Optimal strategies under worst-case scenarios have been studied in Bernard et al. [2013a]. Bernard et al. utilize copulas to construct cost-efficient strategies with a predefined dependence structure in the tail between the payoff and the market. In their study they show that such strategies with state-dependent copula constraints dominate traditional diversification strategies in terms of the provided protection in the states of market downturns. We derive similar strategies, however using correlation constraints instead of copula constraints in the tail. We found that for an investor seeking negative dependence with the market, it is cheaper to construct a strategy with conditional correlation constraint in the tail. However, the constructed strategies with conditional correlation constraints do not provide sufficient protection in bad states of the economy. Therefore, when analyzing a strategy, negative correlation with the market in the tail is not a sufficient indicator for the protection level in the event of a market crisis.

We investigate the interdependence among three CEE stock markets and be- tween CEEs vis-à-vis euro area, using daily data from 2001-2011. Initially, we estimate bivariate ADCC models. Then, OLS regressions are employed to understand the evolution of correlations in time and during the recent financial crises. Finally, we examine the relationship between correlations and volatilities using the simple OLS model and the rolling stepwise regression methodology. Our results indicate that 3 out of 4 series exhibit asymmetries in conditional variances, while only 1 pair out of 6 exhibit asymmetries in correlations. We found that correlations are increased over time and during the recent financial crises for both pairs (CEEs-CEEs and CEEs-eurozone). However, the highest increase is observed for CEEs-eurozone. Mainly, we found a positive rela- tionship between correlations and volatilities, even though this relationship is niether constant in time nor strictly positive or negative during all the sample period, but rather time-varying with periods of being higher or lower than zero.

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model.

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Using the dynamic conditional correlation (DCC) model due to Engle (2002), we estimate time varying correlations of quarterly real GDP growth among the G7 countries. In general, we find that rather heterogeneous patterns of international synchronization exist during US recessions. During the 2007-2009 recession, however, international co-movement increased substantially. (authors' abstract)

The first part of this dissertation examines the presence of the financial contagion across European stock markets with respect to the Greece sovereign debt crisis by estimating the time-varying conditional correlations of stock returns between Greece and other European countries over 2001 to 2012. We find that the correlations vary over time and reach the peaks in the late 2008 during theU.S.subprime crisis, and in the beginning of 2010 of the height of European debt crisis. Further, the correlations between stock index returns of Greece and Spain, France, Ireland, Netherlands are significantly increased by Greek sovereign credit rating downgrade announcements. The second part of this dissertation examines the correlations of gold, dollar and U.S. stock returns over 2001 to 2012 using ADCC-GARCH model. The conditional correlations of gold-dollar returns are negative during all sub-sample periods and significantly increase in magnitude during both subprime crisis and sovereign debt crisis. The conditional correlations of gold-stock returns are positive on average over time. However, gold-stock correlation falls below zero during subprime crisis and sovereign debt crisis. Gold-stock correlation is significantly negatively affected by positive CPI announcements. And gold-dollar correlation is significantly negatively affected by negative GDP announcements and positive unemployment announcements. The effects of macroeconomic announcements are stronger during economic recessions.

Since the introduction univariate GARCH models number of available models have grown rapidly and has been extended to the multivariate area. This paper compares three different multivariate GARCH models and they are evaluated using out of sample Value at Risk of dif- ferent portfolios. Sector portfolios are used with different market capitalization. The models compared are the DCC,CCC and the GO-Garch model. The forecast horizon is 1-day, 5-day and 10-day ahead forecast of the estimated VaR limit. The DCC performs best with regards to both conditional anc unconditional violations of the VaR estimates.

