Volatility risk premia and exchange rate predictability

Assessing the compensation for volatility risk implicit in interest rate derivatives

Comovement and Volatility Risk Premia

The Cross-Sectional Variation of Volatility Risk Premia

Forward foreign exchange rates and risk premia—a reappraisal

Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions

As the latest addition to the basic equity smart beta factors, the volatility risk premium (VRP) has become an attractive potential source of additional returns for investors. The VRP is generally defined as the difference between the implied volatility of options and the subsequently realized volatility. However, few VRP-based investment products are available that both deliver consistent returns and are low cost. The focus of this article is how investors can incorporate the VRP into typical portfolios, exemplified by a balanced 60/40 portfolio and an equal-weight, multi-asset diversified portfolio. The article explores two different methods, the dedicated VRP construct (long only) and the overlay VRP construct (long–short) and concludes that both methods can potentially enhance investors’ portfolio returns without significantly altering the portfolio’s risk profile because the VRP is an attractive and traditionally untapped source of returns that has exhibited low correlations with traditional risk premiums. The conclusions drawn in the article may be applicable to the task of incorporating other smart beta factors into portfolios.

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities

The Yen/Dollar Carry Trade: Carrying the UIP Condition Too Far?

Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs

Identifying Volatility Risk Premia from Fixed Income Asian Options

Volatility is a very important variable in financial research, and so is the volatility risk premium. This article constructs upward, downward and overall volatility risk premiums based on the daily data of Shanghai 50 ETF options and the 5-minute high-frequency data of the Shanghai 50 Index from February 9, 2015 to February 28, 2020. The quantile regression method is used to predict the return of the Shanghai 50 Index in the next 14 days, 30 days, 60 days, 90 days, 180 days, 270 days, and 360 days. Our study finds that the 0.05 quantile has the best prediction effect, and the overall volatility risk premium is better than the upward and downward volatility risk premiums in most of the time, which shows overall risk premium has more information than others.

The impact of exchange rate (ER) movements on the trade balance has been extensively debated in the literature, with conflicting views on the nature and magnitude of this effect. This paper investigates the relationship between ER movements and trade balance in developing economies, with a specific focus on bilateral analysis involving Pakistan. Employing the Nonlinear Autoregressive Distributed Lag (NARDL) model, our results reveal a symmetric relationship between positive and negative ER changes and the trade balance, showing significant long-term and short-term effects across the panel of developing economies. However, country-specific asymmetries are observed, especially in the cases of India and China, and are more pronounced in each bilateral analysis, whether in the short run or long run. The study findings highlight the critical importance of maintaining a stable exchange rate policy, which can provide policymakers with precise insights into the money market and the broader economy while preventing high volatility and continuous depreciation of domestic currencies. Such measures can mitigate the long-term adverse effects of ER movements on trade agreements. The study offers valuable insights for policymakers and investors in developing economies, aiding them in making informed decisions about exchange rate policies.

We propose to utilize the band spectral regression for out-of-sample forecasts of exchange rates. When one period ahead forecast is considered, there is some evidence that the band spectral regression improves its accuracy, especially when the Taylor rule fundamentals model is employed. However, when the forecasting horizon increases, the purchasing power parity (PPP) fundamentals model is found to be powerful, and we can improve the out-of-sample forecast by selecting appropriate frequency bands. Bayesian model averaging shows that placing a large weight on the business cycle frequency improves the accuracy of the out-of-sample forecasting of the PPP model (as well as the monetary fundamentals model) when a longer forecasting horizon is our focus. Without specifying the frequency bands prior to applying the regression, LASSO can provide better out-of-sample exchange rate forecasts for many cases – most patently for the PPP fundamentals model – and provide information about the dynamic relationship between forecasting variables and exchange rates. The frequency domain approach not only improves the accuracy of exchange rate forecast but provides insights for understanding why the PPP fundamentals act as a powerful predictor when the forecasting horizon increases and there is a possible improvement in the time domain regression forecast.

This paper examines short‐horizon exchange rate predictability and investigates whether stock returns contain information for forecasting daily exchange rate movements. Inspired by the uncovered equity parity condition, we show that stock return differentials have in‐sample and out‐of‐sample predictive power for nominal exchange rates with short horizons (1‐day‐ahead predictions). That is, stock markets inform us about exchange rate movements, at least in the case of high‐frequency data.