arXiv · arXiv q-fin · 2025
Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short-versus long-term trend systems remain controversial. This paper adds t…
Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk
arXiv · arXiv q-fin · 2016
The influence of Commodity Trading Advisors (CTA) on the price process is explored with the help of a simple model. CTA managers are taken to be Kelly optimisers, which invest a fixed proportion of their assets in the risky asset and the remainder in a riskless asset. This requires regular adjustment of the portfolio weights as prices evolve. The CTA trading activity impacts the price change in the form of a power la…
Bernhard K. Meister
arXiv · arXiv · 2023
Constant product markets with concentrated liquidity (CL) are the most popular type of automated market makers. In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools. Their wealth results from fee income, the value of their holdings in the pool, and rebalancing costs. Next, we derive a self-financing and closed-form op…
Álvaro Cartea, Fayçal Drissi, Marcello Monga
arXiv · arXiv · 2019
This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst expone…
Tetsuya Takaishi, Takanori Adachi
arXiv · arXiv · 2009
In this paper we develop a tractable structural model with analytical default probabilities depending on some dynamics parameters, and we show how to calibrate the model using a chosen number of Credit Default Swap (CDS) market quotes. We essentially show how to use structural models with a calibration capability that is typical of the much more tractable credit-spread based intensity models. We apply the structural …
Damiano Brigo, Marco Tarenghi
arXiv · arXiv · 2026
Banks must optimize risky investments, dividend payouts, and capital structure under tight Basel III solvency and liquidity constraints, while costly equity issuance serves as a distress-recovery tool. We formulate this as a stochastic control problem that reduces the high-dimensional balance-sheet dynamics to a tractable one-dimensional process in the asset-to-deposit ratio, with state-dependent investment limits. T…
Erhan Bayraktar, Etienne Chevalier, Vathana Ly Vath, Yuqiong Wang
arXiv · arXiv · 2025
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstru…
Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
arXiv · arXiv · 2019
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded …
Fearghal Kearney, Han Lin Shang, Lisa Sheenan
OpenAlex · The Journal of Business · 2006 · cites 130
One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of Þtting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper we study whe…
Śılvia Gonçalves, Massimo Guidolin
arXiv · arXiv · 2026
I revisit the exchange rate disconnect puzzle, first documented by Meese and Rogoff (1983), using generative artificial intelligence (AI) to forecast currency returns based on economic fundamentals. Using ChatGPT and DeepSeek, I analyze a comprehensive dataset of economic data releases for major currency pairs and measure the fundamental strength of each currency. These AI-powered fundamentals exhibit significant cro…
Amin Izadyar
arXiv · arXiv · 2025
Real-time market prediction services need correct predictions before a decision deadline; a correct prediction delivered late is not usable. TIP-Search studies time-predictable inference scheduling over fixed market predictors under uncertain load. It filters conformal latency-quantile feasible models, dispatches over finite workers, and uses shielded constrained online experts to trade accuracy, queue pressure, and …
Xibai Wang
arXiv · arXiv · 2025
The Nested factor model was introduced by Chicheportiche et al. to represent non-linear correlations between stocks. Stock returns are explained by a standard factor model, but the (log)-volatilities of factors and residuals are themselves decomposed into factor modes, with a common dominant volatility mode affecting both market and sector factors but also residuals. Here, we consider the case of a single factor wher…
Othmane Zarhali, Cecilia Aubrun, Emmanuel Bacry, Jean-Philippe Bouchaud, Jean-François Muzy
arXiv · arXiv · 2025
As operators acting on the undetermined final settlement of a derivative security, expectation is linear but price is non-linear. When the market of underlying securities is incomplete, non-linearity emerges from the bid-offer around the mid price that accounts for the residual risks of the optimal funding and hedging strategy. At the extremes, non-linearity also arises from the embedded options on capital that are e…
Paul McCloud
arXiv · arXiv · 2023
I introduce a survey of economic expectations formed by querying a large language model (LLM)'s expectations of various financial and macroeconomic variables based on a sample of news articles from the Wall Street Journal between 1984 and 2021. I find the resulting expectations closely match existing surveys including the Survey of Professional Forecasters (SPF), the American Association of Individual Investors, and …
Leland Bybee
arXiv · arXiv · 2022
Using new annual data of 16 developed countries across bond, equity, and housing markets, I study the return predictability using the payout-price ratios, i.e., coupon price, dividend price, and rent price. None of the 48 country-asset combinations shows consistent in-sample and out-of-sample performance with positive utility gain for the mean-variance investor. Only 3 (4/2) countries show positive economic gains in …
Yang Bai
arXiv · arXiv · 2022
The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third most traded crude oil futures after WTI and Brent. As China's first crude oil futures accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil future…
Shao Ying-Hui, Liu Ying-Lin, Yang Yan-Hong
arXiv · arXiv · 2021
Stock markets can become inefficient due to calendar anomalies known as day-of-the-week effect. Calendar anomalies are well-known in financial literature, but the phenomena remain to be explored in econophysics. In this paper we use multifractal analysis to evaluate if the temporal dynamics of market returns also exhibits calendar anomalies such as day-of-the-week effects. We apply the multifractal detrended fluctuat…
Darko Stosic, Dusan Stosic, Irena Vodenska, H. Eugene Stanley, Tatijana Stosic
arXiv · arXiv · 2021
Classical portfolio optimization often requires forecasting asset returns and their corresponding variances in spite of the low signal-to-noise ratio provided in the financial markets. Modern deep reinforcement learning (DRL) offers a framework for optimizing sequential trader decisions but lacks theoretical guarantees of convergence. On the other hand, the performances on real financial trading problems are strongly…
Alessio Brini, Daniele Tantari