidity measure and bond characteristics, as well as trading activity variables. Furthermore, we evaluate the reliability of end-of-day marks that traders use to value their positions. Our evidence suggests that the price deviations from expected market valuations are significantly larger and more volatile than previously assumed. Overall, the results presented here improve our understanding of the drivers of liquidity and are important for many applications in OTC markets, in general.     

The Effects of Stock Splits on Bid-Ask Spreads

This paper examines the effects of stock splits on bid-ask spreads for NYSE-listed companies. Percentage spreads increase after splits, representing a liquidity cost to investors. These spread increases are directly related to decreases in share prices following splits and can explain part, but not all, of the observed increase in return variability after splits. The evidence thus suggests a liquidity cost of stock splits that must be weighed against any other perceived benefits of splits. Such a liquidity cost may validate that stock splits are a signal of favorable information about the firm.     

Housing and the Macroeconomy: Inflation and the Financial Accelerator

Three themes connecting housing and the macroeconomy are discussed. First, evidence is presented for the property market as one of the drivers of U.S. consumer price inflation. Second, key drivers of house prices are explained to account for the remarkable diversity of international experience. Finally, three potential links between housing, credit, and the financial accelerator are discussed. These are the consumption channel, the investment channel, and feedback between bad loans and risk‐spreads via the financial system—and how institutional differences between countries can explain the presence, absence and magnitudes of these linkages.     

THE DETERMINANTS AND DYNAMICS OF BID-ASK SPREADS ON THE LONDON STOCK EXCHANGE

We analyze the effect of various factors on the size of spreads on the London Stock Exchange since “Big Bang” and find that the price of a security, volume of transactions, risk associated with security returns, and degree of competition among market makers explain 91 percent of the cross-sectional variation in spreads. The results are consistent with the argument that the inside spread encompasses the order-processing, inventory-adjustment, and adverse-information cost of spreads. We also investigate the speed at which spreads move toward their normal levels after a temporary deviation. Although the speed of adjustment varies across firms, the cross-sectional median of 0.896 indicates it takes more than one period (day) for the adjustment to be completed. The volume of transactions and the degree of competition among market makers are the significant factors that affect the speed of correction in spreads toward their normal levels. This implies private information is incorporated more quickly into prices for stocks with greater competition and high trading volume.     

The Housing Market in the United Kingdom: Effects of House Price Volatility on Households

This paper describes the economics of the housing market and explains why house prices are likely to be more volatile than prices in other markets. It illustrates the volatility of house prices relative to some other key economic variables in the UK. The paper then considers the implications of variability in house prices for household behaviour, surveying a number of studies for the UK and elsewhere on the effects of house price volatility on consumption spending, indebtedness, labour supply, and entry to and exit from the homeownership market.     

Measuring the liquidity impact on EMU government bond prices

Abstract

The work reported in this paper aimed to measure the impact of liquidity on European Monetary Union (EMU) government bond prices. Although there is a growing theoretical and empirical literature on liquidity effects in fixed income markets there is no clear answer to the questions how to measure liquidity and whether liquidity is priced in the market at all. The empirical analysis here is based on a unique data set containing individual bond data from six major EMU government bond markets, allowing one to compare yield curves estimated for subportfolios formed with respect to different potential liquidity measures. In a second procedure, liquidity measures are collected on the individual bond level and estimated pricing errors, given some reference yield curve, are regressed against these liquidity variables. This enables the conduction of formal tests on the pricing impact of liquidity measures. Results indicate that the benchmark property and the number of contributors are the most promising liquidity proxies having significant results in most countries. The results do not support the hypothesis that other liquidity measures under consideration, such as the on-the-run property, the issue size, and bid–ask spread related measures have a persistent price impact. A cross-country analysis of the subportfolio level indicates that liquidity effects cannot explain the size of the yield spreads between different issuers. This implies that effects other than liquidity, such as credit risk, are important driving factors of cross-country yield spreads.     

Option prices and stock market momentum: evidence from China

Option prices tend to be correlated to past stock market returns due to market imperfections. We unprecedentedly examine this issue on the SSE 50 ETF option in the Chinese derivatives market. To measure the price pressure in the options market, we construct an implied volatility spread based on pairs of the SSE 50 ETF option with identical expiration dates and strike prices. By regressing the implied volatility spread on past stock returns, we find that past stock returns exert a strong influence on the pricing of index options. Specifically, we find that SSE 50 ETF calls are significantly overvalued relative to SSE 50 ETF puts after stock price increases and the reverse is also true after the stock price decreases. Moreover, we validate the momentum effects in the underlying stock market to be responsible for the price pressure. These findings are both economically and statistically significant and have important implications.     

