We introduce an arbitrage-free framework for robust valuation adjustments. An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in defaultable bonds. The investor does not know the exact return rate of her counterparty's bond,...

In this paper we present a novel and highly flexible method to simulate correlation matrices of financial markets. It produces realistic outcomes regarding stylized facts of empirical correlation matrices and requires no asset return input data. The matrix generation is based on a...

Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman...

How large are government spending and tax multipliers? The fiscal proxy-SVAR literature provides heterogenous estimates, depending on which proxies - fiscal or non-fiscal - are used to identify fiscal shocks. We reconcile the existing estimates via a flexible vector autoregressive model that...

How large are government spending and tax multipliers? The fiscal proxy- SVAR literature provides heterogenous estimates, depending on which proxies - fiscal or non-fiscal - are used to identify fiscal shocks. We reconcile the existing estimates via a flexible vector autoregressive model that...

How large are government spending and tax multipliers? The fiscal proxy-SVAR literature provides heterogeneous estimates, depending on which proxies - fiscal or non-fiscal - are used to identify fiscal shocks. We reconcile the existing estimates via a flexible vector auto-regressive model that...

Under-reaction is a robust response to model misspecification rewarded by financial markets, rather than an “irrational” attitude that leads to extinction. I show that under-reacting rules guarantee predictions as accurate as Bayes' in well-specified learning problems and beat Bayes' in many...

The research focuses on the financial turmoil, pursuing different methods to foretell such turmoil. Besides, the methods are undertaken from (McCulloch and Pitts 1943) and ended till (Hosaka 2019). The evidence from such a comprehensive analysis pointed to the use of various ratios using...

Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural...

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including long-run risk models, disaster risk models, and multifactor linear asset pricing models. Building on recent developments in the conditional...