Adaptive Forecasting of Exchange Rates with Panel Data

This article investigates the statistical and economic implications of adaptive forecasting of exchange rates with panel data and alternative predictors. The candidate exchange rate predictors are drawn from (i) macroeconomic 'fundamentals', (ii) return/volatility of asset markets and (iii) cyclical and confidence indices. Exchange rate forecasts at various horizons are obtained from each of the potential predictors using single market, mean group and pooled estimates by means of rolling window and recursive forecasting schemes. The capabilities of single predictors and of adaptive techniques for combining the generated exchange rate forecasts are subsequently examined by means of statistical and economic performance measures. The forward premium and a predictor based on a Taylor rule yield the most promising forecasting results out of the macro 'fundamentals' considered. For recursive forecasting, confidence indices and volatility in-mean yield more accurate forecasts than most of the macro 'fundamentals'. Adaptive forecast combinations techniques improve forecasting precision and lead to better market timing than most single predictors at higher horizons.exchange rate forecasting; panel data; forecast combinations; market timing

How does stock market volatility react to oil shocks?

We study the impact of oil price shocks on the U.S. stock market volatility. We jointly analyze three different structural oil market shocks (i.e., aggregate demand, oil supply, and oil-specific demand shocks) and stock market volatility using a structural vector autoregressive model. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by unexpected changes in aggregate and oil-specific demand, whereas the impact of supply-side shocks is negligible

Market Fundamentals, Risk and the Canadian Property Cycle: Implications for Property Valuation and Investment Decisions

The dramatic decline in commercial property values in recent years has changed popular perception about real estate investment risk. This paper aims to generate new insights into real estate investment risk and its implications for real estate valuation. It shows that the risk premium on unsecuritized commercial real estate varies over time and is strongly related to general economic conditions. A vector autoregressive model developed to forecast real estate returns reveals that time variation in real estate risk is partly predictable, and thus can help us to forecast future movements in commercial property values. The analysis suggests that in periods surrounding major market movements, changes in commercial property prices are driven more by changes in expected (required) returns than by changes in current and expected future property income. Changing expected returns may reflect rational revisions of real estate investment risk, or alternatively investor psychology or sentiment.

Market conditions, default risk and credit spreads

This study empirically examine the impact of market conditions on credit spreads as motivated by recently developed structural credit risk models. Using credit default swap (CDS) spreads, we find that, in the time series, average credit spreads are decreasing in GDP growth rate, but increasing in GDP growth volatility. We document that credit spreads are lower when investor sentiment is high and when the systematic jump risk is low. In the cross section, we confirm that firm-level cash flow volatility raises credit spreads. More importantly, we demonstrate that the impact of market conditions on credit spreads is substantially affected by firm heterogeneity. During economic expansions, ceteris paribus, firms with high cash flow betas have lower credit spreads than those with low cash flow betas. This relation disappears during economic recessions, consistent with theoretical predictions. -- In diesem Arbeitspapier untersuchen wir empirisch, wie die gesamtwirtschaftlichen Bedingungen die Renditeabstände von Unternehmensanleihen, die mit einem Ausfallrisiko behaftet sind, beeinflussen. Dabei verwenden wir Spreads von Kreditausfallswaps (Credit Default Swap, CDS) als Näherungswert für Kreditspreads und stellen fest, dass die durchschnittlichen Kreditspreads im Zeitverlauf bei wirtschaftlicher Expansion niedriger und bei wirtschaftlicher Rezession höher sind. Wenn das Wirtschaftswachstum volatiler ist, führt dies ebenfalls zu höheren Kreditspreads. Wir stellen fest, dass Kreditspreads bei positiver Anlegerstimmung und geringem Risiko eines marktweiten Sprungs niedriger ausfallen. Firmenübergreifend stellen wir fest, dass ein auf Unternehmensebene volatiler Cashflow zu einer Erhöhung der Kreditspreads führt. Was noch entscheidender ist, wir zeigen, dass in Zeiten wirtschaftlicher Expansion ? bei ansonsten gleichen Bedingungen ? Unternehmen, deren Cashflow stark mit dem gesamtwirtschaftlichen Wachstum korreliert, geringere Kreditspreads aufweisen als solche mit einer schwachen Cashflow-Korrelation. Im Einklang mit den theoretischen Voraussagen verschwindet dieser Zusammenhang in Zeiten wirtschaftlicher Rezession.Credit Risk,Credit Default Swaps,Credit Spreads,Market Conditions

Expert judgement in the Processes of Commercial Property Market Forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

How does investor sentiment affect stock market crises? Evidence from panel data

We test the impact of investor sentiment on a panel of international stock markets. Specifically, we examine the influence of investor sentiment on the probability of stock market crises. We find that investor sentiment increases the probability of occurrence of stock market crises within a one-year horizon. The impact of investor sentiment on stock markets is more pronounced in countries that are culturally more prone to herd-like behavior and overreaction or in countries with low institutional involvement. Results also suggest that investors' sentiment is not a reliable predictor of stock market reversal pointsInvestor sentiment ; stock market crises ; reversal points

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility

Macroeconomic modeling when agents are imperfectly informed

DSGE-models have become important tools of analysis not only in academia but increasingly in the board rooms of central banks. The success of these models has much to do with the coherence of the intellectual framework it provides. The limitations of these models come from the fact that they make very strong assumptions about the cognitive abilities of agents in understanding the underlying model. In this paper we relax this strong assumption. We develop a stylized DSGEmodel in which individuals use simple rules of thumb (heuristics) to forecast the future inflation and output gap. We compare this model with the rational expectations version of the same underlying model. We find that the dynamics predicted by the heuristic model differs from the rational expectations version in some important respects, in particular in their capacity to produce endogenous economic cycles.DSGE-model, imperfect information, heuristics, animal spirits