Extraction of the atrial activity from the ECG based on independent component analysis with prior knowledge of the source kurtosis signs

In this work it will be shown that a contrast for independent component analysis based on prior knowledge of the source kurtosis signs (ica-sks) is able to extract atrial activity from the electrocardiogram when a constrained updating is introduced. A spectral concentration measure is used, only allowing signal pair updates when spectral concentration augments. This strategy proves to be valid for independent source extraction with priors on the spectral concentration. Moreover, the method is computationally attractive with a very low complexity compared to the recently proposed methods based on spatiotemporal extraction of the atrial fibrillation signal

The Nature and Determinants of Volatility in Agricultural Prices

The volatility of 19 agricultural commodity prices are examined at monthly and annual frequencies. All of the price series are found to exhibit persistent volatility (periods of relatively high and low volatility). There is also strong evidence of transmission of volatilities across prices. Volatility in oil prices is found to be a significant determinant of volatilities in the majority of series and, likewise, exchange rate volatility is found to be a predictor of volatility in over half the series. There is also strong evidence that stock levels and yields are influencing price volatility. Most series exhibit significant evidence of trends in their volatility. However, these are in a downward direction for some series and in an upward direction for other series. Thus, there is no general finding of long term increases in volatility across most agricultural pricesVolatility, Agricultural Prices

Using generic order moments for separation of dependent sources with linear conditional expectations

In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia21ª European Signal Processing ConferenceMarrakechMarruecosEuropean Signal Processing Society (EURASIP

Liberalized Markets Have More Stable Exchange Rates: Short-Run Evidence from Four Transition Countries

The paper looks at the hypothesis that financial-market liberalization can create a basis for more stable exchange rates, as deviations of exchange rates from equilibrium levels bring forth stabilizing flows of liquidity. This hypothesis suggests that opening up financial markets militates in favor of exchange-rate flexibility by increasing the viability of a floating regime as well as making it more difficult to maintain a peg. The paper examines this hypothesis in a sample of four transition economies and finds that exchange rates tend to return faster to their Hodrick-Prescott-based values where markets are liberalized. The results suggest that early and successful foreign-exchange liberalization pays off in terms of depth of the market and, hence, faster adjustment of the exchange rate to shocks. Moreover, it implies that central banks should not be overly concerned with short-run volatility of their national exchange rates.endogenous liquidity, error-correction mechanism, exchange rate, nonlinearity

Understanding Carry Trade Risks Using Bayesian Methods: A Comparison with Other Portfolio Risks from Currency, Commodity and Stock Markets

The purpose of this dissertation is to understand the risks embedded in Carry Trades. For this, we use a broad range of stochastic volatility (SV) models, estimate them using Bayesian techniques via Markov chain Monte Carlo methods, and analyze various risk measures using these estimation results. Many researchers have tried to explain the risk factors deriving Carry returns with standard risk models (factor models, Sharp ratios etc.). However, the high negative conditional skewness of Carry Trades hints the existence of jumps and shows that they have non normal returns, suggesting looking only at first two moments such as sharp ratios or using standard risk models are not enough to understand their risks. Therefore, we investigate Carry risks by delving into its SV and jump components and separate out their effects for a more thorough analysis. We also compare these results with other market portfolios (S&P 500, Fama HML, Momentum, Gold, AUD/USD, Euro/USD, USD/JPY, DXY, Long Rate Carry and Delta Short Rate Carry) to be able to judge the riskiness of Carry relative to other investment alternatives. We then introduce a new model diagnostic method, which overcomes the flaws of the previous methods used in the literature. This is important since model selection is a central question in SV literature, and although various methods were suggested earlier, they do not provide a reliable measure of fit. Using this new diagnostic method, we select the best-fitted SV model for each portfolio and use their estimation results to carry out the risk analysis. We find that the extremes of volatility, direct negative impact of volatilities on returns, percent of overall risk due to jumps considering both returns and vols, and negative skewness are all more pronounced for Carry Trades than for other portfolios. This shows that Carry risks are more complicated than other portfolios. Hence, we are able to remove a layer from the Carry risks by analyzing its jump and SV components in more depth. We also present the rolling correlations of these portfolio returns, vols, and jumps to understand if they co-move and how these co-movements change over time. We find that despite being dollar-neutral, Carry is still prone to dollar risk. DXY-S&P appear to be negatively correlated after 2003, when dollar becomes a safe-haven investment. S&P-AUD are very positively correlated since both are risky assets, except during currency specific events such as central bank interventions. MOM becomes negatively correlated with Carry during crisis and recovery periods since MOM yields positive returns in crisis and its returns plunge in recovery. Carry-Gold are mostly positively correlated, which might be used to form more enhanced trading and hedging strategies. Carry-S&P are mostly very positively correlated, and their jump probability correlations peak during big financial events. Delta Carry, on the other hand, distinguishes from other portfolios as a possible hedging instrument. It is not prominently correlated to any of the portfolios. These correlations motivate us to search for common factors deriving the 11 portfolios under consideration. We find through the Principal Component Analysis that there are four main components to explain their returns and two main components to explain their vols. Moreover, the first component in volatility is the common factor deriving all risky asset vols, explaining 75% of the total variance. To model this dynamic relationship between these portfolios, we estimate a multivariate normal Markov switching (MS) model using them. Then we develop a dynamic trading strategy, in which we use the MS model estimation results as input to the mean-variance optimization to find the optimal portfolio weights to invest in at each period. This trading strategy is able to dynamically diversify between the portfolios, and having a sharp ratio of 1.25, it performs much better than the input and benchmark portfolios. Finally, MS results indicate that Delta Carry has the lowest variance and positive expected return in both states of the MS model. This supports our findings from risk analysis that Delta Carry performs well during volatile periods, and vol elevations have a direct positive impact on its returns

