Investigating the Links between UK House Prices and Share Prices with Copulas

Rakesh K Bissoondeeal,Leonidas Tsiaras

doi:10.1007/s11146-021-09854-0

Rakesh K Bissoondeeal, Leonidas Tsiaras

Open Access

https://doi.org/10.1007/s11146-021-09854-0

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Abstract

We investigate the nonlinear links between the housing and stock markets in the UK using copulas. Our empirical analysis is conducted at both the national and regional levels. We also examine how closely London house prices are linked to those in other parts of the UK. We find that (i) the dependence between the different markets exhibits significant time-variation, (ii) at the national level, the relationship between house prices and the stock market is characterised by left tail dependence, i.e., they are more likely to crash, rather than boom, together, (iii) although left tail dependence with the stock market is a prominent feature of some regions, it is by no means a universally shared characteristic, (iv) the dependence between property prices in London and other parts of the UK displays widespread regional variations.

Highlights

The stock and housing markets are sources of investment assets for many investors, but are two important pillars of an economy
For North West (NW), West Midlands (WM) and SC, our empirical results indicate that the GJR-generalised autoregressive conditional heteroscedasticity (GARCH) model with a Normal disribution is the more appropriate specification
Subsequent rows provide the average of the difference in the conditional probabilities between the optimal model and (a) the constant Normal copula, (b) the best constant copula and (c) the time-varying (TV) Normal copula. (ii) Positive numbers indicate the likelihood of joint extremes is underestimated study the linkages between property prices in London and other parts of the UK, our analysis shows that these linkages are predominantly time-varying and the nature of dependence varies greatly from region to region

Summary

Introduction

The stock and housing markets are sources of investment assets for many investors, but are two important pillars of an economy. Our paper shows that the dependence between the stock and national UK housing market is both asymmetric and time-varying and that using a constant copula can significantly underestimate tail risk. To examine the dependence structure between the stock and housing markets, we attempt to model their conditional joint distribution using copula theory.

Results

Conclusion