Macroeconomic Regimes and Regime Shifts

Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock markets. Similarly, an investor might want to invest in different asset classes in different market regimes for a stable risk adjusted return profile. Here, we explore the use of State Switching Markov Autoregressive models for identifying and predicting different market regimes loosely modelled on the Wyckoff Price Regimes of accumulation, distribution, advance and decline. We explore the behaviour of various asset classes and market sectors in the identified regimes. We look at the trading strategies like trend following, range trading, retracement trading and breakout trading in the given market regimes and tailor them for the specific regimes. We tie together the best trading strategy and asset allocation for the identified market regimes to come up with a robust dynamically adaptive trading system to outperform simple traditional alphas. This paper was created as part of a WorldQuant University degree program towards an MSc in Financial Engineering. This paper is reproduced with the consent and permission of WorldQuant University. All rights reserved.

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“…This further leads to calculation of smoothed probabilities and expected durations. (Hamilton J. D., 2014)…”

Section: Internalmentioning

confidence: 99%

Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

Srivastava

Bhattacharyya

2018

SSRN Journal

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“…This task was accomplished by fitting a SETAR(k) model to each commodity price. The choice of the number of regimes for economic time series can be decided by the framework of the empirical study, and the number of regimes were generally considered as two [see, for example, 34,35,36]. It should be noted that, for the sake of clarity, the number of regimes, k, was fixed to three for both the simulated and real data sets.…”

Section: Application To Commodity Pricesmentioning

confidence: 99%

Temporal clustering of time series via threshold autoregressive models: application to commodity prices

2017

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This study aimed to find temporal clusters for several commodity prices using the threshold non-linear autoregressive model. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. The clustering of commodity prices was examined using the proposed clustering approach based on time series models to incorporate the time varying properties of price series into the clustering scheme. Accordingly, the primary aim in this study was grouping time series according to the similarity between their Data Generating Mechanisms (DGMs) rather than comparing pattern similarities in the time series traces. The approximation to the DGM of each series was accomplished using threshold autoregressive models, which are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time dimension. Furthermore, generating a time varying commodity price index and sub-indexes can become possible. Consequently, we conducted a simulation study to assess the effectiveness of the proposed clustering approach and the results are presented for both the simulated and real data sets.

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“…The research is aligned with the recent literature in structural parameter drifting, or nonlinear behaviour of structural parameters. According to Hamilton (2014), nonlinear mechanisms that trigger macroeconomic regime shifts are one of the most noteworthy contemporary issues in macroeconomics. Contemporary economies are subject to remarkable changes, recurrent crises, recessions, and financial stress.…”

Section: Introductionmentioning

confidence: 99%

Exchange Rate Pass-Through in Brazil: А Markov Switching DSGE Estimation for the Inflation Targeting Period

Marodin¹,

Portugal²

2019

RJMF

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This paper investigates the nonlinearity of exchange rate passthrough in the Brazilian economy during the inflation targeting period (2000-2018) using a Markov Switching new Keynesian DSGE model. We find evidence of two distinct regimes for exchange rate passthrough and for the volatility of shocks to inflation. Under the so-called 'normal' regime, the long-run pass-through to consumer prices inflation is estimated as almost zero, only 0.00057 of a percentage point given a 1% exchange rate shock. In comprasion, the expected pass-through to inflation under a 'crisis' regime is 0.1035 of a percentage point, for the same exchange rate shock. These results allow us to identify four distinct cycles for exchange rate pass-through during the inflation targeting period in Brazil, and suggest that higher central bank credibility and anchored inflation expectations may be related to lower levels of pass-through.

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Macroeconomic Regimes and Regime Shifts

Cited by 20 publications

References 49 publications

Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

Temporal clustering of time series via threshold autoregressive models: application to commodity prices

Exchange Rate Pass-Through in Brazil: А Markov Switching DSGE Estimation for the Inflation Targeting Period

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