What Drives the Owner‐Occupied and Rental Housing Markets? Evidence from an Estimated DSGE Model

Most dynamic stochastic general equilibrium (DSGE) models with a housing market do not explicitly include a rental market and assume a tight mapping between house prices and rents over the business cycle. However, rents are much smoother than house prices in the data. We match this feature of the data by adding both an owner‐occupied housing market and a rental market in a standard DSGE model. The intertemporal preference shock accounts for more than half of the variation in house prices and contributes to residential investment fluctuations through the liquidity constraint, and nominal rigidity in rental contracts captures the variation in the price‐rent ratio.     

Pricing mortgage insurance contracts under housing price cycles with jump risk: evidence from the U.K. housing market

Previous studies have investigated the determinants of housing price cycles in the housing market; however, we observed the phenomenon of housing price jumps in the 2007 subprime crisis. This paper presents a discussion on the housing price cycle and abnormal price jumps to describe the behavior of housing prices in the United Kingdom. The empirical results show that the impact factors of housing cycles are market risk and the switching factor. Furthermore, the impact factors of jump risks include the bursting of the housing bubble and financial crises. Therefore, in this paper, we employ the Markov switching model with jump risks to value the MI contracts and analyze the influences of housing price cycles, jump risks, risks of market interest rate, and the prepayment risks on MI premiums. The results of sensitivity analysis show that more volatile housing price index returns, as well as longer periods of higher volatility in housing prices, raise MI premiums. Moreover, the MI premium is positively related to the absolute value of the average jump amplitude and the shock frequency of abnormal events. There is the tradeoff between the market interest rate and the prepayment risk. The influences of market interest rate are different on MI premium with/without prepayment risks.     

The Components of the Bid‐Ask Spread: the Case of the Athens Stock Exchange

We analyse the components of the bid‐ask spread in the Athens Stock Exchange (ASE), which was recently characterised as a developed market. For large and medium capitalisation stocks, we estimate the adverse selection and the order handling component of the spreads as well as the probability of a trade continuation on the same side of either the bid or the ask price, using the Madhavan et al. (1997) model. We extend it by incorporating the traded volume and we find that the adverse selection component exhibits U‐shape patterns, while the cost component pattern depends on the stock price. For high priced stocks, the usual U‐shape applies, while for low‐priced ones, it is an increasing function of time, mainly due to the order handling spread component. Furthermore, the expected price change and the liquidity adjustment to Value‐at‐Risk that is needed are higher in the low capitalisation stocks, while the most liquid stocks are the high priced ones. Moreover, by estimating the Madhavan et al. (1997) model for two distinct periods we explain why there are differences in the components of the bid‐ask spread.     

Does short selling improve stock price efficiency and liquidity? Evidence from a natural experiment in China

China introduced short selling for designated stocks in March 2010. Using this important policy change as a natural experiment, we examine the effect of short selling on stock price efficiency and liquidity. We show that the introduction of short selling significantly improves price efficiency, as measured by the differences in individual stock responses to market returns and the delay in price adjustments. Short selling also enhances stock liquidity, as measured by bid-ask spread and Amihud [2002. ‘Illiquidity and Stock Returns: Cross-section and Time-series Effects.’ Journal of Financial Markets 5: 31–56] illiquidity measure; and reduces stock volatility. Overall, our results suggest that short selling helps to stabilize asset prices, provides additional liquidity and improves market quality, even in an emerging economy with a less developed stock market than that in the US and Europe.     

To Trade or Not to Trade: The Effect of Broker Search and Discretionary Trading on Securities Market Performance

In this article we examine the interaction of brokerage search with the Bayesian learning behavior of competitive dealers under asymmetric information. We particularly focus on the effects of price search and discretionary trading on the performance of a dealer market. A search process is incorporated into a model in which brokers determine their reservation price and whether to continue their trades. The model enables us to uncover the interrelationships among search cost, bid‐ask spread, and price volatility. We show that both spread revision and price volatility are dependent upon the optimal search process, inventory fluctuation, and search cost. Furthermore, our model predicts a negative relationship between price volatility and liquidity trading volume.     

