Riza Demirer | Southern Illinois University Edwardsville (original) (raw)

Papers by Riza Demirer

Journal of Behavioral Finance

Mathematics

This paper examines the role of non-cash flow factors over correlation jumps in financial markets... more This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies.

Journal of Risk and Financial Management

This paper examines the propagation of oil price uncertainty shocks to real equity prices using a... more This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while simultaneously accounting for individual-country peculiarities. Utilising a recently developed model-free, robust estimate of oil price uncertainty, we document a statistically significant and negative effect of uncertainty shocks emanating from oil prices on the large majority of global stock markets, with the adverse effect of oil price uncertainty shocks found to be stronger for emerging economies as well as net oil-exporting nations. Interestingly, however, global stock markets exhibit a great deal of heterogeneity in their recovery following oil uncertainty shocks as some experience rapid corrections in stock valuations while others suffer from extended slumps. While the results are sensitive to t...

Journal of Forecasting

This paper examines the predictive power of interest rate uncertainty over preprovision net reven... more This paper examines the predictive power of interest rate uncertainty over preprovision net revenues (PPNR) in a large panel of bank holding companies (BHC). Utilizing a linear dynamic panel model, we show that supplementing forecasting models with interest rate uncertainty improves the forecasting performance with the augmented model yielding lower forecast errors in comparison to a baseline model which includes unemployment rate, federal funds rate, and spread variables. Further separating PPNRs into two components that reflect net interest and noninterest income, we show that the predictive power of interest rate uncertainty is concentrated on the non-interest component of bank revenues. Finally, examining the point predictions under a severely stressed scenario, we show that the model can successfully predict the negative effect on overall bank revenues with a rise in the non-interest component of income during 2009:Q1. Overall, the findings suggest that stress testing exercises that involve bank revenue models can benefit from the inclusion of interest rate uncertainty and the cross-sectional information embedded in the panel of BHCs.

Economics Letters

This study examines the role of climate uncertainty over price volatility in the carbon emissions... more This study examines the role of climate uncertainty over price volatility in the carbon emissions market using novel measures of uncertainty that capture transitional and physical climate risks. Applying a multivariate stochastic volatility model to daily European Union Allowance prices, we show that climate uncertainty indeed serves as a significant driver of price fluctuations in emissions prices with physical climate risks associated with uncertainty surrounding natural hazards playing a more dominant role over policy uncertainty in recent years. While our findings highlight the growing role of public concern over global warming and climate hazards than policy aspects as a driver of pricing dynamics in the emissions market, our findings present an interesting opening for hedging strategies towards attaining decarbonization goals in investment positions.

Annals of Financial Economics

Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of u... more Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of uncertainty associated with infectious diseases can be used to predict, both in- and out-of-samples, low-frequency movements of output growth for the United States (US). The predictability of monthly industrial production growth and quarterly real Gross Domestic Product (GDP) growth during the current period of heightened economic uncertainty due to the COVID-19 pandemic is likely to be of tremendous value to policymakers.

SSRN Electronic Journal, 2021

This paper presents a novel perspective on the interaction between equity and currency markets in... more This paper presents a novel perspective on the interaction between equity and currency markets in emerging market economies (EMEs) by (i) examining the nonlinear effects of capital flows on return spillovers between the stock and currency markets in a sample of twelve EMEs via the causality-in-quantiles approach of Balcilar et al., (2016), and (ii) providing a comparative analysis of the influence of debt versus equity flows over the spillover patterns. We show that the causal effects of international debt and equity flows on return spillovers across the equity and FX markets are largely concentrated at lower quantiles, suggesting that the arrival of information via capital flows tends to ease shock transmissions across these markets. At the same time, international flows are found to facilitate the propagation of shocks in the direction of the currency market from the equity market, in line with the portfolio rebalancing hypothesis wherein equity market fluctuations lead to a subsequent correction in the currency market. The findings have important implications for investors and policy makers regarding the role of international capital flows as a facilitator of informational spillovers in emerging equity and currency markets.

Energy Economics, 2022

This study examines the predictive power of the global financial cycle (GFCy) over oil market vol... more This study examines the predictive power of the global financial cycle (GFCy) over oil market volatility using the GARCH-MIDAS framework. The GARCH-MIDAS model provides an appropriate setting to forecast high frequency oil market volatility using global predictors that are only available at low frequency. We show that the global financial cycle carries significant predictive information over both oil market volatility proxies, both in-and out-of-sample. The predictive relationship is found to be positive, more strongly during the pre-GFC period, suggesting that rising global asset prices coupled with improved cross-border capital flows are associated with rising volatility in the oil market. While the GARCH-MIDAS model incorporating GFCy or any other proxy of global financial/economic conditions yields economic gains compared to the conventional GARCH-MIDAS-RV specification, especially in the pre-GFC period; the stance is found to be robust to risk aversion and leverage ratio. The economic gains observed from the GFCy-based model particularly during the pre-GFC period when world markets experienced a steady rise in global asset prices and cross-border capital flows underline the potential role of risk appetite (or behavioural factors) in forecasting applications. Overall, our results suggest that incorporating low frequency proxies of global asset market conditions can provide significant forecasting gains for energy market models, with significant implications for both investors and policymakers.

