High Volatility Regime Research Articles

Anomaly in Stock-Bond Correlations: The Role of Monetary Policy

The paper estimates constant conditional correlation (CCC) GARCH models to test whether the dramatic changes in stock-bond market correlations can be explained by monetary policy variables such as OIS interest rate shocks or volatility regimes. We find that both specifications are empirically relevant: Correlations decrease after positive monetary shocks (decreasing rates) as well as in times with large central bank activity (high rate volatility regimes).

A Markov-Switching Model for Indian Stock Price and Volume

Using weekly data from the Indian stock market, we examine the relationship between stock price and trading volume. Our framework is Markov Switching-Vector Error Correction Model (MS-VECM). We justify the use of nonlinear model using the Brock, Dechert and Scheinkman (BDS) test and the information criteria. The long-run dynamics are characterised by one cointegrating vector relating the price to trading volume. We find that stock price is weakly exogenous only in the high volatility regime. The MS-VECM with two regimes provides a good characterisation of the Indian stock market and performs well relative to the other linear and nonlinear models. JEL Classification: C32, G12, E32

Interactions between real economic and financial sides of the US economy in a regime-switching environment

This objective of this study is to examine the linkages between real (economic) and financial variables in the United States in a regime-switching environment that accounts explicitly for high volatility in the stock market and high stress in financial markets. Since the linearity test shows that the linear model should be rejected, we employ the Markov-switching VECM to examine the same objective using the Bayesian Markov-chain Monte Carlo method. The regime-dependent impulse response function (RDIRF) highlights the increasing importance of the financial sector of the economy during stress periods. The responses and their fluctuations are significantly greater in the high-volatility regime than in the low-volatility regime.

Herding behavior in real estate markets: Novel evidence from a Markov-switching model

Employing a dynamic model that captures herding under different market regimes we provide novel evidence on the herding behavior of US-listed Real Estate Investment Trusts (REITs). Estimates of herding behavior are derived using a Markov regime-switching model. Although static herding model rejects the existence of herding in REITs markets estimates of the regime-switching model reveal substantial evidence of herding behavior under the crash regime for almost all sectors. Most interestingly we observe a shift from negative herding behavior during low and high volatility regimes to positive herding behavior under crash regime for almost all REITs sectors.

Structural Changes in Inflation Dynamics: A Bayesian Analysis Allowing for Multiple Breaks at Different Dates for Different Parameters

I make inferences about complicated patterns of structural breaks in inflation dynamics. I extend Chib's (1998) approach by allowing multiple parameters such as the unconditional mean, a group of persistence parameters, and/or the residual variance to undergo mutually independent structural breaks at different dates with the different number of breaks. Structural breaks are modeled as abrupt changes to identify potential regime shifts in economic structure such as a long-run inflation target, monetary policy, and price-setting behavior. I consider postwar quarterly U.S. inflation rates based on the CPI and the GDP deflator over the period from 1953:Q1 to 2013:Q4. I find that two inflation measures had distinct structural changes in different parameters as well as at different dates using Bayesian model selection procedures. CPI inflation experienced a dramatic drop in persistence around the early 1980s, but GDP deflator inflation is still persistent. In addition, the residual variance for both inflation measures switched from a low volatility regime to a high volatility regime in the early 1970s, but it returned to another low volatility regime at different dates: the early 1980s for GDP deflator inflation and the early 1990s for CPI inflation. The residual variance for CPI inflation has increased again since the early 2000s, while GDP deflator inflation has remained less volatile. These volatility shifts are confirmed by the empirical results based on the unobserved components model with stochastic volatility. However, I do not find evidence of a structural shift in the unconditional mean. When reviewing the recent literature, considerable controversy exists over the structural break in inflation persistence around the early 1980s but this appears to be dependent on the measures of inflation, as highlighted by the empirical findings in this paper.

The role of U.S. subprime mortgage-backed assets in propagating the crisis: Contagion or interdependence?

Though relatively small, the subprime mortgage-backed securities market is often identified as the source of the crisis that swept through the U.S. financial system from 2007 onwards. We investigate if its role in the propagation of the crisis was due to contagion or interdependence. Using a Markov-switching VAR with time-varying transition probabilities, we analyse the transmission of shocks across the financial system. We find little evidence of asset correlation changes between normal and crisis regimes and those that do occur are predominantly associated with liquidity variables. Otherwise, relationships are stable across market conditions, implying that the U.S. financial crisis was due to cross-market interdependencies rather than contagion. There is limited evidence that the deteriorating quality of the underlying assets can explain the transition from ‘normal’ market conditions to a high-volatility regime, although this is not consistent across model specifications.

The Time-Series Linkages between US Fiscal Policy and Asset Prices

This paper studies the interplay of fiscal policy and asset price returns of the United States in a time-varying-parameter vector autoregressive model. Using annual data from 1890 to 2013, we study the effects of dynamic shocks to both fiscal policy and asset returns on asset returns and fiscal policy. Distinguishing between low volatility (bull market) and high volatility (bear market) regimes together with a time-varying-parameter vector autoregressive model enables us to isolate the different size and sign of responses to shocks during different time periods. The results indicate that increases in the primary deficit to GDP ratio decrease house returns over the entire sample and at each impulse horizon. Unlike the house return response, stock returns only decrease in the first year after the fiscal shock, but then increase for the following eight years. Furthermore, the findings show that asset return movements affect fiscal policy, whereby fiscal policy responds more to equity returns than to house returns. The response of fiscal policy to asset returns proves relatively stable and constant over time while controlling for and identifying various asset return regimes. Asset returns respond uniformly to fiscal policy shocks since the 1900's.

Impact of Global Shocks and Volatility on Herd Behavior in an Emerging Market: Evidence from Borsa Istanbul

This paper contributes to the literature on the financial integration in international stock markets by examining the dynamic relationship between global factors and herd behavior in an emerging market. Utilizing a time-varying transition probability Markov Switching model (TVTP-MS), we examine the role of global risk factors on investor behavior in Borsa Istanbul, dominated largely by foreign investors who hold a substantial share of the stocks traded in this market. Our tests yield three distinct market regimes - low, high, and extreme volatility - and evidence consistent with herd behavior during both the high and extreme volatility regimes. U.S. market related factors are found to dominate regime transitions and thus significantly contribute to herd behavior in all market sectors with the exception of industrials, suggesting that industrials are relatively immune from global shocks. Multivariate synchronization tests further suggest that herding regimes are perfectly synchronized across all market sectors, implying that none of the sectors can serve as a leading indicator for switching to (or from) the herding regimes.

Regime switching model of US crude oil and stock market prices: 1859 to 2013

This paper examines the relationship between US crude oil and stock market prices, using a Markov-Switching vector error-correction model and a monthly data set from 1859 to 2013. The sample covers the entire modern era of the petroleum industry, which typically begins with the first drilled oil well in Titusville, Pennsylvania in 1858. We estimate a two-regime model that divides the sample into high- and low-volatility regimes based on the variance–covariance matrix of the oil and stock prices. We find that the high-volatility regime more frequently exists prior to the Great Depression and after the 1973 oil price shock caused by the Organization of Petroleum Exporting Countries. The low-volatility regime occurs more frequently when the oil markets fell largely under the control of the major international oil companies from the end of the Great Depression to the first oil price shock in 1973. Using the National Bureau of Economic research business cycle dates, we also find that the high-volatility regime more likely occurs when the economy experiences a recession.

A regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates

We use the Bayesian Markov-switching vector error correction (MS-VEC) model and the regime-dependent impulse response functions (RDIRFs) to examine the transmission dynamics between oil spot prices, precious metals (gold, silver, platinum, and palladium) spot prices and the US dollar/euro exchange rate. Using daily data from 1987 to 2012, two regimes (low and high volatility regimes) appear to be prevalent for this system. We find evidence that among the five commodity prices the gold prices are the most informative in the group in the high volatility regime, while gold, palladium, and platinum are the most informative in the low volatility regime. Though the platinum and palladium prices impact each other, the impacts in the high volatility regime are asymmetric. In addition to its low correlation in the group, palladium's negative impact on the exchange rate and gold makes it a reliable hedge asset for investors. Gold is the least volatile variable, thus affirming its use as a “safe haven” asset, while silver and oil are the most volatile in the group. Understanding the dynamics of these commodity prices should help investors decide how to invest during periods of low vs. highly volatile regimes.

Impact of the financial crisis on Indian commodity markets: Structural breaks and volatility dynamics

The shocks transmitted across the financial markets during the financial crisis resulted in structural changes in commodity volatility. Hence, understanding volatility dynamics in changing scenario is vital for derivative pricing, risk management and monetary policy stabilization. This paper studies the impact of the financial crisis by analyzing the following aspects: 1.) Presence of structural break/regime shift in volatility by deploying Markov and Wavelet model during financial crisis; and 2.) Crisis impact on volatility dynamic behavior such as persistence, leverage and long memory by deploying hybrid Wavelet-EGARCH and fractional integration. Spot prices of 18 commodities were examined, including all sub-sectors of energy, metals and agriculture; Indian commodity indices & sub-indices, global benchmark indices, and also stock indices such as S&P 500, S&P and VIX, Nifty 50. The results show that there was a shift from low to high volatility regime in commodity market returns during the global financial crisis. The duration of stay in each regime, and the convergence and divergence from long run equilibrium were different across commodities; agricultural commodities showed faster convergence to long run equilibrium while metal and energy experienced higher persistence and attracted more financial speculation. The impact of the crisis on agricultural commodities was limited to internationally traded commodities such as sugar and rubber. The common breaks and different volatility dynamics have been attributed to systematic risk and to idiosyncratic components respectively. It was also found that during and after the crisis, more than idiosyncratic risk, it was systematic risk that contributed significantly to the volatility patterns of Indian commodity markets.

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.

Spatial price transmission on agricultural commodity markets under different volatility regimes

Agricultural commodity markets have been affected by a rise in volatility since 2006. In this context, this paper proposes to analyze the impact of volatility on market linkages for a homogenous commodity. The methodology is based on the cointegration framework. More specifically, Threshold Vector Error Correction Models (TVECM) with two regimes are estimated. Unlike other studies, the regime switch is triggered by the level of volatility and not by trade. The empirical study is done on rapeseed, corn, feed barley and protein pea French markets over 2006–2013. Empirical results show that in a high volatility regime, price deviations from the long term equilibrium are corrected faster and the price equilibration process after a price shock takes less time. Moreover, it appears that the information in the reference (producer) price becomes more relevant for commodities with (without) a futures market when the volatility rises. Nonetheless, these results are more pronounced with an ex ante than an ex post volatility measure.

A regime switching approach for hedging tanker shipping freight rates

Tanker shipping is the primary means for the transportation of petroleum and petroleum products around the world and thus plays a crucial role in the energy supply chain. However, the high volatility of tanker freight rates has been a major concern for market participants and led to the development of the tanker freight derivatives in the form of forward freight agreements (FFAs). The aim of this paper is to investigate the performance of these instruments in managing tanker freight rate risk. Using a data set for six major tanker routes covering the period between 2005 and 2013, we examine the effectiveness of alternative hedging methods, including a bivariate Markov Regime Switching GARCH model, in hedging tanker freight rates. The regime switching GARCH specification links the concept of equilibrium freight rate determination underlying different market conditions and the dynamics of the conditional second moments across high and low volatility regimes. Overall, we find evidence supporting the argument that the tanker freight market is characterized by different regimes. However, while the use of a regime switching model allows for a significant improvement in the performance of the hedge in-sample, out-of-sample results are mixed.

What does financial volatility tell us about macroeconomic fluctuations?

We provide an extensive analysis of the predictive ability of financial volatility for economic activity. We consider monthly measures of realized and implied volatility from the stock and bond markets. In a dynamic factor framework, we extract the common long-run component of volatility that is likely to be linked to economic fundamentals. Based on powerful in-sample predictive ability tests, we find that the stock volatility measures and the common factor significantly improve macroeconomic forecasts of conventional financial indicators, especially over short horizons. A real-time out of sample assessment yields similar conclusions under the assumption of noisy revisions in macroeconomic data. In a nonlinear extension of the dynamic factor model, we identify two distinct volatility regimes, and show that the high-volatility regime provides early signals of the Great Recession, which was associated with severe financial distress and credit disintermediation.

Tug-of-War: Time-Varying Predictability of Stock Returns and Dividend Growth*

We propose a regime-switching present-value model with latent variables to jointly investigate the predictability of stock returns and dividend growth. We find that both return predictability and dividend growth predictability are time-varying. Interestingly, the predictability of stock returns and dividend growth is a tug-of-war contest: when dividend growth is highly predictable in the high-volatility regime, stock returns are largely unpredictable; in contrast, when dividend growth is less predictable in the low-volatility regime, stock returns are significantly predictable. We also investigate macroeconomic determinants of regime switches and find that two regimes are intimately related to macroeconomic risk and economic activity.

Effects of volatility shocks on the dynamic linkages between exchange rate, interest rate and the stock market: The case of Turkey

This study analyzes the dynamic relationship between exchange rate (against US dollar), interest rate and the stock market (both in local currency) of Turkey from January 2003 to September 2013. In particular, the paper tries to answer if the correlations between these important variables change abruptly in high volatile periods and if they do, is this change temporary or permanent? In that manner, we first estimate the dynamic correlations between these variables using the VAR(p)–FIAPARCH(1,d,1)–cDCC(1,1) approach. Then, we endogenously detect the volatility shift dates by a novel method of penalized contrast functions and investigate the relation between the dynamic correlations and the high volatile periods. Results reveal that volatility shocks create abrupt changes in the dynamic correlations, however this effect is only short term and do not sustain between consecutive high volatility regimes. Thus, policymakers and investors do not need to be concerned about long run contagion effects.

Heterogeneous price dynamics in U.S. regional electricity markets

The U.S. electricity wholesale market is organized in several deregulated regional markets. This paper compares price dynamics of electricity in the U.S. wholesale markets and shows that electricity prices from the West and East coasts have different regime dynamics. Our methodology suggests that electricity prices are better parameterized by four regimes with different levels of volatility. Additionally, West and East coast markets differ in the time spent in each regime. The extremely high volatility regime describes West coast prices during the California electricity crisis, but East coast prices are also frequent in that regime. We find evidence of synchronization of price dynamics in the mean-reverting and highest volatility regimes, i.e., prices from the East and West coasts tend to be in the same regimes at the same time.

Emerging market sovereign bond spreads and shifts in global market sentiment

The paper studies how the relationship between emerging market sovereign bond spreads, economic fundamentals and global financial market conditions differs across three regimes of global market sentiment. Following the identification of periods characterized by low, medium and high volatility in financial markets, we analyze the behavior of spreads from three different angles. First, we demonstrate that the cross-country correlation of spreads increases in high-volatility regimes, implying that countries cannot fully decouple from developments in other emerging markets during periods of distress. Second, using the interactions of several domestic and global variables with the probabilities of each regime prevailing in a given period as the explanatory variables of spreads, the fixed effects panel estimation shows that while country-specific fundamentals are important determinants of spreads in each regime, the importance of global financial conditions increases in high-volatility periods. Third, we show that countries can benefit from stronger fundamentals in the form of lower exposure of their sovereign spreads to unfavorable regime shifts in global market sentiment.

Energy price dynamics in the U.S. market. Insights from a heterogeneous multi-regime framework

Energy is a key input in investment decision making with a well-known effect on economic growth. Inelasticity of energy demand urges an understanding of its price dynamics. This paper makes a joint analysis of the price of oil, natural gas, and electricity in U.S. markets using a multi-regime specification that captures the stylized facts of energy prices. Oil and natural gas returns have similar regime dynamics and are well characterized by a high and low volatility regime. Electricity returns have to be parameterized with more regimes: a low volatility, a price spike, and a mean-reverting regime. The California crisis period stands out as a sole regime with extremely high volatility. Our methodology allows the synchronization among regimes to be studied and we find that electricity prices tend to be synchronized in U.S. wholesale markets, while natural gas returns show synchronization with electricity returns in the low volatility regime.