Financial sentiment analysis models, which extract meaning from vast amounts of unstructured data, play a crucial role in sentiment-driven financial decisions. However, the complex and domain-specific language used in finance poses unique challenges for adversarial attacks. To address these challenges, we propose a novel, white-box attack methodology leveraging a pre-trained general-purpose language model (GPT-4o). We employ carefully designed instructions and incorporate a new loss function based on embedding similarity to ensure semantic coherence while producing syntactically diverse samples. Our experimental results demonstrate that both FinBERT and Fin-GPT, leading models in financial sentiment analysis, exhibit significant susceptibility to our proposed adversarial attacks. Specifically, the sentiment predictions of these models were successfully altered for a substantial proportion of the samples across three public datasets, including Financial Phrase Bank (FPB), Twitter Financial News Sentiment (TFNS), and Sentimence and Entity Annotated Financial News (SEntFiN). Our findings emphasize the need for enhanced robustness in financial classification models against adversarially targeted attacks. By understanding and addressing these vulnerabilities, it is possible to improve the reliability and security of automated financial systems.

To simultaneously account for the properties of interest-rate term structure and foreign exchange rates within one arbitrage-free framework, we propose a class of multi-currency quadratic models (MCQM) with an (m + n) factor structure in the pricing kernel of each economy. The m factors model the term structure of interest rates. The n factors capture the portion of the exchange rate movement that is independent of the term structure. Our modeling framework represents the first in the literature that not only explicitly allows independent currency movement, but also guarantees internal consistency across all economies without imposing any artificial constraints on the exchange rate dynamics. We estimate a series of multi-currency quadratic models using U.S. and Japanese LIBOR and swap rates and the exchange rate between the two economies. Estimation shows that independent currency factors are essential in releasing the tension between the currency movement and the term structure of interest rates.

We consider the design and estimation of quadratic term structure models. We start with a list of stylized facts on interest rates and interest rate derivatives, classified into three layers: (1) general statistical properties, (2) forecasting relations, and (3) conditional dynamics. We then investigate the implications of each layer of property on model design and strive to establish a mapping between evidence and model structures. We calibrate a two-factor model that approximates these three layers of properties well, and show that a flexible specification for the market price of risk is important in capturing the stylized evidence in forecasting relations while factor interactions are indispensable in generating the hump-shaped dynamics of bond yields.

We propose a simple model of credit contagion in which we include macro- and microstructural interdependencies among the debtors within a credit portfolio. The microstructure captures interdependencies between debtors that go beyond their exposure to common factors, e.g., business or legal interdependencies. We show that even for diversified portfolios, moderate microstructural interdependencies have a significant impact on the tails of the loss distribution. This impact increases dramatically for less diversified microstructures.

Credit limit management is of paramount importance for successful short-term credit risk management, even more so when the situation in credit and financial markets is tense. We consider a continuous-time model where the credit provider and the credit taker interact within a game-theoretic framework under different information structures. The model with complete information provides decision-theoretic insights into the problem of optimal limit policies and motivates more complicated information structures. Moving to a partial information setup, incentive distortions emerge that are not in the bank’s interest. We discuss how these distortions can effectively be reduced by an incentive-compatible contract. Finally, we provide some practical implications of our theoretical results.

We identify and characterize a class of term structure models where bond yields are quadratic functions of the state vector.We label this class the quadratic class and aim to lay a solid theoretical foundation for its future empirical application.We consider asset pricing in general and derivative pricing in particular under the quadratic class.We provide two general transform methods in pricing a wide variety of fixed income derivatives in closed or semi-closed form.We further illustrate how the quadratic model and the transform methods can be applied to more general settings.

We analyze American put options in a hyper-exponential jump-diffusion model. Our contribution is threefold. Firstly, by following a maturity randomization approach, we solve the partial integro-differential equation and obtain a tight lower bound for the American option price. Secondly, our method allows to disentangle the contributions of jumps and diffusion for the early exercise premium. Finally, using American-style options on the S&P 100 index from January 2007 until December 2012, we estimate various hyper-exponential specifications and investigate the implications for option pricing and jump-diffusion disentanglement. We find that jump risk accounts for a large part of the early exercise premium.

This paper performs specification analysis on the term structure of variance swap rates on the S&P 500 index and studies the optimal investment decision on the variance swaps and the stock index. The analysis identifies 2 stochastic variance risk factors, which govern the short and long end of the variance swap term structure variation, respectively. The highly negative estimate for the market price of variance risk makes it optimal for an investor to take short positions in a short-term variance swap contract, long positions in a long-term variance swap contract, and short positions in the stock index.

We estimate a flexible affine model using an unbalanced panel containing S&P 500 and VIX index returns and option prices and analyze the contribution of VIX options to the model’s in- and out-of-sample performance. We find that they contain valuable information on the risk-neutral conditional distributions of volatility at different time horizons, which is not spanned by the S&P 500 market. This information allows enhanced estimation of the variance risk premium. We gain new insights on the term structure of the variance risk premium, present a trading strategy exploiting these insights, and show how to improve S&P 500 return forecasts.

In a Lucas exchange economy with standard power utility, we study asset prices under learning and ambiguous information. In contrast with models featuring only learning or ambiguity, our model is successful in matching the equity premium, the interest rate, and the volatility of stock returns under empirically reasonable parameters. Our closed-form formulas also show that a severe downward bias arises in the empirical relation between stock returns and return volatility. We quantify this bias in simulations and show that our model can explain why such a relation is difficult to detect in the data.