Forecasting metal prices: Do forecasters herd?

We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters that foster incentives to scatter forecasts around a consensus forecast.     

Oil price dynamics,macro-finance interactions and the role of financial speculation

What is the role of financial speculation in determining the real oil price? We find that while macroeconomic shocks have been the main real oil price upward driver since mid-1980s, financial shocks have sizably contributed since early 2000s as well, and at a much larger extent since mid-2000s. Even though financial shocks contribute 44% out of the 65% real oil price increase over the period 2004–2010, the third oil price shock is a macro-finance episode: macroeconomic shocks actually largely account for the 2007–2008 oil price swing. While we then find support to the demand side view of real oil price determination, we however also find a much larger role for financial shocks than previously noted in the literature.     

Real exchange rate adjustment in European transition countries

In single-equation tests, real exchange rates show mean reversion for nine of 10 Central and Eastern European transition countries for the period January 1993 to December 2005. Because of the shift from controlled to market economies and accompanying crises, failed policy regimes and changes in exchange rate regimes, unit root tests for transition countries often require allowance for structural changes. Accounting for structural breaks gives substantially faster mean-reversion speeds than those found for major industrialized countries. These fast adjustment speeds are plausible: Transition countries had perhaps 10 years to make unprecedented adjustments required for accession to the European Union. A number of papers have applied non-linear models to the Central and Eastern European countries. This paper investigates four non-linear models and compares them with piece-wise linear break models. The break models appear superior in detecting mean reversion for the Central and Eastern European transition countries.     

Impact of macro-economic surprises on carry trade activity

Can official news and policy announcements affect foreign exchange speculation? A widespread speculative strategy in foreign exchange markets is carry trade. This paper explores the links between macro-economic news and foreign exchange options to identify macro-economic fundamentals most relevant to the pricing of downside risk – measured by risk reversals options contracts – to carry trade activity. Focusing primarily on the Japanese yen carry trade, we identify a significant impact of macro-economic surprises on dollar/yen risk reversals. The effect is sizeable, with news related to bilateral trade balance of particular concern. Moreover, there is a close link between risk reversals and speculative futures positions in Japanese yen. This allows us to quantify a substantial effect of macro-economic news on carry trade activity, with the cost of hedging as the transmission mechanism.     

A revisit to the dependence structure between the stock and foreign exchange markets: A dependence-switching copula approach

This paper develops a dependence-switching copula model to examine dependence and tail dependence for four different market statuses, namely, rising-stocks/appreciating-currency, falling-stocks/depreciating-currency, rising-stocks/depreciating-currency, and falling-stocks/appreciating-currency. The model is then applied to daily stock returns and exchange rate changes for six major industrial countries over the 1990–2010 period. The dependence and tail dependence among the above four market statuses are asymmetric for most countries in the negative correlation regime, but symmetric in the positive correlation regime. These results enrich the findings in the existing literature and suggest that analyzing cross-market linkages within a time-invariant copula framework may not be appropriate.     

Estimating the degree of operating efficiency gains from a potential bank merger and acquisition: A DEA bootstrapped approach

We propose a bootstrapped Data Envelopment Analysis (DEA)-based procedure to pre-calculate and pre-evaluate the short-run operating efficiency gains of a potential bank merger or acquisition (M&A). As an illustrative example, we apply our proposed procedure to investigate the degree of operating efficiency gains of 45 possible bank M&As in the Greek banking industry over the period from 2007 to 2011. The results reveal that a year before and a year after the initiation of the Greek fiscal crisis, the majority of the potential bank M&As under examination were unable to generate short-run operating efficiency gains. In addition, our results for 2011 indicate that the majority of bank M&As can lead to short-run operating efficiency gains. Finally, the empirical findings support the view that a merger or acquisition between efficient banks does not ensure an efficient bank M&A.     

Nonparametric correlation models for portfolio allocation

This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis.     

Nonlinear portfolio selection using approximate parametric Value-at-Risk

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the nonlinear portfolio selection formulation using VaR risk measure is in general a computationally intractable optimization problem. We investigate in this paper nonlinear portfolio selection models using approximate parametric Value-at-Risk. More specifically, we use first-order and second-order approximations of VaR for constructing portfolio selection models, and show that the portfolio selection models based on Delta-only, Delta–Gamma-normal and worst-case Delta–Gamma VaR approximations can be reformulated as second-order cone programs, which are polynomially solvable using interior-point methods. Our simulation and empirical results suggest that the model using Delta–Gamma-normal VaR approximation performs the best in terms of a balance between approximation accuracy and computational efficiency.     

A statistical model of speculative bubbles,with applications to the stock markets of the United States,Japan, and China

It is common knowledge that the more prices deviate from fundamentals, the more likely it is for prices to reverse. Taking this into account, we propose a simple statistical model to identify speculative bubbles in financial markets. Through the estimates of the time varying parameters, including transition probabilities, we can identify when and how newly born bubbles grow and burst over time. The model can be estimated by recursive computations, which require a huge storage capacity for standard computers. For this reason, we introduce an approximation in the computation, maintaining the recursive nature of our estimation technique. We then apply this model to the stock markets of the United States, Japan, and China, estimate its parameters and the probabilities of a bubble crash, and obtain several interesting results: the time series data of the stock price bubble show an inherently non-stationary development and the probability of a bubble crash indeed increases as the stock price becomes too high or too low.     

Emerging markets and heavy tails

Emerging countries are held to be subject to more frequent and more pronounced external and internal shocks than their developed counter-parts. This suggests that key variables pertaining to their markets, including their exchange rates, will be marked by greater likelihood of extreme observations and large fluctuations. We focus on the hypothesis that compared to developed country exchange rates, emerging country exchange rates will be more pronouncedly heavy-tailed. We find support for the hypothesis using recently proposed robust tail index estimation methods which, in particular, perform well under heavy-tailed dependent GARCH processes that are often used for modeling exchange rates. According to the estimation results reported in the paper, variances may be infinite for several emerging country exchange rates. Tail index values ζ = p ∈ (2.6, 2.8) appear to be at the dividing boundary between the two sets of countries: while the moments of order p ∈ (2.6, 2.8) are finite for most of the developed country exchange rates, they may be (or are) infinite for most of the emerging country exchange rates. We also study the impact of the on-going financial and economic crisis, and find that heavy-tailedness properties of most exchange rates did not change significantly with the onset of the crisis. At the same time, some foreign exchange markets have experienced structural changes in their heavy-tailedness properties during the crisis.