{"id":16416,"date":"2022-05-30T16:46:51","date_gmt":"2022-05-30T14:46:51","guid":{"rendered":"https:\/\/quantpedia.com\/?p=16416"},"modified":"2025-06-04T13:56:32","modified_gmt":"2025-06-04T11:56:32","slug":"introduction-and-examples-of-monte-carlo-strategy-simulation","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/introduction-and-examples-of-monte-carlo-strategy-simulation\/","title":{"rendered":"Introduction and Examples of Monte Carlo Strategy Simulation"},"content":{"rendered":"

The Monte Carlo method (Monte Carlo simulations) is a class of algorithms that rely on a repeated random sampling to obtain various scenario results. Monte Carlo simulations are used to predict the probability of different outcomes when it would be difficult to use other approaches such as optimization. The main aim is to create alternative scenarios, which account for possible risk and help with decision making. The simulations are used in various fields, from finance and quantitative analysis to engineering or science. We plan to unveil our new \u201cMonte Carlo\u201d report for Quantpedia Pro<\/a> clients in a next few days, and this article is our introduction to different methodologies that can be used for Monte Carlo calculation.<\/strong><\/p>\n\n\n\n

Monte Carlo Simulations are a useful tool both for risk management and portfolio management. Risk managers may calculate a proper budget for a potential quantitative investment strategy, whereas portfolio managers may set their expectations more precisely and easily perform a sensitivity analysis of their strategies. Monte Carlo simulations are an option to create endless alternative scenarios to test your strategies in. Their main advantage is that they reach beyond historical data and rather alter the history artificially.<\/p>\n\n\n\n

Underlying Strategy<\/span><\/h2>\n\n\n\n

In our analysis, we applied Monte Carlo simulations to a quantitative investment strategy. More specifically we used a trading strategy called a Return Asymmetry Investment factor in commodity futures<\/a>. The strategy is based on the idea that the greater (lower) upside asymmetry is associated with lower (higher) expected returns, analyzed in numerous theoretical studies<\/a>.<\/p>\n\n\n\n

Quantpedia\u2019s commodity futures strategy aims to examine the return asymmetry in commodity futures in a slightly less traditional way. Instead of using skewness<\/a> as a proxy for the return asymmetry, we rely on a new asymmetric measure IE proposed by Jiang et al.<\/a> (2020), which uses the difference between upside and downside return probabilities to capture the degree of asymmetry.<\/p>\n\n\n\n

As for the investment universe, the strategy follows Pady\u0161\u00e1k and Vojtko<\/a> (2019). Their dataset of commodities offers broad exposure to the commodity market. The portfolio is constructed on the asymmetric measure as follows. At the beginning of each month, each commodity’s asymmetry (IE) is calculated using the latest 260 daily returns. Then the commodities are ranked according to their IE. The portfolio goes long on the bottom four commodities with the lowest IE in the previous month and short on the top four commodities with the highest IE in the last month (ties in ranks can change these).<\/p>\n\n\n\n

Monte Carlo strategy simulation types<\/span><\/h2>\n\n\n\n

There are several types of Monte Carlo applications in quantitative trading strategies. We will focus on different ways of randomization of strategy\u2019s returns. In this article we cover the most practical and most widely used ones:<\/p>\n\n\n\n