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.     

Dynamic Currency Linkages and Their Determinants: An Empirical Study for East Asian Economic Community Region

In this article, Copula GARCH models have been employed to study the inter-temporal process of currency market co-movements between ASEAN+6 countries (referred to in this study as East Asian Economic Community) and ASEAN+6 currency market index. Empirical results show that the sample countries of the region exhibit varying levels of currency co-movements with the Asian benchmark. Markov regime switching results show that many of the countries which had high dependences with the regional currency index as was found in copula estimations had also overlapping currency market cycles. Using Principal Component Analysis, we find that three statistical factors explain exchange rate co-movements which came out to be trade linkages, economic risk, and currency market openness in our dynamic panel data estimation.     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

MODELLING A REGIME‐SHIFTING BEVERIDGE CURVE

This paper offers new insights into Beveridge curve analysis by modelling the unemployment–vacancy rate relationship within a Markov regime‐switching environment in which the probabilities of curve‐shifting are determined endogenously by shift factors. Shift factors include structural factors such as labour market participation and net migration, while cyclical variables include GDP growth, the real rate of interest, and labour productivity. This approach enables us to estimate regime‐specific parameters and to assess the role played by these factors in influencing the transition probabilities of switching between regimes. Using New Zealand data, we show that increases in the participation rate have shifted the Beveridge curve inward, while increases in net migration have shifted the curve outward.     

Examination of Taiwan's travel and tourism market cycle through a two-period Markov regime-switching model

This study analyzes the Taiwan travel and tourism (T&T) market cycle. According to data of the weighted Taiwan tourism stock index from January 1997 to August 2015, the two-period Markov regime-switching model identifies two distinct regimes of the T&T cycle and incorporates a specific set of mean and variance parameters for each period to control for the structural changes in Taiwan's T&T market. The effect of China visitor arrivals to Taiwan on the T&T market is also verified. The empirical findings offer essential information and policy implications for Taiwan's government tourism policymakers and business managers.     

Overlapping Generations and Environmental Policy： An Introduction

A small but growing body of literature uses overlapping generations （OLG） models to study environmental policy for long-lived problems such as climate change. An OLG model, unlike the infinitely lived representative agent model, dis- tinguishes between impatience with respect to one＇s own future utility, and attitudes toward successors＇ utility. I discuss the problem of time inconsistency, the role of Markov perfection, and show that a class of OLG models can be studied using me- thods developed to analyze models of non-constant discounting. An example illu- strates the techniques and determines the conditions under which, in equilibrium, there is under-investment or over-investment in natural capital.     

Patterns of poverty among elderly Americans: a latent class Markov model

This article studies poverty persistence and the role of social security programmes on poverty among elderly in the US. We use a Latent Markov model to disentangle unobserved heterogeneity and state dependence. Because of its dynamic nature, unobserved heterogeneity is modelled to vary over time. This allows to capture different latent states of poverty that change over time. Result indicates the existence of three unobserved types evolving over time according to their propensity to be poor. Moreover, a strong persistence in poverty especially for women, individuals living alone and ethnic minorities is found. Finally, the estimates indicate that giving social assistance tends to reduce poverty.     

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.