Modeling volatility and changes in the swap spread

We investigate the determinants of changes in U.S. interest rate swap spreads using a model that explicitly allows for volatility interactions between swaps of different terms to maturity. Changes in the swap spread are found to be positively related to interest rate volatility, to changes in the default risk premium in the corporate bond market, and to changes in the liquidity premium for government securities. Swap spread changes are negatively related to changes in the level of interest rates and changes in the slope of the term structure. We also find that there is a strong and significant volatility interaction among spreads for swaps of different maturities and that the process for the conditional variance of the spread is highly persistent across all maturities.     

Market-Making in the Third Market for NYSE-Listed Securities

This paper empirically examines market making in the third market for common stocks that are listed on the NYSE. Although the same non-NYSE members make a market on both types of stocks, bid-ask spreads are wider on Rule 19c-3 stocks than on Rule 390 stocks. Market-making by NYSE members is minimal and spreads posted by NYSE members are wider than those posted on identical stocks by non-NYSE members. This suggests that NYSE members do not compete on the basis of spread but use methods such as price matching and the internalization of orders to attract order flow to the third market.     

Return spread and liquidity: Evidence from Hong Kong ADRs

Fifteen Chinese H-shares listed on the Stock Exchange of Hong Kong are cross listed as ADRs on the NYSE. We empirically determine the role of security specific liquidity associated with those ADRs and their underlying H-shares on return spreads, differences between the returns on ADRs and their corresponding H-shares after controlling for ADRs and H-shares excess market returns and their respective price inverses denoting conditional betas. We use three proxies for liquidity, trading volume, turnover, and illiquidity (Amihud, 2002) and find that only trading volume and turnover are consistent determinants of return spread for the majority of Chinese ADRs with primary listing in Hong Kong Stock Exchange (SEHK). We use a switching regression model and find that the model parameter estimates are not stationary and change, often drastically between pre and post 2000 and 2003. Further tests using Bai Perron indicate return spreads data as non-stationary with multiple regime changes during the sample period. Further the causes of non-stationarity seem to be largely security specific and not driven by broad market swings in either market.     

The impact of aggressive orders in an order-driven market: a simulation approach

This article investigates resiliency in an order-driven market. On basis of a vector autoregressive model capturing various dimensions of liquidity and their interactions, I simulate the effect of a large liquidity shock, measured by a very aggressive market order. I show that, despite the absence of market makers, the market is resilient. All dimensions of liquidity (spread, depth at the best prices and order book imbalances) revert to their steady-state values within 15 orders after the shock. For prices, a long run effect is found. Furthermore, different dimensions of liquidity interact. Immediately after a liquidity shock, the spread becomes wider than in the steady state, implying that one dimension of liquidity deteriorates, while at the same time, depth at the best prices increases, meaning an improvement of another liquidity dimension. In subsequent periods, the spread reverts back to the steady-state level but also depth decreases. Also, I find evidence for asymmetries in the impact of shocks on the ask and bid side. Shocks on the ask side have a stronger impact than shocks on the bid side. Finally, resiliency is higher for less-frequently traded stocks and stocks with a larger relative tick size.     

Liquidity and price pressure in the corporate bond market: evidence from mega-bonds

Larger bonds offer greater liquidity, which should reduce their yields. A simple way for firms to reduce financing costs is to sell bonds with large face values. We find that mega-bonds are more liquid than smaller bonds. However, offering yield spreads on mega-bonds are not lower and are higher than spreads of bonds issued by similar companies. The discount applied to large new issues is consistent with price pressure effects that are also present in the secondary market prices of the issuing firm's existing bonds. Our results suggest a hidden cost to issuing very liquid bonds.     

Empirical tests of real estate market efficiency

Recent empirical research using real estate data has supported the weak and semi-strong forms of the efficient markets hypothesis. Previous studies have not included an estimate of expected appreciation into the tests of market efficiency, thus raising a question about the reliability of the results. We first use a market model to test for market efficiency with results similar to those reported by others. We next use a dynamic multiple indicator, multiple cause (DYMIMIC) model, which extracts a vector of expected appreciation from the price data, to test market efficiency. This approach produces superior results and a stronger conclusion about the efficiency of housing markets. The results indicate limited adjustment delays which can be explained by the existence of high transactions and search costs.     

金融期货最小价格变化单位的市场影响与最优水平

最小价格变化单位是买卖价差的底线，设置过高会人为地提高买卖价差的水平，增加交易费用；过低又会降低市场深度，增加交易谈判成本，影响交易效率。对最优价格升降档位的设定，要综合考虑精度要求、合约乘数、交易谈判成本和实际买卖价差水平等因素；而以实际买卖价差的底线作为最小价格升降档位，能较好地避免人为抬高买卖价差的情况出现。