We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the...

This paper examines the impact of foreign participation in Korean Treasury Bond (KTB) futures and its role in price discovery for KTBs, using daily transactions data from the over-the-counter market for KTBs and from the Korea Exchange for the futures. Our analysis suggests that foreign trading...

On October 26, 2008, Porsche announced a largely unexpected domination plan for Volkswagen. The resulting short squeeze in Volkswagen's stock briefly made it the most valuable listed company in the world. We argue that this was a manipulation designed to save Porsche from insolvency and the...

We elaborate economic explanations for the time-varying risk of month, quarter and year base load electricity forward contracts traded on the Nord Pool Energy Exchange from January 2006 to March 2010. Daily risk quantities are generated by decomposing realized volatility in its continuous and...

The recent financial crisis renewed concerns about a possible destabilizing impact of derivatives trading. Despite a very active research, the question whether or not derivatives tend to destabilize financial markets has not yet been answered to satisfaction. This contribution aims to revise the...

We forecast monthly Value at Risk (VaR) and Conditional Value at Risk (CVaR) using option market data and four different econometric techniques. Independently from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount...

We evaluate price discovery in the natural gas futures and futures options markets using a transaction based approach. By sampling market maker prices, we allow for a distinction between buy and sell prices, both directly from the futures market, and implied from the options market. Information...

The purpose of this study is to revisit price discovery process in Indian stock market for spot and futures of S&P CNX Nifty, by using high-frequency data to gain fresh insights. The sample consists of high-frequency data for the period from January 2014 to August 2015. Stationarity and...

Prior research documents that volatility spreads predict stock returns. If the trading activity of informed investors is an important driver of volatility spreads, then the predictability of stock returns should be more pronounced during major information events. This paper investigates whether...

This research aims to revisit the price discovery relationship between spot and futures prices of Indian equity index S&P CNX Nifty, using neural network approach. This study uses minute-by-minute prices of 167 trading days ranging from January, 2015 to August, 2015 to gain fresh insights on...