Does Basel II Pillar 3 Risk Exposure Data help to Identify Risky Banks?

Basel II Pillar 3 reports provide information about banks' exposure towards a number of risk factors, such as corporate credit risk and interest rate risk. Previous studies nd that the quality of such information is likely to be weak. We analyze the marginal contribution of pillar 3 exposure data to the quality of equity volatility forecasts for individual banks. Our method uses (local in time) measures of risk factor risk using a multivariate stochastic volatility model for ve risk factors, and uses measures of bank sensitivity with respect to these risk factors. We use two sets of sensitivity measures. One takes into account pillar 3 information, and the other one does not. Generally, we generate volatility forecasts as if no market prices of equity were available for the bank the forecast is made for. We do this for banks for which such data is, in fact, available so that we can conduct ex post - tests of the quality of volatility forecasts. We nd that (1) pillar 3 information allows for a better-than-random ranking of banks according to their risk, but (2) pillar 3 exposure data does not help reduce volatility forecast error magnitude.Risk Reporting, Stochastic Volatility, Risk Factors

Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility

Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability

Using Classical Inference Methods to reveal individual-specific parameter estimates to avoid the potential complexities of WTP derived from population moments

nference estimation methods for logit models with Bayesian methods and suggested that the latter are more appealing on grounds of relative simplicity in estimation and in producing individual observation parameter estimates instead of population distributions. It is argued that one particularly appealing feature of the Bayesian approach is the ability to derive individual-specific willingness to pay measures that are claimed to be less problematic than the classical approaches in terms of extreme values and signs. This paper takes a close look at this claim by deriving both population derived WTP measures and individual-specific values based on the classical ‘mixed logit’ model. We show that the population approach may undervalue the willingness to pay substantially; however individual parameters derived using conditional distributions can be obtained from classical inference methods, offering the same posterior information associated with the Bayesian view. The technique is no more difficult to apply than the Bayesian approach – indeed the individual specific estimates are a by-product of the parameter estimation process. Our results suggest that while extreme values and unexpected signs cannot be ruled out (nor can they in the Bayesian framework), the overall superiority of the Bayesian method is overstated

The Cross-Sectional Determinants of Returns: Evidence from Emerging Markets' Stocks

This paper looks at the cross-section of stock returns for the particular case of emerging markets. For each of 21 emerging markets I investigate the role of a set of a priori specified factors in the cross-section of returns, and subsequently assess whether the important factors are common. I use data on emerging markets’ individual stocks from the Emerging Markets Data Base (IFC). My results indicate that the most important pricing factors are common to the emerging markets in my sample, and that these important factors are similar to those identified for mature markets. Among the top six factors are technical factors and price level attributes. The payoffs to these factors are not correlated suggesting that even if investors across markets elect similar factors to price assets, premia are local.International Asset Pricing; Emerging Markets