Publications
Abstract: Banks may be reluctant to remove bad loans from their portfolios during liquidity shortfalls, giving rise to a moral hazard problem. In this paper, we analyze how liquidity shortages affect the ability of the interbank market to provide liquidity in a moral hazard setting. We distinguish two types of liquidity shocks that arise due to a deposit flight (a contraction in the deposit supply) or to a cash-flow shock (an increase in the non-performing loans). We show that the source of a liquidity shortfall is the main determinant of the decision of banks to stop lending in the interbank market, rather than the extra amount of funds that banks need to cover. An increase in the non-performing loans has more adverse effects on balance sheets than a deposit flight. We also demonstrate that competition has a dual effect on financial stability. Interbank competition enhances financial stability by reducing the liquidity provision cost, whereas credit market competition worsens financial stability by inducing banks to take riskier profiles.
Abstract: In this paper we perform density prediction for the equity returns in a non-linear manner by employing a copula-based approach. The use of asymmetric copulas allows to model asymmetric predictive densities and non-linear dependencies between equity returns and some predictor variable. In our proposed approach, the copula parameter and the marginals are estimated simultaneously by using Sequential Monte Carlo techniques. We apply proposed models to daily log returns of 20 assets traded at the NYSE. Among other findings, we show that in terms of predictive log Bayes Factors the asymmetric copula is preferred by more assets than the symmetric copula, advocating the use of non-linear models. Also, dividend yield is a better predictor variable than the lagged returns overall, but this result is reversed if we consider a volatile period only. These results have major implications for the investors when making portfolio decisions or measuring tail risk.
Abstract: In this paper we perform density prediction for the equity returns in a non-linear manner by employing a copula-based approach. The use of asymmetric copulas allows to model asymmetric predictive densities and non-linear dependencies between equity returns and some predictor variable. In our proposed approach, the copula parameter and the marginals are estimated simultaneously by using Sequential Monte Carlo techniques. We apply proposed models to daily log returns of 20 assets traded at the NYSE. Among other findings, we show that in terms of predictive log Bayes Factors the asymmetric copula is preferred by more assets than the symmetric copula, advocating the use of non-linear models. Also, dividend yield is a better predictor variable than the lagged returns overall, but this result is reversed if we consider a volatile period only. These results have major implications for the investors when making portfolio decisions or measuring tail risk.
Abstract: This paper performs a general GARCH and GAS analysis for modelling and forecasting bitcoin returns and risk. Since Bitcoin trading exhibits excess volatility compared with other securities, it is important to model its risk and returns. We consider heavy-tailed GARCH models as well as GAS models based on the score function of the predictive conditional density of the bitcoin returns. We compare out-of-sample 1%-Value-at-Risk (VaR) forecasts under 45 different specifications using three backtesting procedures. We find that GAS models with heavy-tailed distributions provide the best out-of-sample forecast and goodness-of-fit properties to bitcoin returns and risk modelling. Normally-distributed GARCH models are always outperformed by heavy-tailed GARCH or GAS models. Besides, heavy-tailed GAS models provide the best conditional and unconditional coverage for 1%-VaR forecasts, illustrating the importance of modelling excess kurtosis for bitcoin returns. Hence, our findings have important implications for risk managers and investors for using bitcoin in optimal hedging or investment strategies.
Working Papers
Abstract: We analyze the problem of a regulator that sets capital and liquidity requirements to maximize social welfare in a framework in which a bank determines insolvency risk facing a risk-return tradeoff. Capital requirements reduce risk shifting by increasing the bank's skin-in-the-game, but entails a costly substitution of deposits for capital. Liquidity requirements mitigate short-term withdrawal risk, but aggravate risk-shifting because they reduce the bank's returns. We find that liquidity and capital requirements complement (resp. offset) each other when illiquidity and insolvency risk are high (resp. low). In the presence of a lender of last resort, capital (resp. liquidity) becomes more (resp. less) effective, and capital and liquidity become offsetting regulatory tools so that any increase in capital requirements should be coupled with a reduction of liquidity requirements. The main message from this analysis is that capital and liquidity should be regulated jointly taking into account that the effectiveness of capital requirements depends on the liquidity requirement, and viceversa.
Abstract: The performance of sovereign bonds during the 2007-2013 period illustrates how the liquidity of an asset can change over time. To understand the effects of this phenomenon, we develop a theoretical framework that analyses the consequences of holding liquid risky assets such as sovereign bonds on bank’s liquidity risk. In the model, holding cash as a liquidity reserve has a high opportunity cost, hence banks reduce this type of reserves increasing liquidity risk. Using sovereign bonds as liquidity reserves is less expensive, but liquidity risk becomes significant if those assets become worthless. We show that diversifying the type of liquid assets mitigates liquidity and solvency risk by reducing both the opportunity cost of reserves and the risk when the value of sovereign bonds plunges. Furthermore, we find that even though the bank is protected by limited liability, it will choose a sufficiently diversified portfolio of liquid asset, such that it minimizes liquidity risk. All these results are robust to the presence of a Lender of Last Resort (LoLR). However, we show that theLoLR has a dual effect on the bank’s liquidity risk. As the likekihood of LoLR intervention increases, the bank reduces its liquid reserves, nevertheless it hoards higher quality liquid assets. We find that the latter effect prevails, and therefore the LoLR ameliorates liquidity risk, only if it charges sufficiently high penalty rates.