Position Sizing and VaR in Volatile Market Backtests

Summary

The paper investigates whether trading position sizes can help control risk during periods of high volatility. It uses historical backtesting to examine how sizing models behave during crisis events, with particular attention to whether they can reduce value at risk. It also considers short and long positions as ways to manage exposure under stressed conditions.

The described study combines sizing analysis with stock and exchange-traded fund data, and mentions autoregressive, ARIMA, LSTM, and GARCH models. The excerpt does not explain how sizing rules are defined, how the models are incorporated, or what backtest period and crisis events are used. It also provides no VaR estimates or performance results. The text therefore outlines a research direction rather than establishing that any particular sizing approach controls losses; historical results would also depend on assumptions and may not predict future crisis behavior.

Key ideas

• The study uses historical backtests to assess sizing approaches during high-volatility periods.
• Its central risk measure is value at risk during crisis events.
• It considers managing both long and short position sizes.
• The described data and modeling include stocks, ETFs, and AR, ARIMA, LSTM, and GARCH methods.
• The excerpt gives no results or details sufficient to compare sizing rules.

Tags

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


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









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.

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