Quantile estimation with adaptive importance sampling

We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed. Using a new law of iterated logarithm for martingales, we prove the convergence of the adaptive quantile estimators for general distributions with nonunique quantiles thereby extending the work of Feldman and Tucker [Ann. Math. Statist. 37 (1996) 451--457]. We illustrate the algorithm with an example from credit portfolio risk analysis.Comment: Published in at http://dx.doi.org/10.1214/09-AOS745 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Asset Pricing Under The Quadratic Class

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 semi­closed form. We further illustrate how the quadratic model and the transform methods can be applied to more general settings.quadratic class; interest rates; term structure models; state price density; Markov process.

Design and Estimation of Quadratic Term Structure Models

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 two­factor model that approximates these three layers of properties well, and illustrate how the model can be applied to pricing interest rate derivatives.quadratic model; term structure; positive interest rates; humps; expectation hy­pothesis; GMM; caps and floors.

Sentiment spin: Attacking financial sentiment with GPT-3

In this study, we explore the susceptibility of financial sentiment analysis to adversarial attacks that manipulate financial texts. With the rise of AI readership in the financial sector, companies are adapting their language and disclosures to fit AI processing better, leading to concerns about the potential for manipulation. In the finance literature, keyword-based methods, such as dictionaries, are still widely used for financial sentiment analysis due to their perceived transparency. However, our research demonstrates the vulnerability of keyword-based approaches by successfully generating adversarial attacks using the sophisticated transformer model, GPT-3. With a success rate of nearly 99% for negative sentences in the Financial Phrase Bank, a widely used database for financial sentiment analysis, we highlight the importance of incorporating robust methods, such as context-aware approaches such as BERT, in financial sentiment analysis

Thus spoke GPT-3: Interviewing a large-language model on climate finance

This paper is an interview with a Large Language Model (LLM), namely GPT-3, on the issues of climate change. The interview should give some insights into the current capabilities of these large models which are deep neural networks with generally more than 100 billion parameters. In particular, it shows how eloquent and convincing the answers of such LLMs can be. However, it should be noted that LLMs can suffer from hallucination and their responses may not be grounded on facts. These deficiencies offer an interesting avenue for future research

The Monetary Benefit of Tokenizing Renewable Energy

Capital markets remain skewed toward short-term financial gains at the expense of long-term value creation. As a result, renewable energy investments remain viable and accessible only to a narrow set of investors under the traditional financial system. Moreover, current green finance offerings fall short of their allocation targets, and attempts at introducing ESG measures to tilt the scale in favor of sustainable investments are typically too limited to tackle the issue. Therefore, we evaluate the potential of tokenization, an innovative financing tool, to raise more capital for renewable energy projects situated in Africa that remain insufficiently funded to meet global sustainability goals

Short-run Risk, Business Cycle, and the Value Premium

We jointly explain the variations of the equity and value premium in a model with both short-run (SRR) and long-run (LRR) consumption risk. In our preliminary empirical analysis, we find that SRR varies with the business cycle and it has a substantial predictive power for market excess returns and the value premium|both in-sample and out-of-sample. The LRR component also differs significantly from zero, and value stocks have a larger exposure to both LRR and SRR than growth stocks. To explain these patterns in asset returns, we propose an extended and analytically tractable LRR model

Asset Pricing under the Quadratic Class

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 setting

Efficient Calibration of Trinomial Trees for One-Factor Short Rate Models

In this paper we propose a computationally efficient implementation of general one factor short rate models with a trinomial tree. We improve the Hull-White's procedure to calibrate the tree to bond prices by circumventing the forward rate induction and numerical root search algorithms. Our calibration procedure is based on forward measure changes and is as general as the Hull-White procedure, but it offers a more efficient and flexible method of constructing a trinomial term structure model. It can be easily implemented and calibrated to both prices and volatilitie