Consumer Spending in Switzerland: Insights from a Novel Transactional Data Index with Jonas M. Bruhin, Winfried Koeniger & Robert Rohrkemper
Abstract: We analyze transactional payment data to study consumption expenditure patterns in Switzerland. The high-frequency nature of the data enables credible identification of expenditure changes both across and within weeks, which is essential for business decisions and economic policy analysis. We construct a consumer spending index that is granular across regions and broad product categories, allowing for consistent analysis over multiple years. Using data from 2018 to 2024, we demonstrate the index’s potential to (i) reveal expenditure patterns relevant for strategic decisions by businesses and consumers across weekdays and categories, and (ii) identify economically and statistically significant short-term effects of monetary policy shocks.
The transmission of monetary policy to the cost of hedging with Winfried Koeniger & Stephan Minger
Abstract: We analyze the transmission of monetary policy to the costs of hedging using options order book data. Monetary policy transmits to hedging costs both by changing the relevant state variables, such as the value of the underlying, its volatility and tail risk, and by affecting option market liquidity, including the bid-ask spread and market depth. Our estimates suggest that during the peak of the pandemic crisis in March 2020, monetary policy decisions resulted in substantial changes in hedging costs even within short intraday time windows around the decisions, amounting approximately to the annual expenses of a typical equity mutual fund.
Proxy-identification of a structural MGARCH model for asset returns with Jeannine Polivka
Abstract: We extend the multivariate GARCH (MGARCH) specification for volatility modeling by developing a structural MGARCH model that targets the identification of shocks and volatility spillovers in a speculative return system. Similarly to the proxy-SVAR framework, we leverage auxiliary proxy variables to identify the underlying shock system. The estimation of structural parameters, including an orthogonal matrix, is achieved through techniques derived from Riemannian optimization. Our analysis of daily S&P 500 returns, 10-year Treasury yields, and the US Dollar Index, employing news-driven instrument variables, identifies an equity and a bond market shock.
Locally adaptive modeling of unconditional heteroskedasticity with Bruno Jäger & Ostap Okhrin
Abstract: We study local change-point detection in variance using generalized likelihood ratio tests. Building on Suvorikova & Spokoiny (2017), we utilize the multiplier bootstrap to approximate the unknown, non-asymptotic distribution of the test statistic and introduce a multiplicative bias correction that improves upon the existing additive version. This proposed correction offers a clearer interpretation of the bootstrap estimators while significantly reducing computational costs. Simulation results demonstrate that our method performs comparably to the original approach. We apply it to the growth rates of U.S. inflation, industrial production, and Bitcoin returns.
Global estimation of realized spot volatility in the presence of price jumps with Wale Dare
[Link to Current Draft]
Abstract: We propose a non-parametric procedure for estimating the realized spot volatility of a price process described by an Itô semimartingale with Lévy jumps. The procedure integrates the threshold jump elimination technique of Mancini (2009) with a frame (Gabor) expansion of the realized trajectory of spot volatility. We show that the procedure converges in probability in L2([0, T]) for a wide class of spot volatility processes, including those with discontinuous paths. Our analysis assumes the time interval between price observations tends to zero; as a result, the intended application is for the analysis of high frequency financial data.