Dating the Turning Points of Nordic Business Cycles.

When were the significant turning points in business activity in the Nordic countries during the last fourty years? How frequent, long, and sharp were the contractions? This paper provides answers to these questions by applying the Bry and Boschan (1971) algorithms, which have been used to analyze business cycle turns in several countries, in particular the United States. Applying the same methods for Nordic countries it is found that contractions were unusually long and frequent in Sweden, while expansions were unusually long in Finland and Norway. However, contractions were not necessarily sharper in Sweden when compared with the other Nordic countries. Surprisingly, not much evidence of a common Nordic cycle is found. It appears instead that Sweden and Denmark tend to mimic the downturns in the G-7 countries more closely than Finland and Norway.

The Importance of the Loss Function in Option Valuation

Which loss function should be used when estimating and evaluating option valuation models? Many different functions have been suggested, but no standard has emerged. We emphasize that consistency in the choice of loss functions is crucial. First, for any given model, the loss function used in parameter estimation and model evaluation should be the same, otherwise suboptimal parameter estimates may be obtained. Second, when comparing models, the estimation loss function should be identical across models, otherwise inappropriate comparisons will be made. We illustrate the importance of these issues in an application of the so-called Practitioner Black-Scholes model to S&P 500 index options. Quelle devrait être la fonction de perte utilisée pour l'estimation et l'évaluation des modèles de valorisation des options? Plusieurs fonctions ont été suggérées, mais aucune norme ne s'est imposée. Dans ce travail, nous ne proposons pas une fonction en particulier, mais nous soutenons que la cohérence dans le choix des fonctions est cruciale. Premièrement, pour n'importe quel modèle donné, la fonction de perte utilisée dans l'estimation des paramètres et dans l'évaluation du modèle devrait être la même, sinon on obtient des estimations de paramètres sous-optimaux. Deuxièmement, lors de la comparaison des modèles, la fonction de perte utilisée pour l'estimation devrait être la même pour chaque modèle, autrement les comparaisons sont injustes. Nous illustrons l'importance de ces questions dans une application du modèle appelé Black-Scholes du praticien (PBS) aux options de l'indice S&P500.implied volatility functions; valuation errors; out-of-sample forecasting; parameter stability, fonctions de volatilité implicite; évaluation des erreurs; prévision hors échantillon; stabilité des paramètres

The informational content of over-the-counter currency options

Financial decision makers often consider the information in currency option valuations when making assessments about future exchange rates. The purpose of this paper is to systematically assess the quality of option based volatility, interval and density forecasts. We use a unique dataset consisting of over 10 years of daily data on over-the-counter currency option prices. We find that the OTC implied volatilities explain a much larger share of the variation in realized volatility than previously found using market-traded options. Finally, we find that wide-range interval and density forecasts are often misspecified whereas narrow-range interval forecasts are well specified. JEL Classification: G13, G14, C22, C53Density, forecasting, FX, Interval, Volatility

Company Flexibility, the Value of Management and Managerial Compensation

The variation in managerial compensation across countries and industries for firms of similar size is staggering. We analyze this phenomenon in a continuous time model of the firm, where the economic environment evolves stochastically over time and where changes to the firm operations are costly. The underlying idea is that managers in different countries and industries are compensated very differently, not necessarily because their skills differ substantially, but rather because the scope for management to add value to the firms varies substantially. If adjustment costs are low, or if the economic environment is relatively volatile, then the potential value-added from active management is larger. The positive relationship between economic environment volatility and the value of management suggests a real options interpretation of the manager: Active management can be viewed as a real option to make changes in the production plan. Our model shows that the higher the volatility of the economy, the larger the value of this real option. La variation dans les compensations des gestionnaires à travers les pays et les industries pour des compagnies de taille comparable est ahurissante. Nous analysons ce phénomène dans le cadre d'un modèle de compagnie en temps continu, dans lequel l'environnement économique évolue de manière stochastique et les changements dans le fonctionnement de la compagnie sont coûteux. L'idée sous-jacente est que les gestionnaires dans différents pays et industries sont rémunérés de manière très différente non pas parce que leurs compétences diffèrent de manière substantielle mais plutôt parce que la valeur ajoutée par la gestion diffère substantiellement. Si les coûts d'ajustement sont peu élevés ou si l'environnement économique est relativement volatile, alors le potentiel de valeur ajoutée par la gestion active est important. La relation positive entre la volatilité de l'environnement économique et la valeur de la gestion suggère une interprétation du gestionnaire en termes d'option réelle : la gestion active peut être vue comme une option réelle sur un changement dans le plan de production. Notre modèle montre que plus l'économie est volatile, plus l'option a de valeur.CEO Pay Variation, Production Adjustment Costs, Input Price Volatility, Real Options, Variation des rémunérations des directeurs d'entreprises, coûts d'ajustement de production, volatilité des prix des facteurs de production, options réelles

