This work focuses on a self-exciting point process defined by a Hawkes-like intensity and a switching mechanism based on a hidden Markov chain. Previous works in such a setting assume constant intensities between consecutive events. We extend the model to general Hawkes excitation kernels that are piecewise constant between events. We develop an expectation-maximization algorithm for the statistical inference of the Hawkes intensities parameters as well as the state transition probabilities. The numerical convergence of the estimators is extensively tested on simulated data. Using high-frequency cryptocurrency data on a top centralized exchange, we apply the model to the detection of anomalous bursts of trades. We benchmark the goodness-of-fit of the model with the Markov-modulated Poisson process and demonstrate the relevance of the model in detecting suspicious activities.

This study investigates the differential benefits accruing to firms that issue global bonds relative to those issuing domestic bonds. Employing a comprehensive international dataset comprising 11,852 public corporate fixed-rate global bonds and 107,877 domestic bonds denominated in global currencies issued by publicly listed firms over the period 2000–2023, we document that global bond issuance is associated with significantly lower financing costs, enhanced stock market liquidity, increased participation by foreign and long-term institutional investors, and short-term valuation gains. The empirical findings lend support to the investor recognition hypothesis, demonstrating that global bond issuance confers benefits beyond immediate capital-raising objectives by influencing ownership composition and stock market dynamics.

We examine the association between cryptocurrency environmental attention and cryptocurrency bubbles. Our results indicate that environmental attention is positively associated with the probability of a cryptocurrency bubble and ranks as the second most important explanatory factor. The positive association is more pronounced for smaller, less-mature, and proof-of-work (PoW) cryptocurrencies, indicating that cryptocurrency characteristics are important determining factors of bubble formation.

We examine how climate-related (transition and physical) risks impact European bond markets and inflation expectations, and identify their effects across distinct volatility regimes using a Markov-switching vector autoregression model. Our central finding is that the transmission of climate-related risk shocks is highly state-dependent and primarily affects short-term inflation expectations. Transition risks have a limited, disinflationary effect on short-term expectations, but only during low volatility periods. In sharp contrast, physical risks exert a destabilising, inflationary impact during high volatility periods, depressing bond returns and amplifying market stress. Additionally, we observe two more patterns: first, that long-term inflation expectations tend to remain largely anchored. Second, financial linkages and contagion tend to intensify in the high volatility state. Our findings matter for asset pricing and for monetary authorities. They support integrating climate-related risks into stability frameworks, as these shocks presumably intensify and complicate the trade-off between inflation-target credibility and financial stability.

This paper extends the literature studying the prediction of financial crises in two ways, namely by: (i) developing a new text-based indicator measuring banks' sentiment tailored to the context of financial stability, and (ii) applying machine learning (ML) techniques to predict systemic crises in the euro area as defined by the European Systemic Risk Board (ESRB). In-sample analysis indicates that banks' financial stability sentiment (BFSS) is a highly statistically significant predictor of systemic crises, with a negative one standard deviation shock in the BFSS indicator corresponding to increases in the probability of a systemic crisis of 7 and 3 percentage points one-quarter and four-quarters ahead, respectively, while controlling for the credit cycle. Out-of-sample results show that, while the BFSS tends to improve the predictive performance of baseline logistic regression models, ML models grounded in financial stability dictionaries deliver substantially higher predictive accuracy in forecasting systemic crises. By improving the accuracy and timeliness of systemic crisis prediction, this novel application can be useful to complement conventional approaches for calibrating macroprudential policy tools and enhance crisis prevention frameworks.

One consequence of climate change is an increase in the frequency and severity of extreme weather events. The impact of these events is felt by many economies worldwide (e.g. through the cost of replacing damaged assets). The aim of this paper is to examine the relationships between various stock market indices and 260 selected extreme weather events in 21 countries between 1995 and 2024. To address this, we regress all the events against various stock market indices for several sectors of the economy. We find that extreme weather events have so far had a limited impact on stock markets compared with systemic events such as financial crises. However, the overall trend suggests that the physical risk of climate change is having a gradually increasing impact on financial markets. These findings underscore the need for proactive measures to address the escalating long-term financial risks associated with climate change.

This paper examines whether blockholders and their investment horizon influence firms' ESG performance in the Nordic countries. We use ownership data for the three largest owners of the publicly listed firms, and we find a positive, statistically significant association between the ownership of the top two blockholders and a firm's ESG scores. Closer analysis reveals that the impact is driven by long-term blockholders. The effect concentrates on the environmental and social pillars, rather than governance. Our study contributes to the literature by showing that long-term horizons of the largest blockholders, identified through observed holding periods, play a key role in shaping firms' ESG performance in the Nordic context. The results imply that boards, policymakers, and stewardship teams can leverage stable, long-horizon blockholders to advance environmental and social efforts, even when governance scores do not move in tandem.

This paper investigates how weather-affected firms make decisions on fixed asset purchases and financing choices for fixed asset acquisition. Utilizing a unique dataset comprising over 26,000 firms across 40 countries, we find that weather-affected firms are more prone to purchase fixed assets, increasing investments in machinery, equipment, and real estate. These purchases are primarily financed through equity, bank loans, and government grants. Particularly, we find that leasing is a vital fallback financing source for firms experiencing losses due to extreme weather. Firms that exclusively rely on leasing rather than other financial sources are more likely to face significant external financing barriers, including complex loan procedures, high collateral requirements, and increased loan rejection rates. Interestingly, weather-affected firms that have successfully obtained non-leasing finance for fixed asset purchases have a higher tendency to also engage in leasing, highlighting that such firms adopt flexible strategies for fixed asset acquisition.

Portfolio management poses unique challenges for traditional forecasting methods due to its complex, sequential decision-making process. This study leverages reinforcement learning (RL) to address these challenges, focussing on fixed income portfolio management. We develop a novel autonomous RL system using a custom environment for bond exchange-traded fund (ETF) dynamics and the Deep Deterministic Policy Gradient (DDPG) algorithm. Unlike prior studies that merely report algorithmic instability, our work systematically addresses this issue by introducing a robust agent selection process during training. To illustrate the practical benefits, we construct a simple equally weighted ensemble of selected agents that outperforms the static buy-and-hold benchmark by 4.3 % and achieves a total return comparable to the portfolio's best-performing asset, while exhibiting superior risk characteristics during periods of market stress. Our methodology also incorporates methodological innovations, including a scaled reward structure to improve learning in bond markets. While instability is observed in the DDPG algorithm, our results demonstrate that this challenge can be systematically mitigated through robust agent selection and ensemble methods. These findings establish RL as a powerful tool for financial strategies where direct forecasting is complex and uncertain, offering a practical framework for implementation in fixed income markets.

We find that equity option liquidity increases future stock price crash risk in the US market. This effect differs from the stock liquidity-crash risk causality documented elsewhere and remains robust to different measures of option liquidity and crash risk, alternative weighting schemes, option moneyness, and is not influenced by endogeneity issues. The option liquidity-stock crash risk causality is a unique phenomenon, not a manifestation of higher crash risk in times of high volatility, and is not confined to the financial crisis period. The positive impact of option liquidity on future crash risk is more apparent for firms with higher degrees of information asymmetry and for low levels of option investors’ sentiment. Our results support the transient investor channel which posits that, in presence of myopic investors attracted to liquid options, managers make poor decisions and hoard subsequent bad news to avoid negative market reactions, leading to accumulation of bad news and price crashes when information is finally revealed.