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Found 13 papers, 0 papers with code
Estimation of VaR with jump process: application in corn and soybean markets
Value at Risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital.
Analysis of optimal portfolio on finite and small-time horizons for a stochastic volatility model with multiple correlated assets
We also generate a close-to-optimal portfolio near the time to horizon using the first-order approximation of the utility function.
Some asymptotics for short maturity Asian options
Most of the existing methods for pricing Asian options are less efficient in the limit of small maturities and small volatilities.
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning
This paper models stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data.
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model.
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging
The model is analyzed in connection to the quadratic hedging problem and some related analytical results are developed.
Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model
At first, we obtain a closed-form formula for an approximation to the optimal portfolio in a small-time horizon.
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters
In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index.
Multi-asset Generalised Variance Swaps in Barndorff-Nielsen and Shephard model
This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved.
First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process
It is shown that with certain probability, the first-exit time process of the log-return is decomposable into the sum of the first exit time of the Brownian motion with drift, and the first exit time of a L\'evy subordinator with drift.
Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets.
Sequential hypothesis testing in machine learning, and crude oil price jump size detection
In this paper we present a sequential hypothesis test for the detection of general jump size distrubution.
Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning
A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model.
