The dynamic conditional correlation (DCC) and co-range models are two main frameworks widely used to employ range-based univariate volatility. Using the two approaches, we build novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, as well...

In this paper, I identify the long-run risks (LRR) in the frequency domain, and further estimate the macro (consumption) risk premia in different frequency ranges. To achieve the identification, I employ the long-run projections and the associated inference procedure for I(0) process developed...

In this paper, we show that the availability of multiple price series for the same asset can be exploited to estimate its integrated variance. We use a vector error correction model for those prices and its common trend representation to estimate the efficient price of the asset. Because the...

In recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter H0.5. In this paper,...

This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress...

The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their...

The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their...

The main contribution of the paper is proving that the Fourier spot volatility estimator introduced in [Malliavin and Mancino, 2002] is consistent and asymptotically efficient if the price process is contaminated by microstructure noise. Specifically, in the presence of additive microstructure...

We study why widely used low-frequency liquidity cost estimators based on high, low, and close prices perform well in some markets and poorly in others, often yield negative or indeterminate estimates, and how to quantify estimation bias empirically. Using the high-low spread estimator as our...

The prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes -- using a statistical term -- the...