Modelling and forecasting international interest rate spreads: UK, Germany, Japan and the US

The interest rate spread is of importance to policy-makers and finance professionals in asset allocation and is a common measure of financial market stress. In this paper, we model and forecast the interest rate spreads for a number of countries using two well-known continuous time models and discrete time ARMA and ARFIMA models. We use monthly and weekly data which cover the recent global financial market crisis of 2007-2009 for Germany, Japan, UK and the USA. We find that the Merton's continuous-time model outperforms all other model specifications in terms of the mean of the forecast errors, MAPE and RMSE

US and Canadian term structures of interest rates: A forecasting comparison

This paper provides empirical evidence for the US and Canadian yield curves using a one- and two-factor Generalised Vasicek model, using a data set comprised of daily panel data over the period between 2003 and 2011, which includes the recent global financial crisis. The two-factor model is found to have a good fit for both the US and Canadian yield curves. We also compare the forecasting performance of the term structure model with those from ARIMA, ARFIMA and Nelson-Siegel models. We find that for Canada the Nelson-Siegel model dominates, while for the US the ARFIMA model has a satisfactory performance

The volatility of Greek interbank rates : a continuous time analysis

In this paper we investigate the relationship between the volatility of Greek interbank rates and the level of rates by estimating the important CKLS interest rate model using the estimation method of (Nowman, 1997). We also estimate the interest rate models of Merton, Vasicek, CIRSR, Dothan, GBM, Brennan and Schwartz, CIRVR, and CEV models. We find the volatility of short-term rates is highly sensitive to the level of rates in Greece and is much higher than is usually assumed by these commonly used models in the financial markets.peer-reviewe

A highly sensitive mean-reverting process in finance and the Euler-Maruyama approximations

Empirical studies show that the most successful continuous-time models of the short term rate in capturing the dynamics are those that allow the volatility of interestchanges to be highly sensitive to the level of the rate. However, from the mathematics, the high sensitivity to the level implies that the coeffcients do not satisfy the lineargrowth condition, so we can not examine its properties by traditional techniques. This paper overcomes the mathematical difculties due to the nonlinear growth and examines its analytical properties and the convergence of numerical solutions in probability. The convergence result can be used to justify the method within Monte-Carlo simulations that compute the expected payoff of financial products. For illustration, we apply our results compute the value of a bond with interest rate given by the highly sensitive mean-reverting process as well as the value of a single barrier call option with the asset price governed by this process

Empirical analysis of the US swap curve

This paper provides an empirical analysis of the US swap rate curve using principal components analysis (PCA) to identify the factors which explain the variation in the data. We also investigate the forecasting performance of different econometric models for individual maturities across the curve using daily data over the period 1998 to 2011. The PCA analysis indicates that the first two factors explain approximately 99.76% of the cumulative variation in the sample. We also find that a continuous time modelling approach has a satisfactory performance across the curve based on the RMSE

A proposed framework for developing user-centred mobile healthcare applications for the biggest annual mass gathering (Hajj) post COVID-19

The Hajj pilgrimage being the largest annual mass gathering globally with two to three million participants from over 180 counties, will remain a high priority for diseases surveillance for future epidemics or any other international public health emergencies with rapid scalability. This paper highlights the importance of monitoring mass gatherings during a pandemic and how mHealth applications can reduce the burden on health facilities during a mass gathering and tackle future infectious diseases outbreaks. The paper also highlights the importance of developing a user-centred application when designing for a diverse group of users with a shared purpose. As a result, a framework has been proposed to update the current applications or design and develop future mobile health applications. The framework has been developed based on the rationale and evidence found in the literature

Bias in Estimating Multivariate and Univariate Diffusions

Published in Journal of Econometrics, 2011, https://doi.org/10.1016/j.jeconom.2010.12.006</p

The Exact Discretisation of CARMA Models with Applications in Finance

The problem of estimating a continuous time model using discretely observed data is common in empirical finance. This paper uses recently developed methods of deriving the exact discrete representation for a continuous time ARMA (autoregressive moving average) system of order p, q to consider three popular models in finance. Our results for two benchmark term structure models show that higher order ARMA processes provide a significantly better fit than standard Ornstein-Uhlenbeck processes. We then explore present value models linking stock prices and dividends in the presence of cointegration. Our methods enable us to take account of the fact that the two variables are observed in fundamentally different ways by explicitly modelling the data as mixed stock-flow type, which we then compare with the (more common, but incorrect) treatment of dividends as a stock variable