Mathematics, 2021

This paper introduces a new methodology to estimate time-varying alphas and betas in conditional ... more This paper introduces a new methodology to estimate time-varying alphas and betas in conditional factor models, which allows substantial flexibility in a time-varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time-varying parameter model where innovations of the state equation have a spike-and-slab mixture distribution. The mixture distribution specifies two states with a specific probability. In the first state, the innovation variance is set close to zero with a certain probability and parameters stay relatively constant. In the second state, the innovation variance is large and the change in parameters is normally distributed with mean zero and a given variance. The latent state is specified with a threshold that governs the state change. We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the para...

Energy Economics, 2021

This paper contributes to the literature on forecasting the realized volatility of oil and gold b... more This paper contributes to the literature on forecasting the realized volatility of oil and gold by (i) utilizing the Infinite Hidden Markov (IHM) switching model within the Heterogeneous Autoregressive (HAR) framework to accommodate structural breaks in the data and (ii) incorporating, for the first time in the literature, various sentiment indicators that proxy for the speculative and hedging tendencies of investors in these markets as predictors in the forecasting models. We show that accounting for structural breaks and incorporating sentimentrelated indicators in the forecasting model does not only improve the out-of-sample forecasting performance of volatility models but also has significant economic implications, offering improved risk-adjusted returns for investors, particularly for short-term and mid-term forecasts. We also find evidence of significant cross-market information spilling over across the oil, gold, and stock markets that also contributes to the predictability of short-term market fluctuations due to sentiment-related factors. The results highlight the predictive role of investor sentimentrelated factors in improving the forecast accuracy of volatility dynamics in commodities with the potential to also yield economic gains for investors in these markets.

This paper provides a novel perspective to the predictive ability of credit rating announcements ... more This paper provides a novel perspective to the predictive ability of credit rating announcements over stock market returns and volatility using a novel methodology that formally distinguishes between different market states that can be characterized as bull, bear and normal market conditions. Using data on the credit rating announcements published by the three wellestablished credit rating agencies and data on BRICS and PIIGS stock markets, we show that the stock markets react heterogeneously, and in quantile-specific patterns, to ratings announcements with more persistent and widespread effects observed for PIIGS stock markets. The effect of rating announcements is generally stronger and more widespread in the case of volatility of returns, implying significant risk effects of these announcements. Finally, we show that the results of the aggregate ratings are driven mostly by rating upgrades rather than downgrades, implying asymmetry in the predictive ability of ratings announcemen...

In this paper, we analyze the predictive role of firm-level business expectations and uncertainti... more In this paper, we analyze the predictive role of firm-level business expectations and uncertainties derived from a panel survey of U.S. 1,750 business executives from 50 states for the realized variance (sum of daily squared log-returns over a month) of the S&P500 over the monthly period of September, 2016 to July, 2021. Unlike standard models, our predictive framework adopts a timevarying approach due to the existence of multiple structural breaks in the relationship between volatility and the predictors in the model, which in turn leads to statistically insignificant causal effects in a constant parameter setup. Our time-varying results reveal the predictive power of firm-level business uncertainty is concentrated during the early part of the sample associated with the U.S.-China trade war, and towards the end of our data coverage in the wake of the outbreak of the COVID-19 pandemic. Since, in-sample predictability does not guarantee the same over an out-sample, we also conducted a full-fledged forecasting exercise to show that subjective expectations and uncertainties associated with sales growth rates and employment produces statistically significant predictability gains over January, 2020 to July, 2021, given an in-sample of September, 2016 to December, 2019. Our results suggest that subjective measures of business uncertainty at the firm-level indeed captures predictive information regarding aggregate stock market uncertainty which has important implications for investors and economic projections at the policy level.

Applied Economics Letters, 2021

This paper establishes a predictive relationship between financial vulnerability and volatility i... more This paper establishes a predictive relationship between financial vulnerability and volatility in emerging stock markets. Focusing on China and India and utilizing GARCH-MIDAS models, we show that incorporating financial vulnerability can substantially improve the forecasting power of standard macroeconomic fundamentals (output growth, inflation and monetary policy interest rate) for stock market volatility. The findings have significant implications for investors to improve the accuracy of volatility forecasts.

