This paper offers a detailed investigation of the foreign exchange risk premium using a structural relationship in the inflation-index bond market, firstly introduced by Clarida (2012). Unlike the conventional VAR approach, this approach estimates risk premium through the non-arbitrage...

We propose a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear random forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging, the building blocks of our forest are HAR panel...

We study graphon mean-field backward stochastic differential equations (BSDEs) with jumps and associated dynamic risk measures. We establish the existence, uniqueness and measurability of solutions under some regularity assumptions. For an interacting mean-field particle system with...

Our data, relating to a period of extreme market turmoil, show typical leptokurtosis and skewness, leading us to consider the skewed exponential power distribution of Fernández et al. (1995), referred to as the SEP3. We demonstrate that the conditional forecasting of VaR and CVaR, made up of a...

I construct a general equilibrium model and show that the covariance between investment and labor with aggregate output in bad states commands a large premium, supporting empirical findings that connect to downside risk, and time-varying productivity exposures

Herewith we present a new Fourier method for credit risk quantification and allocation in the factor-copula model framework.The key insight is that, compared to directly computing the cumulative distribution function (CDF) of the total loss of a portfolio via Monte Carlo simulation, it is in...

Downside return risk, or the covariance between stock returns and the market in bad states, performs significantly better compared to the single factor CAPM model. I construct a general equilibrium model and show that the same holds using firm policies rather than stock returns. Specifically,...

Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this work and allow for more powerful validations compared to point forecasts. Our aim is to use bivariate copulas in order to characterize the in-sample copulas and to validate...