Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis

ArXiv ID: 2309.09094 “View on arXiv”

Authors: Unknown

Abstract

Backtest is a way of financial risk evaluation which helps to analyze how our trading algorithm would work in markets with past time frame. The high volatility situation has always been a critical situation which creates challenges for algorithmic traders. The paper investigates different models of sizing in financial trading and backtest to high volatility situations to understand how sizing models can lower the models of VaR during crisis events. Hence it tries to show that how crisis events with high volatility can be controlled using short and long positional size. The paper also investigates stocks with AR, ARIMA, LSTM, GARCH with ETF data.

Keywords: backtesting, position sizing, Value at Risk (VaR), high volatility, GARCH, Equities (Stocks/ETF)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper combines several advanced mathematical models (Kalman Filter, GARCH, ARIMA, LSTM) and statistical tests (Kolmogorov-Smirnov) with an explicit backtesting framework, using specific financial data (ETFs) and risk metrics (VaR).

  flowchart TD
    A["Research Goal<br>Assess Position Sizing Strategies<br>in High Volatility Markets"] --> B["Data & Models<br>Stocks/ETF Data + GARCH, ARIMA, LSTM"]
    B --> C["Methodology<br>Backtesting & VaR Simulation"]
    C --> D{"Computational Process"}
    D --> E["Short Position Sizing"]
    D --> F["Long Position Sizing"]
    E & F --> G["Risk Mitigation Analysis"]
    G --> H["Key Findings<br>Optimized sizing reduces VaR<br>during crisis events"]