We study the conditional distribution of future liquidity in the secondary market for corporate bonds as a function of … current liquidity. Increases in liquidity are persistent for investment-grade bonds and flighty for high-yield bonds. Greater … liquidity of high-yield bonds is associated with lower uncertainty about future liquidity of investment-grade bonds, but greater …

This paper employs weighted least squares to examine the risk-return relation by applying high-frequency data from four … returns and expected risk. However, by using quantile regressions, we find that the risk-return relation moves from negative … to positive as the returns’ quantile increases. A positive risk-return relation is valid only in the upper quantiles. The …

This study examined the asymmetric effects of major uncertainty and volatility indices (economic policy uncertainty …, Chicago Board Options Exchange crude oil volatility, CBOE volatility index, CBOE VIX volatility, and NASDAQ 100 volatility … petroleum). The causalityin-quantiles test and the quantile-on-quantile regression technique were employed on daily data …

parameters. The piecewise logistic regression model and Box-Cox transformation of credit risk score is used to derive the … inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of …

in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density … indices, the regression based evaluation strategy is compared with a recently proposed methodology based on likelihood ratio … tests. It is demonstrated that misspecifications of forecasting models can be detected within the proposed regression …

in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density … indices, the regression based evaluation strategy is compared with a recently proposed methodology based on likelihood ratio … tests. It is demonstrated that misspecifications of forecasting models can be detected within the proposed regression …

Support Vector Regression (SVR) algorithm with the risk premium theoretical framework for the forecasting model; consequently …Risk premium plays an important role in stock investing. Experiments have shown that value stocks typically have a … studies suggested high volatility in the series of returns, broken structures, market volatility, or the impact of financial …

In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the … direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets …