The Profitability of Volatility Trading on Exchange-traded Dollar-rupee Options: Evidence of a Volatility Risk Premium?

This article examines the profitability of short volatility strategies in the exchange-traded USDINR options market. Returns from delta-hedged short positions in straddles, strangles and individual call and put options are examined across different trading horizons and volatility regimes. The study finds that short volatility strategies yield significant mean and median returns regardless of the trading horizon and option moneyness before considering transaction costs. This is suggestive of a volatility risk premium priced in USDINR options. However, the returns are found to be insignificant and even negative after accounting for trading costs such as bid-ask spreads and brokerage. The study concludes that although USDINR options appear to be overpriced because of the volatility risk premium, short option strategies can be profitably exploited only by market makers and institutional investors facing low spreads and funding costs. The findings are suggestive of an informationally efficient market.

Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution

The financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under different high and low return volatility episodes at the 2008 Subprime Crisis and the 2020 Covid-19 outbreak. We carry out an inference process to identify the interactions, in which we implement the a pairwise Ising distribution model describing the first and second moments of the distribution of the discretized returns of each asset. Our results indicate that second-order interactions explain more than 80% of the entropy in the system during the Subprime Crisis and slightly higher than 50% during the Covid-19 outbreak independently of the period of high or low volatility analyzed. The evidence shows that during these periods, slight changes in the second-order interactions are enough to induce large changes in assets correlations but the proportion of positive and negative interactions remains virtually unchanged. Although some interactions change signs, the proportion of these changes are the same period to period, which keeps the system in a ferromagnetic state. These results are similar even when analyzing triadic structures in the signed network of couplings.

Finite maturity American-style stock loans with regime-switching volatility

We study finite maturity American-style stock loans under a two-state regime-switching economy. We present a thorough semi-analytic discussion of the optimal redeeming prices, the values and the fair service fees of the stock loans, under the assumption that the volatility of the underlying is in a state of uncertainty. Numerical experiments are carried out to show the effects of the volatility regimes and other loan parameters. doi:10.1017/S1446181121000250

FINITE MATURITY AMERICAN-STYLE STOCK LOANS WITH REGIME-SWITCHING VOLATILITY

We study finite maturity American-style stock loans under a two-state regime-switching economy. We present a thorough semi-analytic discussion of the optimal redeeming prices, the values and the fair service fees of the stock loans, under the assumption that the volatility of the underlying is in a state of uncertainty. Numerical experiments are carried out to show the effects of the volatility regimes and other loan parameters.

Regime specific spillover across cryptocurrencies and the role of COVID-19

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

Impact of Exchange Rate Volatility to Stocks’ Return in Indonesia: The Augmented Markov-Switching Egarch Approach

The stock price is one indicator that represents the economic performance in a country. Changes in stock prices, including various factors, as an example, is the exchange rate changes as the representation from the foreign exchange market. The fluctuating exchange rate price also influences the volatility of the stock price. Furthermore, volatility has different high and low regime stages that will cause a disparate impact on the outcome of the relationship changes. This study aims to examine the presence of asymmetric volatility and its effects on the volatility of LQ45 stock returns, as well as the changes in exchange rates of Rupiah against USD from 1997 to 2017. Using the Augmented Markov Switching EGARCH approach, the results of this study indicate an asymmetric behavior in the volatility of LQ45 stock returns. High volatility regimes are more dependent and more unstable than low volatility regimes, and low volatility regimes dominate the duration compared to the high volatility regime. The good and bad news give different impact on LQ45 stock return volatility and exchange rate changes. Moreover, the unstable economies will respond faster than the stable economies in terms of facing the exchange rate changes.

Regime-Dependent Good and Bad Volatility of Bitcoin

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

Volatility Prediction Using a Realized-Measure-Based Component Model*

This article introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g., realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the daily return has a multiplicative component structure: the first component traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in volatility. Despite being a fixed-parameter model, its component structure implies time-varying parameters, which are “data-driven” to capture changing volatility regimes. We discuss the model dynamics and estimation by maximum likelihood. The empirical analysis reveals statistically significant out-of-sample gains compared to benchmark models, particularly for short forecast horizons and during the financial crisis.