Contributions published at Banking (Stefano Battiston)

Grosse Zentralbanken schlagen Alarm: Die Erderhitzung bedrohe die Finanzmarktstabilität, Bankenpleiten und Firmenzusammenbrüche sind nicht ausgeschlossen. Finanzprofessor Stefano Battiston bemängelt, dass die vorgeschlagenen Massnahmen ungenügend seien – das betrifft auch die Schweizerische Nationalbank.

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We develop a method to analyze the effects on financial stability of the interplay between climate policy shocks and market conditions. We combine the frameworks of the Climate Stress-test with the framework of the network valuation of financial assets, in which the valuation of interbank claims accounts for market volatility as well as for endogenous recovery rates consistent with the network of obligations. We also include the dynamics of common asset contagion involving not only banks but also investment funds, which are key players in the low carbon transition. We then apply the model to a unique supervisory data-set of banks and investment funds at the firm level in order to assess the impact for financial stability of shocks deriving from the disorderly alignment of energy and utility sectors in a range of climate policy scenarios. While under mild shock scenarios systemic losses are contained, we identify the climate policy scenarios and market conditions under which systemic losses can pose a threat to financial stability.

We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneous volatility computed in directional-change intrinsic time. The provided volatility estimation method can be adapted as a universal multiscale risk-management tool independent of the discreteness and the type of analysed high-frequency data.

We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover market microstructure weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates as well as a stock market index. We demonstrate the long memory of instantaneous volatility computed in directional-change intrinsic time. The provided volatility estimation method can be adapted as a universal multiscale risk-management tool independent of the discreteness and the type of analysed large high-frequency data.

We describe an agent-based model where trades happen in event-based time called directional-change intrinsic time. Events are defined as the reversal price moves of a directional-change threshold from a local extreme. The price impact of traded volumes is modelled according to the empirically observed squared root impact function. The time series generated by the agents is characterised by statistical properties typical for foreign exchange rates: low auto-correlation of returns, fat-tailed distribution of returns, aggregated normality, and the price jump scaling law. Furthermore, we introduce and use as a benchmark, the overshoot scaling law, which is an omnipresent feature of liquid markets and relates the expected length of price overshoots to the length of the corresponding directional-change threshold.

Time, as they say, is of essence in the financial world. Every second counts. But the reality is that the economic system in which the finance sector operates is not constricted by our traditional perceptions of time, nor is it a black-and-white world where price monitoring only can keep you afloat. In this article, Vladimir Petrov, PhD research fellow of Marie Sklodowska-Curie initiative BigDataFinance at the University of Zurich, and Anton Golub, Founder and CEO of flov technologies, explore agent-based models in directional-change intrinsic time to help navigate this increasingly unpredictable economy.

The role of development finance institutions in low‐income and emerging countries is fundamental to provide long‐term capital for investments in climate mitigation and adaptation. Nevertheless, development finance institutions still lack sound and transparent metrics to assess their projects' exposure to climate risks and their impact on global climate action. To attempt to fill this gap, we develop a novel climate stress‐test methodology for portfolios of loans to energy infrastructure projects. We apply the methodology to the portfolios of overseas energy projects of two main Chinese policy banks. We estimate their exposure to economic and financial shocks that would result in government inability to introduce timely 2°C‐aligned climate policies and from investors' inability to adapt their business to the changing climate and policy environment. We find that the negative shocks are mostly concentrated on coal and oil projects and vary across regions from 4.2 to 22 percent of the total loan value. Given the current leverage of Chinese policy banks, these losses could induce severe financial distress, with implications on macroeconomic and financial stability.

