Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model.

This paper presents a new multivariate GARCH model with time-varying conditional correlation structure which is a generalization of the Regime Switching Dynamic Correlation (RSDC) of Pelletier (2006). This model, which we name Hierarchical RSDC, is building with the hierarchical generalization of the hidden Markov model introduced by Fine et al. (1998). This can be viewed graphically as a tree-structure with different types of states. The first are called production states and they can emit observations, as in the classical Markov-Switching approach. The second are called abstract states. They can't emit observations but establish vertical and horizontal probabilities that define the dynamic of the hidden hierarchical structure. The main gain of this approach compared to the classical Markov-Switching model is to increase the granularity of the regimes. Our model is also compared to the new Double Smooth Transition Conditional Correlation GARCH model (DSTCC), a STAR approach for dynamic correlations proposed by Silvennoinen and Teräsvirta (2007). The reason is that under certain assumptions, the DSTCC and our model represent two classical competing approaches to modeling regime switching. We also perform Monte-Carlo simulations and we apply the model to two empirical applications studying the conditional correlations of selected stock returns. Results show that the Hierarchical RSDC provides a good measure of the correlations and also has an interesting explanatory power.Multivariate GARCH; Dynamic correlations; Regime switching; Markov chain; Hidden Markov models; Hierarchical Hidden Markov models

Extreme Value Theory and Value at Risk : Application to Oil Market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. As a result oil markets are naturally vulnerable to significant negative volatility. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we apply both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, historical simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the parametric methods. Furthermore, GARCH(1, 1)-t model may provide equally good results, as well as the combining of the two procedures.Extreme Value Theory, Value at Risk, oil price volatility, GARCH, Historical Simulation, Filtered Historical Simulation.

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.Gravity Equation; Flexible Least Squares; Geographical Distance

On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree

This study examines the volatility and correlation and their relationships among the euro/US dollar exchange rates, the S&P500 equity indices, and the prices of WTI crude oil and the precious metals (gold, silver, and platinum) over the period 2005 to 2012. Our model links the univariate volatilities with the correlations via a hidden stochastic decision tree. The ensuing Hidden Markov Decision Tree (HMDT) model is in fact an extension of the Hidden Markov Model (HMM) introduced by Jordan et al. (1997). The architecture of this model is the opposite that of the classical deterministic approach based on a binary decision tree and, it allows a probabilistic vision of the relationship between univariate volatility and correlation. Our results are categorized into three groups, namely (1) exchange rates and oil, (2) S&P500 indices, and (3) precious metals. A switching dynamics is seen to characterize the volatilities, while, in the case of the correlations, the series switch from one regime to another, this movement touching a peak during the period of the Subprime crisis in the US, and again during the days following the Tohoku earthquake in Japan. Our findings show that the relationships between volatility and correlation are dependent upon the nature of the series considered, sometimes corresponding to those found in econometric studies, according to which correlation increases in bear markets, at other times differing from them

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

International audienceIn this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.Gravity Equation; Flexible Least Squares; Geographical Distance

Extreme Value Theory and Value at Risk : Application to Oil Market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. As a result oil markets are naturally vulnerable to significant negative volatility. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we apply both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, historical simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the parametric methods. Furthermore, GARCH(1, 1)-t model may provide equally good results, as well as the combining of the two procedures

Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model (version révisée)

22 pagesThis paper presents a new multivariate GARCH model with time-varying conditional correlation structure, which is a special case of the Regime Switching Dynamic Correlation (RSDC) of Pelletier (2006). This model which we have named Hierarchical RSDC (HRSDC), has been built with the hierarchical generalization of the hidden Markov model introduced by Fine et al. (1998). This can be viewed graphically as a tree-structure with different types of states. The former are called production states, and they can emit observations, as in the class of Markov-Switching approach. The latter are called "abstract" states. They can't emit observations but establish vertical and horizontal probabilities that define the dynamic of the hidden hierarchical structure. The main advantage of this approach, comparable to the classical Markov-Switching model, is that it improves the granularity of the regimes. Our model is also comparable to the new Double Smooth Transition Conditional Correlation GARCH model (DSTCC), a STAR approach for dynamic correlations proposed by Silvennoinen and Terasvirta (2007). The reason is that, under certain assumptions, the DSTCC and our model represent two classical competing approaches to modeling regime switching. We performed, Monte-Carlo simulations, and we applied the model to two empirical applications in studying the conditional correlations of selected stock returns. Results show that the HRSDC provides a good measure of the correlations, and possesses an interesting explanatory power

Working Papers / Documents de travail Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model Energy Markets and CO 2 emissions: analysis by stochastic copula autoregressive model

Abstract We examine the dependence between the volatility of the prices of the carbon dioxide "CO 2 " emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the Stochastic Autoregressive Copulas (SCAR), which is a time varying copula that was first introduced by Hafner and Manner (2012) The main result suggests that the dynamics of the dependence between the volatility of the CO 2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils

Federal minimum wage hikes do reduce teenage employment. A replication study of Bazen & Marimoutou (Oxford Bulletin of Economics and Statistics, 2002)

Corresponding publicationsWorking Paper | Federal Minimum Wage Hikes Do Reduce Teenage Employment International audienceIn 2002 we published a paper in which we used state space time series methods to analyse the teenage employment-federal minimum wage relationship in the US (Bazen and Marimoutou, 2002). The study used quarterly data for the 46 year period running from 1954 to 1999. We detected a small, negative but statistically significant effect of the federal minimum wage on teenage employment, at a time when some studies were casting doubt on the existence of such an effect. In this note we re-estimate the original model with a further 16 years of data (up to 2015). We find that the model satisfactorily tracks the path of the teenage employment-population ratio over this 60 year period, and yields a consistently negative and statistically significant effect of minimum wages on teenage employment. The conclusion reached is the same as in the original paper, and the elasticity estimates very similar: federal minimum wage hikes lead to a reduction in teenage employment with a short run elasticity of around – 0.13. The estimated long run elasticity of between – 0.37 and – 0.47 is less stable, but is nevertheless negative and statistically significant