The Importance of the Loss Function in Option Pricing

Which loss function should be used when estimating and evaluating option pricing models? Many different fucntions have been suggested, but no standard has emerged. We do not promote a partidular function, but instead emphasize that consistency in the choice of loss functions is crucial. First, for any given model, the loss function used in parameter estimation and model evaluation should be identical, otherwise suboptimal parameter estimates will be obtained. Second, when comparing models, the estimation loss function should be identical across models, otherwise unfair comparisons will be made. We illustrate the importance of these issues in an application of the so-called Practitioner Black-Scholes (PBS) model to S&P500 index options. We find reductions of over 50 percent in the root mean squared error of the PBS model when the estimation and evaluation loss functions are aligned. We also find that the PBS model outperforms a benchmark structural model when the estimation loss functions are identical across models, but otherwise not. The new PBS model with aligned loss functions thus represents a much tougher benchmark against which future structural models can be compared. Quelle fonction de pertes devrait être utilisée pour l'estimation et l'évaluation des modèles d'évaluation d'options? Plusieurs fonctions différentes ont été suggérées,0501s aucune norme ne s'est imposée. Nous ne promouvons aucune fonction,0501s soutenons que la cohérence dans le choix des fonctions est cruciale. Premièrement, pour n'importe quel modèle donné, la fonction de pertes utilisée dans l'estimation des paramètres et dans l'évaluation du modèle devrait être la même, sinon on obtient des estimations de paramètres sous-optimales. Deuxièmement, lors de la comparaison de modèles, la fonction de pertes pour l'estimation devrait être la même pour chaque modèle, autrement les comparaisons sont injustes. Nous illustrons l'importance de ces questions dans une application du modèle appelé Black-Scholes du praticien (PBS) aux options de l'index S&P500. Nous trouvons des réductions de plus de 50 pourcent de la racine de l'erreur quadratique moyenne du modèle PBS lorsque les fonctions de pertes d'estimation et d'évaluation sont alignées. Nous trouvons également que le modèle PBS dépasse un modèle de benchmark structurel quand les fonctions de pertes d'estimation sont identiques pour tous les modèles,0501s pas dans les autres cas. Le nouveau modèle PBS à fonctions de pertes alignées représente dès lors un benchmark bien plus robuste auquel les futurs modèles structurels pourront être comparés.Option pricing, implied volatility, practitioner Black-Scholes approach, pricing errors, loss functions, out-of-sample forecasting, parameter stability, Évaluation des options, volatilité implicite, approche Black-Scholes du praticien, erreurs d'évaluation, fonctions de perte, prévisions hors-échantillon, stabilité des paramètres

Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

Financial Asset Returns, Market Timing, and Volatility Dynamics

We consider three sets of phenomena that feature prominently and separately in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs with implications for market timing, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (1) Volatility dependence produces sign dependence, so long as expected returns are nonzero. Hence one should expect sign dependence, given the overwhelming evidence of volatility dependence. (2) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence. In particular, sign dependence does not imply market inefficiency. (3) Sign dependence is not likely to be found via analysis of sign autocorrelations, because the nature of sign dependence is highly nonlinear. (4) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns. Instead, it is more likely to be found at intermediate return horizons. Nous considérons trois ensembles de phénomènes qui sont souvent - et séparément - discutés dans la littérature d'économie financière, à savoir la dépendance de la moyenne conditionnelle (ou l'absence de dépendance) dans les rendements d'actifs, la dépendance (et donc prévisibilité) des signes de rendements d'actifs ainsi que leurs implications dans le timing du marché, et la dépendance (et donc prévisibilité) dans les volatilités des rendements d'actifs. Nous montrons que ces phénomènes sont étroitement interreliés et nous explorons leurs relations en détail. Entre autres, nous montrons que : 1) la dépendance de la volatilité produit une dépendance du signe tant que les rendements attendus sont non nuls. On devrait par conséquent s attendre à une dépendance du signe, étant donné la présence notoire de dépendance de volatilité; 2) le résultat classique qui ne trouve que peu ou pas de dépendance de la moyenne conditionnelle est parfaitement compatible avec un degré significatif de dépendance de signe et de dépendance de volatilité. En particulier, la dépendance de signe n'implique pas une inefficacité du marché; 3) Il est peu probable qu'une analyse des autocorrélations de signes révèle une dépendance de signe, parce que la nature de la dépendance du signe est fortement non linéaire; 4) il est également peu probable que l'on retrouve une dépendance de signe dans des rendements à très haute fréquence (par exemple quotidiens) ou à très basse fréquence (par exemple annuels). Il est plus probable qu'on la trouve avec des horizons de rendements intermédiaires.Sign prediction, direction of change, volatility timing, investment horizon, prédiction des signes, direction de changement, timing de la volatilité, horizon d'investissement

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.Conditional Mean Dependence, Conditional Volatility Dependence, Sign Dependence, VIX

Stable Cu, Fe, and Ni Isotopic Systematics of the Sudbury Offset Dikes and Associated Rocks

The stable isotope ratio compositions of Fe, Ni, and Cu (δ56Fe, δ60Ni, and δ65Cu) are reported for the first time in rocks from the Sudbury Igneous Complex (SIC). Massive sulfide ores, Quartz Diorite (QD), Inclusion bearing Quartz Diorite (IQD), and rocks from members of the Main Mass of the SIC were analyzed. The objective was to better understand the origin(s) and source(s) of the Offset Dikes and the associated sulfide mineralization. Based on stable isotope ratios and petrographic observations, two distinct types of sulfide mineralization hosted within the Offset Dikes are identified. Massive sulfide mineralization hosted within the Offset Dikes was identified to be different than the disseminated blebby sulfide mineralization found within the QD and IQD, based on coordinated isotopic and petrographic analyses. Comparisons of the stable isotope ratios of Fe, Ni, and Cu between rock samples from different Offset Dikes established a homogeneity in Fe, Ni, and Cu compositions. Including between the QD and IQD, and their disseminated sulfides. A correlation between δ60Ni, and δ65Cu values, with lighter compositions for the massive sulfides compared to the residual main mass, indicates an early magmatic origin for the massive sulfides compared to the disseminated sulfides contained in the Offset Dikes

Testing, Comparing, and Combining Value at Risk Measures

Value-at-Risk (VaR) has emerged as the standard tool for measuring and reporting financial market risk. Currently, more than eighty commercial vendors offer enterprise or trading risk management systems which report VaR-like measures. Risk managers are therefore often left with the daunting task of having to choose from this plethora of risk measures. Accordingly, this paper develops a framework for answering the following questions about VaRs: 1) How can a risk manager test that the VaR measure at hand is properly specified, given the history of asset returns? 2) Given two different VaR measures, how can the risk manager compare the two and pick the best in a statistically meaningful way? Finally, 3) How can the risk manager combine two or more different VaR measures in order to obtain a single statistically superior measure? The usefulness of the methodology is illustrated in an application to daily returns on the S&P500. In the application, competing VaR measures are calculated from either historical or option-price based volatility measures, and the VaRs are then tested and compared.