Over the last decades, both advanced and emerging economies have experienced a striking increase in the intra-financial activity across different asset classes and increasingly complex contract types, leading to a far more complex financial system. Until the 2007-2008 crisis, the increased financial intensity and complexity was believed beneficial in making the financial system more resilient and less vulnerable to shocks. However, in 2007-2008, the advanced economies suffered the biggest financial crisis since the 1930s, followed by a severe post-crisis recession, questioning the adequacy of traditional tools in predicting, explaining, and responding to periods of financial distress. In particular, the effect of complex interconnections among financial actors on financial stability has been widely acknowledged. A recent debate focused on the effects of unconventional policies aimed at achieving both price and financial stability. Among these unconventional policies, Quantitative Easing (QE, i.e., the large-scale asset purchase programme conducted by a central bank upon the creation of new money) has been recently implemented by the European Central Bank (ECB). In this context, two questions deserve more attention in the literature. First, to what extent, the resources provided to the banking system through QE are transmitted to the real economy. Second, to what extent, the QE may also alter the pattern of intra-financial exposures and what are the implications in terms of financial stability. Here, we address these two questions by developing a methodology to map the multilayer macro-network of financial exposures among institutional sectors across financial instruments (i.e., loans and deposits, debt securities, and equity), and we illustrate our approach on recently available data. We then test the effect of the implementation of ECB’s QE on the time evolution of the financial linkages in the multilayer macro-network of the euro area, as well as the effect on macroeconomic variables, such as consumption, investment, unemployment, growth, and inflation.

With this interview, we would like to shed some light on the work of the Lykke research team. Lykke’s policy, as one of the innovators in the financial technologies market, has always been attractive to young and talented researchers and developers. Vladimir Petrov, a Marie Curie Ph.D. student at the University of Zurich will tell us how scientific research and development meet at Lykke.

This paper presents an analysis of the European Emission Trading System as a transaction network. It is shown that, given the lack of well-identified trading institutions, industrial actors had to resort to local connections and financial intermediaries to participate in the market. This gave rise to a hierarchical structure in the transaction network. It is then shown that the asymmetries in the network induced market inefficiencies (e.g., increased bid-ask spread) and informational asymmetries, that have been exploited by central agents at the expense of less central ones. Albeit the efficiency of the market has improved from the beginning of Phase II, the asymmetry persists, imposing unnecessary additional costs on agents and reducing the effectiveness of the market as a mitigation instrument.

Existing approaches to assess the economic impact of climate policies tend to overlook the financial sector and to focus only on direct effects of policies on the specific institutional sector they target, neglecting possible feedbacks between sectors, thus, underestimating the overall policy effect. To fill in this gap, we develop a methodology based on financial networks, which allows for analyzing the transmission throughout the economy of positive or negative shocks induced by the introduction of specific climate policies. We apply the methodology to empirical data of the Euro Area to identify the feedback loops between the financial sector and the real economy both through direct and indirect chains of financial exposures across multiple financial instruments. By focusing on climate policy-induced shocks that affect directly either the banking sector or non-financial firms, we analyze the reinforcing feedback loops that could amplify the effects of shocks on the financial sector and then cascade on the real economy. Our analysis helps to understand the conditions for virtuous or vicious cycles to arise in the climate-finance nexus and to provide a comprehensive assessment of the economic impact of climate policies.

Vladimir Petrov is a Lindau Alumnus of the 6th Lindau Meeting on Economic Sciences in 2017. He is a PhD research fellow of Marie Sklodowska-Curie initiative BigDataFinance at the University of Zurich. He shares his impressions of #LINOEcon and has advice for the young scientists of the upcoming 68th Lindau Nobel Laureate Meeting.

Most discussions around cryptocurrencies generally focus on the underlying value of the "coins" being purchased and how the technology will advance and can be put to good use, but many will have also seen that the prices for these currencies are also extremely volatile. Here, Anton Golub, Co-founder and Chief Science Officer at Lykke Corp and Vladimir Petrov, Department of Banking and Finance, University of Zurich, discuss Bitcoin's seasonal volatility.

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Financial networks have shown to be important in understanding systemic events in credit markets. In this paper, we investigate how the structure of those networks can affect the capacity of regulators to assess the level of systemic risk. We introduce a model to compute the individual and systemic probability of default in a system of banks connected in a generic network of credit contracts and exposed to external shocks with a generic correlation structure. Even in the presence of complete knowledge, we identify conditions on the network for the emergence of multiple equilibria. Multiple equilibria give rise to uncertainty in the determination of the default probability. We show how this uncertainty can affect the estimation of systemic risk in terms of expected losses. We further quantify the effects of cyclicality, leverage, volatility and correlations. Our results are relevant to the current policy discussions on new regulatory framework to deal with systemic events of distress as well as on the desirable level of regulatory data disclosure.