Time to default in the UK mortgage market

This paper employs duration analysis to investigate the timing of default in the UK mortgage market. Our analysis is performed on an ex ante basis, in that our explanatory variables are available to mortgage lenders when the loan is first made. We estimate both standard Weibull distributions and generalizations of the Weibull that permit non-monotonic hazard functions. The models fit the data well, suggesting that we have captured the major sources of variation in duration. We find that ‘cash flow’ variables, such as salary and interest rate paid, play the largest role. Surprisingly, loan-to-value ratios are either insignificant or influence default times in a counter-intuitive direction.     

INTERNATIONAL INVESTORS' EXPOSURE TO RISK IN EMERGING MARKETS

We examine the empirical differences in emerging market betas (β's) taken across four major currencies (U.S. dollar, British sterling, Japanese yen, and German mark) where the β's are either mean-variance or mean-lower partial moment β's. The mean variance β's are found to be statistically similar to lower partial moment β's in most cases, which suggests they are robust to nonnormality in the data. The difference between the two β's has become less significant in recent years as emerging markets have become more stable. Furthermore, evidence is presented that β's obtained from both risk measures and calculated from returns denominated in different currencies have the same ordinal association. This shows the primacy of local risk over foreign exchange risk. We conclude that international investors can continue to use the mean-variance β in assessing risk in emerging markets, although investors should not give it a conventional equilibrium interpretation.     

Forward and spot exchange rates in a bivariate TAR framework

Structural exchange rate models explain only a small part of the movements in dollar exchange rate. Recent empirical work has focused on the failure to account for nonlinearities in the data generating mechanism, as an explanation of this bad performance. Here two bivariate threshold autoregressive models for the spot and forward exchange rates are considered. In the first model the regimes are determined by the log difference of the two rates; in the second one the regimes are driven by the forward spot no-arbitrage condition. These processes are able to capture the ‘swing’ behaviour observed in the exchange rate market. Finally the forecasting ability of the models for the dollar/DM exchange rate is evaluated by stochastic simulation.     

Can NN-algorithms and macroeconomic data improve OLS industry returns forecasts?∗

Using an extensive range of macroeconomic indicators and a number of two-stage models mixing OLS and a non-parametric approach known as the nearest neighbour algorithm, the authors analyse the potential for improving forecasts of US industry returns over those built by OLS on industry-specific variables only. Basic performance is measured by the average cross-sectional correlation over time between the 55 forecasted returns and the realized returns across industries. Since investors and asset managers typically want a steady performance over time, the volatility of this cross-sectional correlation is further taken into account in an adaptation of the Sharpe Ratio. Strong evidence is found in favour of certain macroeconomic factors as dominant industry return predictors, and some two-stage models based either purely on OLS or a mix between OLS and the non-linear model can lift both cross-sectional forecasting correlation and Sharpe Ratio. However, viewed overall in relation to the benchmark OLS model, performance is not consistently improved by any particular model.     

Unsmoothing Real Estate Returns: A Regime‐Switching Approach

We propose newly developed unsmoothing techniques for appraisal‐based real estate returns based on a regime‐switching threshold autoregressive (TAR) model. We show that when true returns follow a TAR process, conventional linear autoregressive techniques are misspecified and underestimate true variance. Two exogenous variables, equity returns and gross domestic product growth, outperform other variables as regime indicators and appear to capture risks of downturns in real estate. We extend the model to the smoothing equation, allowing for switching behavior by appraisers, using two new techniques: the TAR‐AR and TAR‐TAR approaches. The “co‐switching” specification opens up a new frontier of empirical research. We estimate the TAR‐TAR using FT returns as the regime indicator, and we find results that outperform conventional smoothing models and have plausible economic explanations.     

Using Bayesian variable selection methods to choose style factors in global stock return models

This paper investigates the presence of global style factors in global equity investment. To this end, we apply Bayesian variable selection methods from the statistics literature to give guidance in the decision to include/omit factors in a global (linear factor) stock return model. Once we have accounted for country and sector, it is possible to see which style or styles best explains current asset returns. This study does not find compelling evidence for global styles as useful explanatory factors in a fixed parameter regression model, once country and sector have been accounted for.     

Testing for infinite order stochastic dominance with applications to finance, risk and income inequality

In this paper we develop a test of infinite order degree stochastic dominance based on the use of the empirical Laplace transform function. Two applications are considered. One uses the income data of Anderson (Econometrica 64:1183–1193, 1996) and derives results consistent with his. In the other application we examine the dominance between the U.S. and U.K. stock markets. Using data on the S&P 500 and the FTALL-Share we show that the U.S. displays infinite order degree stochastic dominance of the U.K.

Skew Brownian Motion and Pricing European Options

Abstract

The volatility smile and systematic mispricing of the Black–Scholes option pricing model are the typical motivation for examining stochastic processes other than geometric Brownian motion to describe the underlying stock price. In this paper a new stochastic process is presented, which is a special case of the skew-Brownian motion of Itô and McKean. The process in question is the sum of a standard Brownian motion and an independent reflecting Brownian motion that is similar in construction to the stochastic representation of a skew-normal random variable. This stochastic process is taken in its exponential form to price European options. The derived option price nests the Black–Scholes equation as a special case and is flexible enough to accommodate stochastic volatility as well as stochastic skewness.     

Time series momentum trading strategy and autocorrelation amplification

This paper investigates why general Moving Average (MA) trading rules are widely used by technical analysts and others. We assume general stationary processes for prices and we derive the autocorrelation function for an MA trading rule. Based on our results, we conjecture that autocorrelation amplification is one of the reasons why such trading rules are popular. Using simulated results, we show that the MA rule may be popular because it can identify price momentum and is a simple way of assessing and exploiting the price autocorrelation structure without necessarily knowing its precise structure. This paper then, provides empirical evidence of autocorrelation amplification using 15-year daily price data for 11 major international stock indices.