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This paper investigates the effect of the conventional and unconventional (e.g. Quantitative Easing - QE) monetary policy intervention on the insurance industry. We first analyze the impact on the stock performances of 166 (re)insurers from the last QE programme launched by the European Central Bank (ECB) by constructing an event study around the announcement date. Then we enlarge the scope by looking at the monetary policy surprise effects on the same sample of (re)insurers over a timeframe of 12 years, also extending the analysis to the Credit Default Swaps (CDS) market. In the second part of the paper by building a set of balance sheet-based indices, we identify the characteristics of (re)insurers that determine sensitivity to monetary policy actions. Our evidences suggest that a single intervention extrapolated from the comprehensive strategy cannot be utilized to estimate the effect of monetary policy intervention on the market. With respect to the impact of monetary policies, we show how the effect of interventions changes over time. Expansionary monetary policy interventions, when generating an instantaneous reduction of interest rates, generated movement in stock prices in the same direction till September 2010. This effect turned positive during the European sovereign debt crisis. However, the effect faded away in 2014-2015. The pattern is confirmed by the impact on the CDS market. With regard to the determinants of these effects, our analysis suggests that sensitivity is mainly driven by asset allocation and in particular by exposure to fixed income assets.
We show that FED policy announcements lead to a significant increase in international comovements in the cross-section of equity and in particular sovereign CDS markets. The relaxation of unconventionary monetary policies is felt strongly by emerging markets, and by countries that are open to the trading of goods and flows, even in the presence of floating exchange rates. It also impacts closed economies whose currencies are pegged to the dollar. This evidence is consistent with recent theories of a global financial cycle and the pricing of a FED’s put. In contrast, ECB announcements hardly affect comovements, even in the Eurozone.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral specification. We study covariance stationarity and identification of the model, and analyze consistency and asymptotic normality of the quasi-maximum-likelihood estimator. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euro-area cross-country holdings shows that Italy and Ireland are key players in spreading risk, France and Portugal are the major risk receivers, and we uncover Spain's non-trivial role as risk middleman.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
Banks can deal with their liquidity risk by holding liquid assets (self-insurance), by participating in interbank markets (coinsurance), or by using flexible financing instruments, such as bank capital (risk-sharing). We use a simple model to show that undiversifiable liquidity risk, i.e. the liquidity risk that banks are unable to coinsure on interbank markets, represents an important risk factor affecting their capital structures. Banks facing higher undiversifiable liquidity risk hold more capital. We posit that empirically banks that are more exposed to undiversifiable liquidity risk are less active on interbank markets. Therefore, we test for the existence of a negative relationship between bank capital and interbank market activity and find support in a large sample of U.S. commercial banks.
Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.