Deviations from Uncovered Interest Rate Parity: A Post Keynesian Explanation

Finding satisfactory explanations of deviations from uncovered interest rate parity (UIRP) has proved to be a frustrating experience for Neoclassical economists. Studies have focused on the role of risk, but thus far no one has been able to put forward a source thereof that can account for the specific pattern of deviations from UIRP. This paper offers an alternative perspective that finally resolves the mystery. Drawing on the work of Marc Lavoie and John Smithin and extending it with some basic Post Keynesian propositions regarding endogenous money, uncertainty, and nonergodicity, it is shown that one can devise a comprehensive explanation of UIRP--an explanation that shows that much more than risk is responsible for deviations. In particular, it is argued that Keynes's "confidence" is a vitally important and overlooked factor. This contention is supported by a regression analysis of the U.S.-German and U.S.-Japanese asset markets.confidence, exchange rate, interest rate parity, risk

Dynamic asset (and liability) management under market and credit risk

We introduce a modelling paradigm which integrates credit risk and market risk in describing the random dynamical behaviour of the underlying fixed income assets. We then consider an asset and liability management (ALM) problem and develop a mul- tistage stochastic programming model which focuses on optimum risk decisions. These models exploit the dynamical multiperiod structure of credit risk and provide insight into the corrective recourse decisions whereby issues such as the timing risk of default is appropriately taken into consideration. We also present a index tracking model in which risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the index. Both in- and out-of-sample (backtesting) experiments are undertaken to validate our approach. In this way we are able to demonstrate the feasibility and flexibility of the chosen framework

A Precise Determination of αs\alpha_s from the C-parameter Distribution

We present a global fit for $\alpha_s(m_Z)$, analyzing the available C-parameter data measured at center-of-mass energies between $Q=35$ and $207$ GeV. The experimental data is compared to a N$^3$LL$^\prime$ + $\mathcal{O}(\alpha_s^3)$ + $\Omega_1$ theoretical prediction (up to the missing 4-loop cusp anomalous dimension), which includes power corrections coming from a field theoretical nonperturbative soft function. The dominant hadronic parameter is its first moment $\Omega_1$, which is defined in a scheme which eliminates the $\mathcal{O}(\Lambda_{\rm QCD})$ renormalon ambiguity. The resummation region plays a dominant role in the C-parameter spectrum, and in this region a fit for $\alpha_s(m_Z)$ and $\Omega_1$ is sufficient. We find $\alpha_s(m_Z)=0.1123\pm 0.0015$ and $\Omega_1=0.421\pm 0.063\,{\rm GeV}$ with $\chi^2/\rm{dof}=0.988$ for $404$ bins of data. These results agree with the prediction of universality for $\Omega_1$ between thrust and C-parameter within 1-$\sigma$.Comment: 24 pages, 19 figure

The Cost of Legal Restrictions on Experience Rating

We investigate the cost of legal restrictions on experience rating in auto and home insurance. The cost is an opportunity cost as experience rating can mitigate the problems associated with unobserved heterogeneity in claim risk, including mispriced coverage and resulting demand distortions. We assess this cost through a counterfactual analysis in which we explore how risk predictions, premiums, and demand in home insurance and two lines of auto insurance would respond to unrestricted multiline experience rating. Using claims data from a large sample of households, we first estimate the variance-covariance matrix of unobserved heterogeneity in claim risk. We then show that conditioning on claims experience leads to material refinements of predicted claim rates. Lastly, we assess how the households’ demand for coverage would respond to multiline experience rating. We find that the demand response would be large

Variable grouping in multivariate time series via correlation

The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho

Private investment in developing countries: The effects of commodity shocks and uncertainty

The link between ex post discrete shocks and private investment have never been formally tested in a panel data context, while the evidence of a link between ex ante commodity price uncertainty and investment is weak. This paper constructs measures of discrete shocks and uncertainty using a new multi-country data set of aggregate commodity price indices, and tests the relationship between various manifestations of commodity price variability and private investment rates within the context of a canonical empirical investment model estimated on a sample of 44 developing countries. The analysis confirms theoretical predictions that positive ex post commodity price shocks have strong positive effects on private investment rates in low income developing countries, conditional upon the level of commodity prices. It is also shown that the prospect of uncertain future commodity prices andex post negative shocks do not affect private investment rates.

Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses