High-variance Regime Research Articles

Renewable energy investment under stochastic interest rate with regime-switching volatility

We examine the impact of the interest rate and its characteristics, such as long run mean and instantaneous variance risk (VR), on renewable energy investments in the power sector. The model has stochastic electricity price, stochastic interest rate, and variance regime switches. We show that an increase in the interest rate, while generally increasing the value of a power project, can have a non-monotone effect if the subsidy is sufficiently large. VR increases (reduces) the project value in the high variance regime, if the subsidy is sufficiently large (low). Under a fixed price contract, value declines and it is optimal to delay investment following an increase in the interest rate. The model helps to explain the US offshore industry experience in 2023.

On real interest rate convergence among G7 countries

I analyze real interest rate convergence among six industrialized countries in 1975M1–2011M3 within a multi-country framework by means of a dynamic latent factor model. This approach makes it possible to examine common trending behavior and common transitory movements in the real interest rates. Time-varying variances of the components allow for gradual endogenous transition from a high variance regime toward a low variance regime. The estimation results suggest that four permanent and four transitory components capture the real interest rate dynamics among the sample of G7 countries at the beginning of the sample period. The common components’ variances mostly decline over time, and in part even converge to values close to zero. This indicates a reduction in the number of stochastic factors, which in turn can be interpreted as confirmation of the convergence hypothesis of less cross-country dispersion over time. I observe rapid convergence during the late 1970s and 1980s, followed by slower transition since the beginning of the 1990s when financial markets had already been highly integrated.

The term structure of liquidity premia and the macroeconomy in Canada: A dynamic latent-factor approach

This study derives the term structure of liquidity premia using the stationary vector-stochastic process for the expectation hypothesis of Campbell and Shiller (1987, 1991). The bivariate VAR model identifies liquidity premia for 12 bond yields that are mean-stationary. A state-space representation for a dynamic factor model captures three well-known latent factors for the term premium curve – level, slope, and curvature. The latent factors are able to aggregate the information from the 12 term premia to accurately identify a term premium curve that increases in maturity on average to 117 basis points. The dynamic factor estimations with macroeconomic variables capture important links from the macroeconomy to the term premium factors that indicate the term premium curve responds to higher inflation and interest rates and to higher expectations of future inflation due to excess demand. Regime-switching estimations for the variances of the latent factors stochastically divide the sample period into high- and low-variance regimes. The dynamic factor models for the two regimes indicate that the term premium curve has steepened by almost 50 basis points in the current low-variance regime relative to that in the previous high-variance regime.

The effects of the U.S. business cycle on the Canadian economy: A regime-switching VAR approach

This study assumes a large-closed and small-open economy relationship between the United States and Canadian economies using a seemingly unrelated regression (SUR) system of equations, where the U.S. economy is treated as being an exogenous block and the Canadian economy as the endogenous block. The model is extended to a regime-switching framework to evaluate asymmetries in the systematic responses of the Canadian economy to movements in the business cycle in the U.S. The estimation results suggest that initially over 75 per cent of an innovation in the U.S. output gap is propagated to output in Canada in the high-variance regime and over 55 per cent in the low-variance regime. Nevertheless, the response of the Canadian output gap remains proportional (at about two thirds) to the innovations in the U.S. output gap in both regimes. Over 50 per cent of the covariance between the impulse responses for the output gaps in both countries can be attributed to the U.S. shock in both regimes. However, the response of the output gap in the Canadian economy is much more persistent and stable in the current low-variance regime. Long-run static multipliers for the Canadian output gap suggest that the effects of disturbances in the U.S. business cycle are more than twice as large in the current macroeconomic regime.

The effect of transmission variance on observer placement for source-localization

Detecting where an epidemic started, i.e., which node in a network was the source, is of crucial importance in many contexts. However, finding the source of an epidemic can be challenging, especially because the information available is often sparse and noisy. We consider a setting in which we want to localize the source based exclusively on the information provided by a small number of observers – i.e., nodes that can reveal if and when they are infected – and we study where such observers should be placed.We show that the optimal observer placement depends not only on the topology of the network, but also on the variance of the node-to-node transmission delays. We consider both low-variance and high-variance regimes for the transmission delays and propose algorithms for observer placement in both cases. In the low-variance regime, it suffices to only consider the network-topology and to choose observers that, based on their distances to all other nodes in the network, can distinguish among possible sources. However, the high-variance regime requires a new approach in order to guarantee that the observed infection times are sufficiently informative about the location of the source and do not get masked by the noise in the transmission delays; this is accomplished by additionally ensuring that the observers are not placed too far apart.We validate our approaches with simulations on three real-world networks. Compared to state-of-the-art strategies for observer placement, our methods have a better performance in terms of source-localization accuracy for both the low- and the high-variance regimes.

A comparison of two methods for detecting abrupt changes in the variance of climatic time series

Abstract. Two methods for detecting abrupt shifts in the variance – Integrated Cumulative Sum of Squares (ICSS) and Sequential Regime Shift Detector (SRSD) – have been compared on both synthetic and observed time series. In Monte Carlo experiments, SRSD outperformed ICSS in the overwhelming majority of the modeled scenarios with different sequences of variance regimes. The SRSD advantage was particularly apparent in the case of outliers in the series. On the other hand, SRSD has more parameters to adjust than ICSS, which requires more experience from the user in order to select those parameters properly. Therefore, ICSS can serve as a good starting point of a regime shift analysis. When tested on climatic time series, in most cases both methods detected the same change points in the longer series (252–787 monthly values). The only exception was the Arctic Ocean sea surface temperature (SST) series, when ICSS found one extra change point that appeared to be spurious. As for the shorter time series (66–136 yearly values), ICSS failed to detect any change points even when the variance doubled or tripled from one regime to another. For these time series, SRSD is recommended. Interestingly, all the climatic time series tested, from the Arctic to the tropics, had one thing in common: the last shift detected in each of these series was toward a high-variance regime. This is consistent with other findings of increased climate variability in recent decades.

Information updating and the bounce-back effect of stock market returns

Purpose – Bounce-back effect of stock market returns has been found empirically using different approaches. However, few paper explains the underlying mechanism. The paper aims to discuss these issues. Design/methodology/approach – This paper fills this gap and provides an explanation for bounce-back effect in stock market. Findings – This paper contributes to the literature in threefold. The authors contribute a formal economic model to rationalize the bounce-back effect of stock market returns. It is based on a model of stock return with volatility feedback under the assumption of Markov-Switching market volatility. Originality/value – The authors use the general Markov-Switching bounce-back model, developed by Bec et al. (2015), to provide empirical evidence for the existence of bounce-back effect in stock market. The empirical result shows “W” shape of bounce-back effect, which is exactly the same as predicted by the economic theoretical model. Finally, the authors propose an alternative approach to estimate the magnitude of volatility feedback and the marginal effect on the expected return of an anticipated high variance regime.

Tobin tax and trading volume tightening: a reassessment

This article extends the previous literature on the Tobin tax. We find that very roughly, a doubling in transaction costs would reduce trading volume by 25% to 40% in the Forex. Most importantly, this article is the first contribution to specify the trading volume of the Forex through different (low and high volatility) regimes. Our results show evidence of nonlinear patterns for trading volumes and transaction costs on the Forex. Thus, the Tobin tax would not have a monotonic impact on trading activity across market conditions. The change in elasticity between low and high volatility regimes would be slight but significantly different. We may suggest that the high-variance regime might be the fundamentalist regime and the low-variance regime might be the chartist regime. It is a first step towards understanding which categories of agents would react to the introduction of a tax. Our results seem consistent with Tobin’s underlying thinking; since a tax would penalize chartists more than fundamentalists, it could reduce exchange rate volatility.

Modeling the Natural Gas Spot-futures Markets as a Regime Switching Vector Error Correction Model

While using the jump diffusion system controlled by the Markov-switching technique, this study designs a generalized dynamic conditional correlation (GDCC) time-varying transition probabilities (TVTP) Markov-switching vector error correction model (MRS-VECM) in which the parameters of the error correction term and the spot-futures correlations switches according the phase of volatility regime to reexamine the dynamics of the relationship between the natural gas spot and futures prices. The data used in this study is unleaded gasoline traded on the New York Mercantile Exchange, covering the period November 2, 1994 to June 2, 2009, resulting in 3,642 daily observations. Our empirical results are consistent with the following notions. First, the gasoline market reacts more quickly to the spot-futures price discrepancy when a high variance regime is encountered. Second, the spot-futures correlations vary considerably across various variance states, and the correlation for the high variance state is significantly lower than that of the low variance state. Third, the state-varying hedge ratios by our GDCC-TVTP-MRS-VECM could provide a more efficient futures hedging strategy when compared to conventional hedging models, such as the naïve, VECM, constant conditional correlation multivariate generalized ARCH (CCC-MVGARCH) and dynamic conditional correlation multivariate generalized ARCH (DCC-MVGARCH) models. Finally, the lagged spot-futures price deviation functions as an indicator for the variance-turning process.

Sources of regime switching in short-term interest rates for Canada

The purpose of this study is to identify sources of regime switching in short-term interest rates for Canada. The choice of information variables is based on three well-known hypotheses about interest rates: the expectations hypothesis, the Fisher relationship, and the condition for uncovered interest parity. The empirical framework consists of an autoregressive distributive lag (ADL) model in error-correction form with information spreads and a Markov regime-switching estimation methodology. The models choose interest-rate regimes that are similar in many respects to those found for the USA. The different regimes in Canada are driven by movements in the monetary policy rate, the US short-term rate, and the long-term yield. The monetary policy rate is found to trigger very large changes in the short-term rate, responding one-for-one in the high-variance regime and slightly less in the more ‘normal’ interest-rate regime. Both the US short rate and long-term yield are also found to trigger relatively large changes in short-term rates, but only in the high-variance regime. Inflation, however, is not found to contain additional information about regime switches in interest rates at the short-term horizon.

The Effect of Market Regimes on Style Allocation

We analyse time-varying risk premia and the implications for portfolio choice. Using Markov Chain Monte Carlo (MCMC) methods, we estimate a multivariate regime-switching model for the Carhart (1997) four-factor model. We find two clearly separable regimes with different mean returns, volatilities and correlations. In the High-Variance Regime, only value stocks deliver a good performance, whereas in the Low-Variance Regime, the market portfolio and momentum stocks promise high returns. Regime-switching induces investors to change their portfolio style over time depending on the investment horizon, the risk aversion, and the prevailing regime. Value investing seems to be a rational strategy in the High-Variance Regime, momentum investing in the Low-Variance Regime. An empirical out-of-sample backtest indicates that this switching strategy can be profitable, but the overall forecasting ability for the regime-switching model seems to be weak compared to the iid model.

The dynamics of the relationship between spot and futures markets under high and low variance regimes

AbstractThis investigation is one of the first studies to examine the dynamics of the relationship between spot and futures markets using the Markov‐switching vector error correction model. Three mature stock markets including the U.S. S&P500, the U.K. FTSE100 and the German DAX 30, and two emerging markets including the Brazil Bovespa and the Hungary BSI, are used to test the model, and the differences between the two sets of markets are examined. The empirical findings of this study are consistent with the following notions. First, after filtering out the high variance regime, the futures price is shown to lead the spot price in the price discovery process, as demonstrated by prior studies; conversely, the spot market is more informationally efficient than the futures market under the high variance condition. Second, the price adjustment process triggered by arbitrage trading between spot and futures markets during a high variance state is greater in scale than that based on a low variance state, and the degree of the co‐movement between spot and futures markets is significantly reduced during the high variance state. Third, a crisis condition involved in the high variance state is defined for the two emerging markets, whereas an unusual condition is presented for the three mature markets. Last, the lagged spot–futures price deviations perform as an information variable for the variance‐turning process. However, the portion of the variance‐switching process accounted for by this signal variable is statistically marginal for the three mature markets selected for this study. Copyright © 2008 John Wiley & Sons, Ltd.

The role of permanent and transitory components in the fluctuations of Latin–American real exchange rates

Following the approach suggested by Engel and Kim (1999), we estimate the permanent and transitory components of the real exchange rates in four Latin–American countries for the period 1957:01 to 2002:04. Results suggest that transitory component is the driving force of the real exchange rates in Argentina and Mexico. A principal role of the permanent component is observed in the real exchange rates of Brazil and Chile. Estimates probabilities of the high-variance regime allow to identify the principal events happened in these countries. This information is closely related to nominal shocks and therefore, it explains the significant role of this component in these countries.

The Yield Curve Through Time and Across Maturities

We develop an unobserved component model in which the short-term interest rate is composed of a stochastic trend and a stationary cycle. Using the Nelson-Siegel model of the yield curve as inspiration, we estimate an extremely parsimonious state-space model of interest rates across time and maturity. Our stochastic process generates a three-factor model for the term structure. At the estimated parameters, trend and slope factors matter while the third factor is empirically unimportant. Our baseline model fits the yield curve well. Model generated estimates of uncertainty are positively correlated with estimated term premia. An extension of the model with regime switching identifies a high-variance regime and a low-variance regime, where the high-variance regime occurs rarely after the mid-1980s. The term premium is higher, and more so for yields of short maturities, in the high-variance regime than that in the low-variance regime. The estimation results support our model as a simple and yet reliable framework for modeling the term structure.

The Effect of Market Regimes on Style Allocation

We analyse time-varying risk premia and the implications for portfolio choice. Using Markov Chain Monte Carlo (MCMC) methods, we estimate a multivariate regime-switching model for the Carhart (1997) four-factor model. We find two clearly separable regimes with different mean returns, volatilities, and correlations. In the High-Variance Regime, only value stocks deliver a good performance, whereas in the Low-Variance Regime, the market portfolio and momentum stocks promise high returns. Regime-switching induces investors to change their portfolio style over time depending on the investment horizon, the risk aversion, and the prevailing regime. Value investing seems to be a rational strategy in the High-Variance Regime, momentum investing in the Low-Variance Regime. An empirical out-of-sample backtest indicates that this switching strategy can be profitable, but the overall forecasting ability for the regime-switching model seems to be weak compared to the iid model.