This paper examines the fundamental linkages between stock markets and safe haven assets by devel... more This paper examines the fundamental linkages between stock markets and safe haven assets by developing a two-factor, regime-based volatility spillover model with global and regional stock market shocks as risk factors. The risk exposures of safe havens with respect to global and regional shocks are found to display significant time variation and regime-specific features, with the exception of VIX for which consistent negative risk exposures are observed with respect to both global and regional shocks. While traditional safe havens like precious metals exhibit positive risk exposures to both regional and global shocks during high volatility periods, Swiss Francs, Japanese Yen and U.S. Treasuries are found to display either insignificant or negative risk exposures during market stress periods to equity market shocks, implying these assets would serve as more effective hedges or safe havens for equity investors. Our findings highlight the importance of dynamic models in assessing the l...

Economics and Business Letters, 2021

We propose a dynamic, forward-looking hedging strategy to manage stock market risks via positions... more We propose a dynamic, forward-looking hedging strategy to manage stock market risks via positions in REITs, conditional on the level of risk aversion. Our findings show that REITs do not only offer significant risk reduction for passive portfolios, but also offer much improved risk-adjusted returns with the greatest benefits observed for Australia, Canada and the U.S. Overall, our findings suggest that time-varying risk aversion can be utilized to (i) establish effective hedges against stock market risks via positions in REITS, and (ii) improve the risk-return profile of passive portfolios.

$Research paper thumbnail of Do Bivariate Multifractal Models Improve Volatility Forecasting in Financial Time Series? An Application to Foreign Exchange and Stock Markets$

This paper examines volatility linkages and forecasting for stock and foreign exchange (FX) marke... more This paper examines volatility linkages and forecasting for stock and foreign exchange (FX) markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model (MSM) that accounts for possible interactions between stock and FX markets. Examining daily data from the advanced G6 and emerging BRICS nations, we compare the out-of-sample volatility forecasts from GARCH, univariate MSM and bivariate MSM models. Our findings show that the GARCH model generally offers superior volatility forecasts for short horizons, particularly for FX returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer forecast horizons, consistently across most markets. Finally, the bivariate MF model provides superior forecasts compared to the univariate alternative in most G6 countries and more consistently for FX returns, while its benefits are limited in the case of emerging markets. Overall, our findings suggest that multifractal mo...

Theoretical and Applied Climatology, 2021

We extend the literature on the effect of rare disaster risks on commodities by examining the eff... more We extend the literature on the effect of rare disaster risks on commodities by examining the effect of the El Niño-Southern Oscillation (ENSO) on crude oil via the recently developed kth order nonparametric causality-in-quantiles framework, utilizing a long range historical data set spanning the period 1876:01 to 2020:10. The methodology allows us to test for the predictive role of ENSO over the entire conditional distribution of not only real oil returns but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and regime-changes. Empirical findings show that the Southern Oscillation Index (SOI), measuring the ENSO cycle, not only predicts real oil returns, but also volatility, over the entirety of the respective conditional distributions. The findings highlight the role of rare disaster risks over not only financial markets, but also commodities with significant implications for policymakers and investors.

Scottish Journal of Political Economy, 2021

Using high-frequency (daily) data on macroeconomic uncertainties and the partial crossquantilogra... more Using high-frequency (daily) data on macroeconomic uncertainties and the partial crossquantilogram approach, we examine the directional predictability of disentangled oil-price-shocks for the entire conditional distribution of uncertainties of five advanced economies (Canada, Euro Area, Japan, the United Kingdom, and the United States). Our results show that oil-demand, supply, and financial risk-related shocks can predict the future path of uncertainty; however, the predictive relationship is contingent on the initial level of macroeconomic uncertainty and the size of the shocks. Our results suggest that macroeconomic uncertainty is indeed predictable at high frequency, and that oil-price-shocks capture valuable predictive information regarding the future path of macroeconomic uncertainties.

Proceedings of the 48th International Academic Conference, Copenhagen, 2019

Decomposing the term structure of U.S. treasury yields into two components, the expectations fact... more Decomposing the term structure of U.S. treasury yields into two components, the expectations factor and the maturity premium, we examine whether the U.S. term structure contains predictive information over emerging stock market volatility. Based on data from 20 emerging markets, we provide positive evidence that holds even after controlling for country specific factors including turnover and market size. Our findings indicate the market's expectation of future short term rates, implied by the expectations factor, serves as a stronger predictor of stock market volatility compared to the maturity premium component of the yield spread. Moreover, the predictive power of the U.S. term structure increases following the global financial crisis.