An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading

Abstract

This paper tests using two-regime Markov-switching models with asymmetric, time-varying exponential generalized autoregressive conditional heteroskedasticity (MS-EGARCH) variances in random-length lumber futures trading. By assuming a two-regime context (a low s = 1 and high s = 2 volatility), a trading algorithm was simulated with the following trading rule: invest in lumber futures if the probability of being in the high-volatility regime s = 2 is lower or equal to 50%, or invest in the 3-month U.S. Treasury bills (TBills) otherwise. The rationale tested in this paper was that using a two-regime Markov-switching (MS) algorithm leads to an overperformance against a buy-and-hold strategy in lumber futures. To extend the current literature in MS trading algorithms, two location parameter scenarios were simulated. The first uses an unconditional mean or expected value (no factors), and the second incorporates market and behavioral factors. With weekly simulations form 2 January 1994 to 28 July 2023, the results suggest that using MS-EGARCH models in a no-factors scenario is appropriate for active lumber futures trading with an accumulated return of 158.33%. Also, the results suggest that it is not useful to add market and behavioral factors in the MS-GARCH estimation because it leads to a lower performance.

Number of Volatility Regimes in the Muscat Securities Market Index in Oman Using Markov-Switching GARCH Models

“…Recently De la Torre-Torres et al [15,16] investigated the application of two-regime Markov-switching models featuring asymmetric, time-varying exponential Generalized Autoregressive Conditional Heteroskedasticity (MS-EGARCH) variances within the framework of random-length Lumber Futures trading. They explored a trading strategy based on a two-regime framework (low volatility with s = 1 and high volatility with s = 2), where the decision to invest in Lumber Futures or 3-month U.S. Treasury bills (TBills) depended on the probability of being in the high-volatility regime s = 2 being less than or equal to 50%.…”