The subprime crisis was quite damaging for hedge funds. Using the local projection method (Jordà 2004, 2005, 2009), we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks-especially volatility and illiquidity shocks-over the subprime crisis...

This study proposes a wavelets approach to estimating time-frequency-varying betas in the capital asset pricing model (CAPM) framework. The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness. Furthermore, we emphasize the effect of an...

Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks, while the traditional mean-variance approach may fail to perform well. This study proposes an innovative semiparametric method consisting of two modeling components: the nonparametric estimation and...

This study investigates the factors of Bitcoin's tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin's 5% and 1% VaR. For the 5% VaR, quantity variables, such as...

In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto...

This study investigates the simplicity and adequacy of tail-based risk measures-valueat-risk (VaR) and expected shortfall (ES)-when applied to tail targeting of the extreme value (EV) model. We implement Lévy-VaR and ES risk measures as full density-based alternatives to the generalized Pareto...

This study evaluates the sensitivity and robustness of the systemic risk measure, Conditional Value-at-Risk (CoVaR), estimated using the vine copula and APARCH-DCC models. We compute the CoVaR for the two portfolios across fve allocation strategies. The novel vine copula captures the complex...

As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide...

In this study, we use bank loan information to construct proxies for corporate transparency and examine whether these measures reflect information asymmetry in the stock market. Our analysis is based on a novel dataset of stock transactions and bank loans of all publicly listed firms on the...

Although psychometric features have been considered for alternative credit scoring, they have not yet been applied to peer-to-peer (P2P) lending because such information is not available on platforms. This study proposed an alternative credit scoring model for P2P lending by extracting typical...