Detecting abrupt changes in the spectra of high-energy astrophysical sources

Variable-intensity astronomical sources are the result of complex and often extreme physical processes. Abrupt changes in source intensity are typically accompanied by equally sudden spectral shifts, that is, sudden changes in the wavelength distribution of the emission. This article develops a method for modeling photon counts collected from observation of such sources. We embed change points into a marked Poisson process, where photon wavelengths are regarded as marks and both the Poisson intensity parameter and the distribution of the marks are allowed to change. To the best of our knowledge, this is the first effort to embed change points into a marked Poisson process. Between the change points, the spectrum is modeled nonparametrically using a mixture of a smooth radial basis expansion and a number of local deviations from the smooth term representing spectral emission lines. Because the model is over-parameterized, we employ an ℓ1ℓ1 penalty. The tuning parameter in the penalty and the number of change points are determined via the minimum description length principle. Our method is validated via a series of simulation studies and its practical utility is illustrated in the analysis of the ultra-fast rotating yellow giant star known as FK Com

Information criteria for astrophysical model selection

Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.Comment: 5 pages, no figures. Update to match version accepted by MNRAS Letters. Extra references, minor changes to discussion, no change to conclusion

Defining and characterising structural uncertainty in decision analytic models

An inappropriate structure for a decision analytic model can potentially invalidate estimates of cost-effectiveness and estimates of the value of further research. However, there are often a number of alternative and credible structural assumptions which can be made. Although it is common practice to acknowledge potential limitations in model structure, there is a lack of clarity about methods to characterize the uncertainty surrounding alternative structural assumptions and their contribution to decision uncertainty. A review of decision models commissioned by the NHS Health Technology Programme was undertaken to identify the types of model uncertainties described in the literature. A second review was undertaken to identify approaches to characterise these uncertainties. The assessment of structural uncertainty has received little attention in the health economics literature. A common method to characterise structural uncertainty is to compute results for each alternative model specification, and to present alternative results as scenario analyses. It is then left to decision maker to assess the credibility of the alternative structures in interpreting the range of results. The review of methods to explicitly characterise structural uncertainty identified two methods: 1) model averaging, where alternative models, with different specifications, are built, and their results averaged, using explicit prior distributions often based on expert opinion and 2) Model selection on the basis of prediction performance or goodness of fit. For a number of reasons these methods are neither appropriate nor desirable methods to characterize structural uncertainty in decision analytic models. When faced with a choice between multiple models, another method can be employed which allows structural uncertainty to be explicitly considered and does not ignore potentially relevant model structures. Uncertainty can be directly characterised (or parameterised) in the model itself. This method is analogous to model averaging on individual or sets of model inputs, but also allows the value of information associated with structural uncertainties to be resolved.

The second moments matter: The response of bank lending behavior to macroeconomic uncertainty

In this paper we investigate whether macroeconomic uncertainty could distort banks’ allocation of loanable funds. To provide a road- map for our empirical investigation, we present a simple framework which demonstrates that lower uncertainty about the return from lending should lead to a more unequal distribution of lending across banks as managers take advantage of more precise knowledge of different lending opportunities. When bank-specific differences in lending opportunities are harder to predict, we should observe less cross-sectional variation in loan-to-asset ratios. Using a comprehensive U.S. commercial bank data set, we receive support for our hypothesis.Bank lending, financial intermediation, credit, macroeconomic, uncertainty, panel data, ARCH.

Hazardous Times for Monetary Policy: What do Twenty-three Million Bank Loans Say about the Effects of Monetary Policy on Credit Risk?

We investigate the impact of the stance and path of monetary policy on the level of credit risk of individual bank loans and on lending standards. We employ the Credit Register of the Bank of Spain that contains detailed monthly information on virtually all loans granted by all credit institutions operating in Spain during the last twenty-two years – generating almost twenty-three million bank loan records in total. Spanish monetary conditions were exogenously determined during the entire sample period. Using a variety of duration models we find that lower short-term interest rates prior to loan origination result in banks granting more risky new loans. Banks also soften their lending standards – they lend more to borrowers with a bad credit history and with high uncertainty. Lower interest rates, by contrast, reduce the credit risk of outstanding loans. Loan credit risk is maximized when both interest rates are very low prior to loan origination and interest rates are very high over the life of the loan. Our results suggest that low interest rates increase bank risk-taking, reduce credit risk in banks in the very short run but worsen it in the medium run. Risk-taking is not equal for all type of banks: Small banks, banks with fewer lending opportunities, banks with less sophisticated depositors, and savings or cooperative banks take on more extra risk than other banks when interest rates are lower. Higher GDP growth reduces credit risk on both new and outstanding loans, in stark contrast to the differential effects of monetary policy.monetary policy;low interest rates;financial stability;lending standards;credit risk;risk-taking;business cycle;bank organization;duration analysis

Estimating state price densities by Hermite polynomials: theory and application to the Italian derivatives market

We study the problem of extracting the state price densities from the market prices of listed options. Adapting a model of Madan and Milne to a multiple expiration setting, we present an estimation method for the risk-neutral probability at a moving horizon of fixed length. With the exception of volatility, all model parameters can be estimated by linear regression and their number can be chosen arbitrarily, depending on the size of the dataset. We discuss empirical issues related to the application of this model to real data and show results on listed options on the Italian MIB30 equity index.option pricing, state-price densities, orthogonal polynomials, risk-neutral valuation, calibration

Corporate Payout Policy in Japan

This paper examines cash dividends and share repurchases in Japan - discerning between keiretsu and non-keiretsu groupings of firms - during the period 1990 to 2008, a period of extensive Japanese corporate governance reform. As in the United States, share repurchases in Japan have grown strikingly across firm groupings even relative to cash dividends which have also increased. Unlike in the United States, cash dividends remain the dominant form of payout across the groupings of firms in Japan. Despite extensive corporate governance reform, the keiretsu grouping of firms exhibits a comparative reticence to alter its corporate payout policy. In particular, it remains the case that keiretsu firms disburse relatively large amounts of cash, they rely relatively heavily on cash dividends rather than share repurchases, they exhibit a greater tendency to discontinue cash dividend payouts, their payouts are relatively sensitive to earnings and these payouts respond relatively rapidly with respect to earnings. In addition, the cash dividend payouts in keiretsu firms have been relatively concentrated, while these payouts from non-keiretsu firms concentrate increasingly over time. The findings also suggest that larger firms in Japan are more likely to payout and if they decide to do so they tend to payout more. As the level of concentration of ownership in Japanese firms increases the amount of cash dividends disbursed decreases. Privatized firms are more likely to pay cash dividends and if they decide to do so and they are not keiretsu affiliated they tend to payout more.Payout policy, dividends, share repurchases, corporate governance