[pt] Em Fleming, Kirby e Ostdiek (2001), encontram-se evidências de que a utilização de modelos de previsão da volatilidade, possui valor econômico significante quando se compara simplesmente com a matriz de variância incondicional, num arcabouço de otimização de portfólio. Indo além, este trabalho propõem averiguar se os modelos mais complexos de Correlação Condicional Constante (CCC) e Dinâmica (DCC) sugeridos respectivamente por Bollerslev (1990) e Engle (2002) podem oferecer melhores resultados. Os resultados encontrados são dependentes da preferência do investidor. Um investidor mais avesso ao risco, terá maior utilidade ao empregar o modelo DCC e CCC quando comparado ao simples modelo da média móvel com decaimento exponencial, popularizados por RiskMetrics. Isso ocorre porque os modelos DCC e CCC apresentam desvio padrão e retorno geralmente inferiores. Ainda, não é possível afirmar como em Fleming, Kirby e Ostdiek (2001) que a utilização de modelos de previsão da volatilidade, possui valor econômico significante. / [en] At Fleming, Kirby e Ostdiek (2001), evidences are found that volatility timming models, have signicant economic value when comparing with the simple unconditional variance matrix, in a framework of portfolio optimization. Going further, this work analyze if the more complex Constant (CCC) and Dynamic (DCC) Conditional Corrrelation models, suggested respectivily by Bollerslev (1990) and Engle (2002) can have a higher performance. The results found depend on the investor´s preference. A more risk averse investor has a higher utility level employing the DCC and CCC models when comparing with the simple exponencial moving avarage model, popularized by RiskMetrics. This happens because the DCC and CCC models usually have smaller standard deviation and return. Futhermore, it is not possible to assert, like at Fleming, Kirby e Ostdiek (2001), that volatility timming models have higher economic value.

Neste trabalho realizamos um amplo estudo de simulação com o objetivo principal de avaliar o desempenho de carteiras de mínima variância global construídas com base em modelos de previsão da volatilidade que utilizam dados de alta frequência (em comparação a dados diários). O estudo é baseado em um abrangente conjunto de dados financeiros, compreendendo 41 ações listadas na BM&FBOVESPA entre 2009 e 2017. Nós avaliamos modelos de previsão de volatilidade que são inspirados na literatura ARCH, mas que também incluem medidas realizadas. Eles são os modelos GARCH-X, HEAVY e Realized GARCH. Seu desempenho é comparado com o de carteiras construídas com base na matriz de covariância amostral, métodos de encolhimento e DCC-GARCH, bem como com a carteira igualmente ponderada e o índice Ibovespa. Uma vez que a natureza do trabalho é multivariada, e a fim de possibilitar a estimação de matrizes de covariância de grandes dimensões, recorremos à especificação DCC. Utilizamos três frequências de rebalanceamento (diária, semanal e mensal) e quatro conjuntos diferentes de restrições sobre os pesos das carteiras. A avaliação de desempenho baseia-se em medidas econômicas tais como retornos anualizados, volatilidade anualizada, razão de Sharpe, máximo drawdown, Valor em Risco, Valor em Risco condicional e turnover. Como conclusão, para o nosso conjunto de dados o uso de retornos intradiários (amostrados a cada 5 e 10 minutos) não melhora o desempenho das carteiras de mínima variância global. / In this work we perform an extensive backtesting study targeting as a main goal to assess the performance of global minimum variance (GMV) portfolios built on volatility forecasting models that make use of high frequency (compared to daily) data. The study is based on a broad intradaily financial dataset comprising 41 assets listed on the BM&FBOVESPA from 2009 to 2017. We evaluate volatility forecasting models that are inspired by the ARCH literature, but also include realized measures. They are the GARCH-X, the High-Frequency Based Volatility (HEAVY) and the Realized GARCH models. Their perfomances are benchmarked against portfolios built on the sample covariance matrix, covariance matrix shrinkage methods, DCC-GARCH as well as the naive (equally weighted) portfolio and the Ibovespa index. Since the nature of this work is multivariate and in order to make possible the estimation of large covariance matrices, we resort to the Dynamic Conditional Correlation (DCC) specification. We use three different rebalancing schemes (daily, weekly and monthly) and four different sets of constraints on portfolio weights. The performance assessment relies on economic measures such as annualized portfolio returns, annualized volatility, Sharpe ratio, maximum drawdown, Value at Risk, Expected Shortfall and turnover. We also account for transaction costs. As a conclusion, for our dataset the use of intradaily returns (sampled every 5 and 10 minutes) does not enhance the performance of GMV portfolios.

In this paper we examine the extent of international trade synchronization during periods of international trade collapses and US recessions. Using dynamic correlations based on monthly trade data for the G7 economies over the period 1961-2011, our results suggest rather idiosyncratic patterns of international trade synchronization during collapses of international trade and US recessions. During the great recession of 2007-2009, however, international trade experienced the most sudden, severe and globally synchronized collapse. (author's abstract)