Extreme Dependence in Asset Markets Around the Globe

The dependence between large stock returns is higher than the dependence between small to moderate stock returns. This is defined as extreme dependence, and it is particularly observed for large negative returns. Therefore, diversification gains calculated from the overall dependence will overestimate the true potential for diversification during turmoil periods. This thesis answers questions on how the dependence between large negative stock returns can appropriately be modelled. The main conclusions of this thesis read that extreme dependence is often present, can become rather strong, should not be ignored, and shows substantial time-variation. More specifically, extreme dependence shows up as contagion, with small local crashes evolving into more severe crashes. In addition, due to financial globalization, and emerging market liberalization in particular, extreme dependence between regional stock markets has substantially increased. Furthermore, extreme dependence can vary over time by becoming weaker or stronger, but it can also be subject to structural changes, such as a change from symmetric dependence to asymmetric dependence. Using return data at the highest possible level of detail, improves the accuracy of forecasting joint extreme negative returns. Finally, this thesis shows how different econometric techniques can be used for modelling extreme dependence. The use of copulas for financial data is relatively new, therefore a substantial part of this thesis is devoted to new copula models and applications. Other techniques used in this thesis are GARCH, regime-switching, and logit models

Contagion as Domino Effect in Global Stock Markets

This paper shows that stock market contagion operates through a domino effect, where small crashes evolve into more severe crashes. Using a novel unifying framework we model the occurrence of local, regional and global crashes in terms of past occurrences of these different crashes and financial variables. We find convincing evidence that global crashes do not occur abruptly but are preceded by local and regional crashes. Additionally, interest rates, bond returns and volatility affect the probabilities of different crash types, indicating interdependence. We show that in forecasting global crashes our model outperforms a binomial model for global crashes only

Time Variation in Asset Return Dependence: Strength or Structure?

The dependence between asset returns varies. Its strength can become stronger or weaker. Also, its structure can change, for example, when asymmetries related to bull and bear markets become more or less pronounced. To analyze these different types of variations, we develop a model that separately accommodates these changes. It combines a mixture of structurally different copulas with time variation. Our model shows both types of changes in the dependence between several equity market returns. Ignoring them leads to biases in risk measures. An underestimation of Value-at-Risk by maximum 15% occurs exactly when most harmful, during crisis periods

The Economic Value of Fundamental and Technical Information in Emerging Currency Markets

We measure the economic value of information derived from macroeconomic variables and from technical trading rules for emerging markets currency investments. Our analysis is based on a sample of 21 emerging markets with a floating exchange rate regime over the period 1997-2007 and explicitly accounts for trading restrictions on foreign capital movements by using non-deliverable forward data. We document that both types of information can be exploited to implement profitable trading strategies. In line with evidence from surveys of foreign exchange professionals concerning the use of fundamental and technical analysis, we find that combining the two types of information improves the risk-adjusted performance of the investment strategies

Forecasting Value-at-Risk under temporal and portfolio aggregation

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of 10 trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly, or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation with that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations, and the method of forecast construction. We find that the level of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modeling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution, and forecast choices are also smaller compared with temporal aggregation

Forecasting Value-at-Risk Under Temporal and Portfolio Aggregation

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregatio