Kwok Ping Tsang

What Does the Yield Curve Tell Us About Exchange Rate Predictability

Since the term structure of interest rates embodies information about future economic activity, we extract relative Nelson-Siegel (1987) factors from cross-country yield curve differences to proxy expected movements in future exchange rate fundamentals. Using monthly data for the United Kingdom, Canada, Japan, and the United States, we show that the yield curve factors predict exchange rate movements and explain excess currency returns one month to two years ahead. Our results provide support for the asset pricing formulation of exchange rate determination and offer an intuitive explanation to the uncovered interest parity puzzle by relating currency risk premiums to inflation and business cycle risks.

Anchoring and Loss Aversion in the Housing Market: Implications on Price Dynamics

In this paper we develop a simple model with anchoring and loss aversion to explain house price dynamics. We have two testable implications: 1) when both cognitive biases are present, price dispersion and trade volume are pro-cyclical; 2) if anchoring decreases with time, then price dispersion and trade volume are higher for transactions whose previous purchase is more recent. Using a dataset that contains most real estate transactions in Hong Kong from 1992 to 2006, we find strong and significant anchoring and loss aversion which are robust to type of housing and sample period. The finding is consistent with the strong correlation between house price, price dispersion, and volume in the data. Moreover, anchoring decreases with time since previous transaction, and both price dispersion and volume show the same pattern. Our results suggest that anchoring and loss aversion can induce cyclicality in house prices.

Frequency Dependence in a Real-Time Monetary Policy Rule

We estimate a monetary policy rule for the US allowing for possible frequency de- pendence - i.e., allowing the central bank to respond differently to persistent innovations than to transitory innovations, in both the real-time unemployment rate and the real-time inflation rate. The estimation method we use is flexible, and requires no strong a priori as- sumptions on the pattern of frequency dependence or on the nature of the data-generating process. It also allows for possible feedback in the relationship. As suggested by theory, our results convincingly reject linearity in the monetary policy rule in the sense that the coefficients in the monetary policy rule are found to be frequency dependent: the response depends on the persistence of a fluctuation in unemployment or inflation. Our approach also provides useful insights into how the central banks monetary policy rule has varied over time.

Tax-driven Bunching of Housing Market Transactions: The Case of Hong Kong

We study the implications of a property market transaction tax. As property buyers are obligated to pay a transaction tax (i§stamp dutyi¨ or SD) where the rate increases with the value of the transaction, there are incentives to trade at the cutoff points of the tax schedule or just below them. Thus, both i§bunching in transactionsi¨ and i§underpricingi¨ should be observed near those cutoffs. Furthermore, the bunching points should change with the tax schedule. We confirm these conjectures with a rich dataset from the Hong Kong housing market and provide a measure of tax avoidance.

Can Anchoring and Loss Aversion Explain the Predictability in the Housing Market

We offer an explanation of why changes in house prices are predictable. Extending the static model in Leung and Tsang (2010), we analyze the housing market with loss averse sellers and anchoring buyers in a dynamic setting. A buyers current offer price increases with the housing units previous purchase price, and the seller has the tendency to delay the sale of a housing unit that has a loss. We show that when both cognitive biases are present, changes in house prices are predicted by price dispersion and trade volume. Using a sample of housing transactions in Hong Kong from 1992 to 2006, we find that price dispersion and transaction volume are indeed powerful predictors of housing return. For forecasting both in and out of sample, the two variables perform as well as conventional predictors like real interest rate and real stock return.

Does the Right to Choose Matter for Defined Contribution Plans

We find that sensitivity of fund flows and fund performance are both related to participants’ right to choose their investments in defined contribution plans. Under the Mandatory Provident Fund system of Hong Kong, both employers and employees are required to contribute to a retirement account. Originally, employees’ investment choices were restricted to a subset of funds chosen by their employers. The system was later modified so that employees are allowed to invest in any fund within the system. We present evidence that flows of fund have become more sensitive to past fund performance after this policy change, and that average fund performance in the system has also improved. Based on the improvement in fund performance, we estimate the accumulated cost of the lack of choice to be around 10% of the current total asset value of the system.

The Impact of Monetary Policy on Local Housing Markets: Do Regulations Matter?

This paper shows that monetary policy has uneven impacts on local housing markets, and that the magnitude of the impacts are correlated with housing supply regulations. We apply the linearized present value model, which allows the log rent–price ratio to be decomposed into the expected present values of all future real interests rates, real housing premia, and real rent growth, to the housing markets in 23 US metropolitan statistical areas. Based on the indirect inference bias-corrected VAR estimates, we find that MSAs that are more regulated have (i) a higher variance in the log rent–price ratio, (ii) a larger share of the variance explained by real interest rate, and (iii) a stronger impulse response of house price to the real interest rate shock.We apply the linearized present value model, which allows the log rent-price ratio to be decomposed into the expected present value of all future real interests rates, real housing premia, and real rent growth, to the housing market in 23 U.S. metropolitan areas from 1978 to 2011. Based on the indirect inference bias-corrected VAR estimates, we show that variation in the pricing error accounts for half of volatility of log rent-price ratio, and the remaining volatility is mainly contributed by the expected future real interest rates. In addition, a change in the real interest rate has an immediate impact on the fundamental house price, and the impact is significantly larger for more regulated housing markets.

Can Anchoring and Loss Aversion Explain the Predictability of Housing Prices

We offer an explanation of why changes in house price are predictable. We consider a housing market with loss‐averse sellers and anchoring buyers in a dynamic setting. We show that when both cognitive biases are present, changes in house prices are predicted by price dispersion and trade volume. Using a sample of housing transactions in Hong Kong from 1992 to 2006, we find that price dispersion and transaction volume are, indeed, powerful predictors of housing return. For both in and out of sample, the two variables predict as well as conventional predictors such as the real interest rate and real stock return.

Nonexponential Discounting: A Direct Test and Perhaps a New Puzzle

Standard models of intertemporal utility maximization assume that agents discount future utility flows at a constant rate—exponential discounting. Euler equations estimated over different time horizons should have equal discount rates but they do not. Rising term yield premia imply discount rates that rise with longer horizons since uncertainty is much too small to account for the difference in interest rates. Such deviations from exponential discounting are large enough to make a significant difference in consumption choices over long horizons. Our results can be viewed as providing estimates of horizon-specific discounts, or as a further puzzle concerning intertemporal substitution and uncertainty.

What Cycles? Data Detrending in DSGE Models

It is widely-known that different methods of detrending data yield different business cycle features. The choice of the detrending method, however, is usually arbitrarily made. This paper aims at revealing potential pitfalls of different detrending methods for the estimation of a standard medium-scale DSGE model. By comparing nine popular detrending methods, we find that model parameter estimates, variance decompositions, optimal monetary policies, and out-of-sample forecasting performances of the model are all sensitive to how the data are detrended. We also discuss some possible criteria to choose among different methods.