Price Calibration of basket default swap: Evidence from Japanese market

The aim of this paper is the price calibration of basket default swap from Japanese market data. The value of this instruments depend on the number of factors including credit rating of the obligors in the basket, recovery rates, intensity of default, basket size and the correlation of obligors in the basket. A fundamental part of the pricing framework is the estimation of the instantaneous default probabilities for each obligor. Because default probabilities depend on the credit quality of the considered obligor, well-calibrated credit curves are a main ingredient for constructing default times. The calibration of credit curves take into account internal information on credit migrations and default history. We refer to Japan Credit Rating Agency to obtain rating transition matrix and cumulative default rates. Default risk is often considered as a rare-event and then, many studies have shown that many distributions have fatter tails than those captured by the normal distribution. Subsequently, the choice of copula and the choice of procedures for rare-event simulation govern the pricing of basket credit derivatives. Joshi and Kainth (2004) introduced an Importance Sampling technique for rare-event that forces a predetermined number of defaults to occur on each path. We consider using Gaussian copula and t-student copula and study their impact on basket credit derivative prices. We will present an application of the Canonical Maximum Likelihood Method (CML) for calibrating t-student copula to Japanese market data.Basket Default Swaps, Credit Curve, Monte Carlo method, Gaussian copula, t-student copula, Japanese market data, CML, Importance Sampling

A micro-to-macro approach to returns, volumes and waiting times

none2noModelling stock prices has been a research topic for many decades and it is still an open question. Different approaches have been used in the literature, the majority of which can be classified within the so-called econometric framework and sometimes also referred to as the macro-to-micro approach. Another strand of literature relies on the modelling of directly observable quantities, the so-called micro-to-macro approach. Based on this second line of research, we propose a new multivariate stochastic process to model simultaneously price returns, trading volumes and the time interval between changes in trades, price and volume. The proposed model is based on a generalization of semi-Markov chain models and copulas and is motivated by empirical evidence that the three mentioned variables are correlated and long-range autocorrelated. Utilizing Monte Carlo simulations, we compared our model with real data from the Italian stock market and show that it can reproduce many empirical pieces of evidence. The proposed model can be used in the field of portfolio optimization, development of risk measure and volatility forecasting.openD'Amico G.; Petroni F.D'Amico, G.; Petroni, F

Modeling international financial returns with a multivariate regime switching copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.asymmetric dependence, canonical vine copula, international returns, regime-switching, risk management, Value-at-Risk.

Overreaction and Multiple Tail Dependence at the High-frequency Level — The Copula Rose

This paper applies a non- and a semiparametric copula-based approach to analyze the first-order autocorrelation of returns in high frequency financial time series. Using the EUREX D3047 tick data from the German stock index, it can be shown that the temporal dependence structure of price movements is not always negatively correlated as assumed in the stylized facts in the finance literature. Depending on the sampling frequency, the estimated copulas exhibit some kind of overreaction phenomena and multiple tail dependence, revealing patterns similar to the compass rose.High Frequency Data, Non- and Semiparametric Copulas, Overreaction, Tail Dependence, Compass Rose

Modelling international financial returns with a multivariate regime switching copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copula. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are construted from bivariate conditional copulas and provide a very flexible way of characterizig dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.asymmetric dependence, canonical vine copula, international returns, regime-switching, risk management, Value-at-Risk

Modeling International Financial Returns with a Multivariate Regime Switching Copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk

Penalized Sieve Estimation and Inference of Semi-Nonparametric Dynamic Models: A Selective Review

In this selective review, we first provide some empirical examples that motivate the usefulness of semi-nonparametric techniques in modelling economic and financial time series. We describe popular classes of semi-nonparametric dynamic models and some temporal dependence properties. We then present penalized sieve extremum (PSE) estimation as a general method for semi-nonparametric models with cross-sectional, panel, time series, or spatial data. The method is especially powerful in estimating difficult ill-posed inverse problems such as semi-nonparametric mixtures or conditional moment restrictions. We review recent advances on inference and large sample properties of the PSE estimators, which include (1) consistency and convergence rates of the PSE estimator of the nonparametric part; (2) limiting distributions of plug-in PSE estimators of functionals that are either smooth (i.e., root-n estimable) or non-smooth (i.e., slower than root-n estimable); (3) simple criterion-based inference for plug-in PSE estimation of smooth or non-smooth functionals; and (4) root-n asymptotic normality of semiparametric two-step estimators and their consistent variance estimators. Examples from dynamic asset pricing, nonlinear spatial VAR, semiparametric GARCH, and copula-based multivariate financial models are used to illustrate the general results

Modeling International Financial Returns with a Multivariate Regime Switching Copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk