Currency Forecast Errors at Times of Low Interest Rates: Evidence from Survey Data on the Yen/Dollar Exchange Rate

Using survey expectations data and Markov-switching models, this paper evaluates the characteristics and evolution of investors' forecast errors about the yen/dollar exchange rate. Since our model is derived from the uncovered interest rate parity (UIRP) condition and our data cover a period of low interest rates, this study is also related to the forward premium puzzle and the currency carry trade strategy. We obtain the following results. First, with the same forecast horizon, exchange rate forecasts are homogeneous among different industry types, but within the same industry, exchange rate forecasts differ if the forecast time horizon is different. In particular, investors tend to undervalue the future exchange rate for long term forecast horizons; however, in the short run they tend to overvalue the future exchange rate. Second, while forecast errors are found to be partly driven by interest rate spreads, evidence against the UIRP is provided regardless of the forecasting time horizon; the forward premium puzzle becomes more significant in shorter term forecasting errors. Consistent with this finding, our coefficients on interest rate spreads provide indirect evidence of the yen carry trade over only a short term forecast horizon. Furthermore, the carry trade seems to be active when there is a clear indication that the interest rate will be low in the future.

Exchange Rate Predictability in a Changing World

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time Varying Parameters (TVP) estimated by Bayesian methods. In core out-of-sample results, we improve upon a random walk benchmark for at least half, and for as many as eight out of ten, of the currencies considered. This contrasts with a constant parameter Taylor rule model that yields a more limited improvement upon the benchmark. In further results, Purchasing Power Parity and Uncovered Interest Rate Parity TVP models beat a random walk benchmark, implying our methods have some generality in exchange rate prediction.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula

We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric dependence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back-testing, e find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.

Artificial Snow Optimization in Winter Sport Destinations Using a Multi-agent Simulation

This paper presents the Juste-Neige system for predicting the snow height on the ski runs of a resort using a multi-agent simulation software. Its aim is to facilitate snow cover management in order to i) reduce the production cost of artificial snow and to improve the profit margin for the companies managing the ski resorts; and ii) to reduce the water and energy consumption, and thus to reduce the environmental impact, by producing only the snow needed for a good skiing experience. The software provides maps with the predicted snow heights for up to 13 days. On these maps, the areas most exposed to snow erosion are highlighted. The software proceeds in three steps: i) interpolation of snow height measurements with a neural network; ii) local meteorological forecasts for every ski resort; iii) simulation of the impact caused by skiers using a multi-agent system. The software has been evaluated in the Swiss ski resort of Verbier and provides useful predictions.

Making Chronic Conditions Count: 5. Chronic Airflow Obstruction

Chronic conditions are responsible for a significant proportion of early deaths. They reduce qualityof life in many of the adults living with them, represent substantial financial costs to patients andthe health and social care system, and cause a significant loss of productivity to the economy.This report contains estimates and forecasts of the population prevalence of chronic airflowobstruction, and it shows how it varies across the island and what change is expected between2007, 2015 and 2020.

Making Chronic Conditions Count: 1. Diabetes

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of diabetes, and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 2. Coronary Heart Disease (Angina and Heart Attack)

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of coronary heart disease (angina and heart attack), and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 3. Hypertension

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of hypertension and shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 4. Stroke

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of stroke, and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Chronic Airflow Obstruction Briefing

The Chronic Conditions Hub is a website that brings together information on chronic health conditions. It allows you to easily access, manage and share relevant information resources. The Chronic Conditions Hub includes the Institute of Public Health in Ireland’s (IPH) estimates and forecasts of the number of people living with chronic conditions. On the Chronic Conditions Hub you will find: - A Briefing for each condition - Detailed technical documentation - Detailed national and sub-national data that can be downloaded or explored using online data tools - A prevalence tool that allows you to calculate prevalence figures for your population data Chronic airflow obstruction (CAO) is a chronic lung condition that interferes with normal breathing. For the purpose of this briefing, CAO includes chronic obstructive pulmonary disease (COPD), chronic bronchitis and emphysema. CAO is responsible for a substantial amount of early deaths, reduced quality of life and significant costs to the health and social care system and to the economy. The World Health Organization estimates that COPD is the fourth leading cause of death worldwide and predicts that it will soon become the third leading cause of death.

Chronic Airflow Obstruction Briefing: Technical Documentation

Chronic airflow obstruction (CAO) is a chronic lung condition that interferes with normal breathing. CAO includes chronic obstructive pulmonary disease (COPD), chronic bronchitis and emphysema. IPH has systematically estimated and forecast the prevalence of CAO on the island of Ireland. This document details the methods used to calculate these estimates and forecasts.

Diabetes Briefing

Diabetes mellitus is a group of metabolic disorders characterised by too much glucose in the blood. The body breaks down digested food into a sugar called glucose from which it derives energy. The hormone insulin allows the body to use that sugar by helping glucose to enter the cells. When a person has diabetes, either the pancreas fails to produce enough insulin or the body cannot properly use the insulin it has. As a result there is a build-up of glucose in the blood causing the cells to be starved of energy. The Chronic Conditions Hub is a website that brings together information on chronic health conditions. It allows you to easily access, manage and share relevant information resources. The Chronic Conditions Hub includes the Institute of Public Health in Ireland’s (IPH) estimates and forecasts of the number of people living with chronic conditions. On the Chronic Conditions Hub you will find:- A Briefing for each condition - Detailed technical documentation - Detailed national and sub-national data that can be downloaded or explored using online data tools

Coronary Heart Disease Briefing

Coronary heart disease (CHD) is a collective term for diseases that occur when the walls of the coronary arteries become narrowed by a gradual build up of fatty material called atheroma. For the purpose of this briefing, CHD includes heart attack and angina (chest pain on exertion or stress). The Chronic Conditions Hub is a website that brings together information on chronic health conditions. It allows you to easily access, manage and share relevant information resources. The Chronic Conditions Hub includes the Institute of Public Health in Ireland’s (IPH) estimates and forecasts of the number of people living with chronic conditions. On the Chronic Conditions Hub you will find: - A Briefing for each condition - Detailed technical documentation - Detailed national and sub-national data that can be downloaded or explored using online data tools - A prevalence tool that allows you to calculate prevalence figures for your population data