Alfonso Novales Cinca

Econometric Models of Credit Spreads

In its document “Basel III: A global regulatory framework for more resilient banks and banking systems”, the Basel Committee set a CVA methodology for the trading book, at a time the determination of the credit spread for those entities interested in advanced models in their risk management is vital. In a later document, the Basel Committee reaffirmed the idea of requiring the financial entities to estimate the credit spread curves considering the different factors of rating, sector and region of each counterparty. In the aftermath of the financial and economic crisis, is the OLS regression the optimal method to estimate credit spread curves? Least squares estimates can be influenced by the presence of outliers, so that more robust estimators may be preferred. Additionally, from the point of view of a financial institution, it would be desirable to reduce the fluctuations in risk estimates, which may need of nonlinear specifications. Using single name CDS data for the 2006-2012 period, we compare the performance of hierarchical models and exponential models with that of the linear least squares approach to provide credit risk estimates.

Basis Risk When Hedging a Global Credit Portfolio

The calculation of the capital charge for CVA risk, as required by the Basel Committee on Banking Supervision, is usually rather unstable due to the volatility of CDS spreads. Since credit derivatives on single names are not very liquid, the implied adjustments in capital charges could be reduced by hedging a credit derivative portfolio with a contrary position in a credit index. We examine the efficiency of such hedge in the face of decreased correlations between single name CDSs and CDS indices, and we also evaluate the level of basis risk still remaining under the hedge. We address several questions: Is there enough diversification of risk in a global credit portfolio to allow for a good hedge? Is basis risk higher in North America than in Europe? Does the effectiveness of the hedge increase when we take credit quality into account? Do conditional second order moments provide a better hedge than least squares?

Looking Through Systemic Risk: Determinants, Stress Testing and Market Value

We provide a methodology to estimate a global credit risk factor (GRF) from CDS spreads. The estimated factor contains higher explanatory power on CDS spread fluctuations across sectors than standard credit indices like iTraxx or CDX. We find a positive association between the GRF and implied volatility variables, and a negative association with MSCI stock market sector indices as well as with interest rates and with the slope and the curvature of the term structure. Such correlations provide useful insights for risk management and hedging of credit portfolios. Indeed, we present a factor model that can be used in a stress testing methodology of credit portfolios as well as to evaluate future credit risk scenarios. Finally, we show evidence suggesting that the GRF was priced in the market during the 2006-2015 period.

Long-Term Swings and Seasonality in Energy Markets

This paper introduces a continuous-time model for commodity pricing under the assumption that logged prices converge to a mean level that experiences smooth, periodic fluctuations over long periods of time. Our model incorporates that assumption by modelling the mean reversion level through a Fourier series. To validate the model, we perform an empirical study of futures prices on Natural Gas, Crude Oil, and Heating Oil. We provide evidence that such long-term fluctuations are present in the price of these energy commodities, possibly together with standard seasonal and cyclical components. We analyse the empirical performance of our pricing model versus two alternative competitors, namely, those proposed in Schwartz (1997) and Lucia and Schwartz (2002). Our findings show that our model outperforms both benchmarks, providing a simple and powerful tool for portfolio management, risk management and derivative pricing.

Why a Volatility Index Can Be Useful in the Spanish Financial Market

Two volatility indexes, VIBEX and VIBEX-NEW, are calculated for the Spanish financial market by using a non-model free, and a model free methodology, respectively. VIBEX-NEW index is worthy of being chosen first, due to liquidity problems in Spanish option market on IBEX35. Daily changes in this index have a negative relationship with IBEX35 current returns, so it can be considered like a measure of the current risk perception in the market. This negative relationship doesn’t change with ups and downs in volatility index, or different sign in IBEX35 return, so VIBEX-NEW information capability stand over the time in different market conditions. As we would expect, the negative relationship between returns and changes in VIBEX-NEW is quantitatively more important in the high volatility regime, it is, in the high volatility expectations periods. Finally, we find that daily changes in the volatility index are positively related to daily changes in future IBEX35 realized volatility, although the high mean forecasting error suggests that forecasting ability of VIBEX-NEW could be questioned.