A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study examines dependence of extreme electricity prices in Australian markets.
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this featur…
In an Ultrafast Extreme Event (or Mini Flash Crash), the price of a traded stock increases or decreases strongly within milliseconds. We present a detailed study of Ultrafast Extreme Events in stock market data. In contrast to popular belief, our analysis suggests that most of the Ultrafast Extreme Events are not prima…
Simplifies pricing options in jump-diffusion models using gauge transformations.
The paper calculates extreme measures in continuous time conic finance.
Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evide…
We derive an extremal fractional Gaussian by employing the Lévy-Khintchine theorem and Lévian noise. With the fractional Gaussian we then generalize the Black-Scholes-Merton option-pricing formula. We obtain an easily applicable and exponentially convergent option-pricing formula for fractional markets. We also carry o…
PreBit predicts Bitcoin price movements using social media and financial data.
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
This paper highlights the role of risk neutral investors in generating endogenous bubbles in derivatives markets. We find that a market for derivatives, which has all the features of a perfect market except completeness and has some risk neutral investors, can exhibit extreme price movements which represent a violation…
Improved spread option pricing with a new approximation method.
This paper explores the possibility that asset prices, especially those traded in large volume on public exchanges, might comply with specific physical laws of motion and probability. The paper first examines the basic dynamics of asset price displacement and finds one can model this dynamic as a harmonic oscillator at…
This papers addresses the stock option pricing problem in a continuous time market model where there are two stochastic tradable assets, and one of them is selected as a numéraire. It is shown that the presence of arbitrarily small stochastic deviations in the evolution of the numéraire process causes significant chang…
This study found significant asymmetry between potential maximum gain and loss in asset returns, improving predictability and utility for investors.
Characterizes super-replication prices in a financial market model.
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…
Closed-form pricing method for multi-asset options.
Study tail risk in high-frequency finance using -regularized regression.
Study on price fluctuations and persistence in European electricity spot markets.
Quantum walks model financial returns with flexibility and asymmetry.
Research predicts XRP price anomalies using graph topologies.
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockc…
Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and ar…
Standard economic theory assumes that agents in markets behave rationally. However, the observation of extremely large fluctuations in the price of financial assets that are not correlated to changes in their fundamental value, as well as the extreme instance of financial bubbles and crashes, imply that markets (at lea…
This paper investigates how two important sources of risk -- market tail risk and extreme market volatility risk -- are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of cova…
Hydropower reduces system electricity price and volatility, especially at extreme levels.
DeepTrust uses NLP to quickly identify and verify financial anomalies on Twitter.
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
Quantum theory reinterprets financial pricing by focusing on observable price transitions.
The paper shows real market exists free lunches with vanishing risks.
The paper models stock returns using -Gaussians and negative binomials.
The article models financial asset returns using Gaussian mixtures and EVT-based copulas to price equity options.
We present a new volatility model, simple to implement, that includes a leverage effect whose return-volatility correlation function fits to empirical observations. This model is able to capture both the "retarded effect" induced by the specific risk, and the "panic effect", which occurs whenever systematic risk become…
Model captures asymmetric extreme events in financial returns.
The paper shows that benchmark-neutral pricing minimizes option prices.
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theo…
In this study we examine the evolution of price, volume, and the bid-ask spread after extreme 15 minute intraday price changes on the NYSE and the NASDAQ. We find that due to strong behavioral trading there is an overreaction. Furthermore we find that volatility which increases sharply at the event decays according to …
The paper introduces a new model to improve exotic option pricing.
In this paper, we obtain asymptotic formulas with error estimates for the implied volatility associated with a European call pricing function. We show that these formulas imply Lee's moment formulas for the implied volatility and the tail-wing formulas due to Benaim and Friz. In addition, we analyze Pareto-type tails o…
Polynomial processes have the property that expectations of polynomial functions (of degree , say) of the future state of the process conditional on the current state are given by polynomials (of degree ) of the current state. Here we explore the application of polynomial processes in the context of structur…
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with the minimum and maximum p…
Develops a new model for measuring extremal dependence in financial markets.
Predicts future commodity arrivals using remote sensing data and machine learning.
Study proposes pricing mechanism for cryptocurrency options.
New method simulates multivariate extreme events using GANs and Aitchison coordinates.