Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic processes and volatility is typically inferred from return dynamics. Prediction markets have a different structure: prices are bounded probabilities, payoffs are binary, and contracts resolve at known deadlines. We develop and estimate a volatility model tailored to binary prediction markets. The model combines two economic mechanisms: a Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time, and a Glosten-Milgrom order-flow component, capturing volatility from informed trading as reflected in spreads and volume. Using a large panel of Kalshi contracts, we show that these structural variables carry substantial forecasting power. Plain ARCH/GARCH benchmarks are dominated by structural specifications; combining the structural model with residual GARCH dynamics gives the best overall forecasts. The model also provides an interpretable measurement framework: volatility is highest near fifty-fifty prices, rises near resolution, and varies across categories with the timing and discreteness of information arrival. Economics contracts are closer to smooth deadline-resolution dynamics, while sports contracts exhibit more event-concentrated, jump-like behavior. Across major categories, category-specific fitting does not systematically improve out-of-sample performance, suggesting that the structural specification transfers beyond the pooled headline result.
This paper investigates the dynamics of the implied volatility surface (IVS) and its term structure as a foundation for constructing profitable trading strategies in the options market. Contrary to the classical assumption of constant volatility in the Black–Scholes model, empirical evidence demonstrates that implied volatility systematically varies with strike price and time to expiration, thereby forming a three-dimensional surface. The study provides a comparative analysis of two key dimensions of the IVS – the term structure and the strike structure (skew) – with a particular focus on the phenomenon of volatility time shift. Based on econometric modelling employing the Nelson–Siegel factorization and principal component analysis, we propose a system of trading signals for calendar spreads, skew arbitrage, and dynamic hedging. The empirical dataset comprises options on the 500 index (SPX) over the period 2020–2025. The results indicate that incorporating the temporal dynamics of the IVS improves the risk-adjusted returns of trading strategies by 15–22% relative to strategies that rely solely on level information.
A. A. Mishin· EKONOMIKA I UPRAVLENIE: PROB...· 0 citations
Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricing, and regulatory capital. In this paper, we examine whether volatility forecasts improve when models incorporate information on how markets are connected, and whether the choice of connection measure matters. Using daily data on 29 equity indices from every major region over 2015-2025, we let each market's forecast draw on the recent volatility of the markets linked to it, with the strength of each link set either by geography, return correlation, or the Diebold-Yilmaz (DY) spillover network estimated from the data. The spillover-informed forecasting model achieved an approximately 13\% reduction in out-of-sample QLIKE loss relative to the standard Heterogeneous AutoRegressive benchmark ($p<0.001$) and attained the highest model confidence set $p$-value among the models considered. Several benchmark models that do not explicitly incorporate cross-market structure were excluded from the 90\% model confidence set. The greatest improvements were observed during periods of elevated market stress: the COVID-19 crash, the Russian invasion of Ukraine, and the $2025$ US tariff shock, spillover-informed forecasts achieved approximately 21\% lower QLIKE loss than an otherwise identical network-blind model ($p = 0.018$), with no significant performance loss during calm periods. The advantage is economically material: a volatility-targeting investor would pay $322$-$373$ basis points per year, net of transaction costs, for spillover-informed forecasts, versus an insignificant $85$-$102$ basis point for the alternatives. Finally, when the model is left to infer connections on its own, it recovers the DY network from the forecast objective alone (permutation $p=0.0005$).
Commodity futures are shaped by harvest cycles, weather shocks, storage conditions, and seasonal demand, but it remains unclear whether recurring patterns yield robust out-of-sample trading profits. Existing research documents return seasonality in commodity futures as well as more complex seasonal structure, while leaving less evidence on how alternative seasonal models compare under common implementation constraints. This article compares dummy-variable regression (DVR), Singular Spectrum Analysis (SSA), and robust low-rank SSA (RLSSA) within a unified trading framework, including a volatility-normalised specification. Using monthly delivery-avoidance returns for 15 liquid commodity futures, the models are estimated on rolling ten-year windows and evaluated from 2016 to 2024 with transaction costs, an equal-weight long benchmark, and Maximum Entropy Bootstrap (MEB) assessment. Across 500 MEB paths, the benchmark has the strongest average full-period profile, with a cumulative return of 16.81%, a Sharpe ratio of 0.191, and a maximum drawdown of -0.414. Classical SSA has the strongest average model outcomes, but negative median cumulative returns and deep drawdowns indicate substantial path sensitivity. Volatility normalisation reduces the average DVR short loss but generally weakens SSA-based portfolios. None of the 18 approximate paired MEB Sharpe tests rejects after within-family Holm adjustment. Because MEB preserves each contract's temporal rank ordering, the assessment is conditional on observed timing rather than a timing-randomised seasonal null. The evidence does not establish robust benchmark outperformance and shows that model performance varies materially across market subperiods.
Volatility is a fundamental characteristic of financial markets and plays a crucial role in investment decision-making, portfolio management, and financial risk assessment. Understanding the behaviour of stock market volatility is particularly important for frontier markets such as the Nepal Stock Exchange (NEPSE), where market fluctuations may differ from those observed in developed economies. This study investigates the volatility dynamics and forecasting performance of the NEPSE Index using daily closing prices from March 2021 to March 2026. Daily logarithmic returns were analysed following tests for stationarity and conditional heteroskedasticity. The volatility process was modelled using the symmetric GARCH(1,1) and asymmetric EGARCH(1,1) models, while model adequacy and forecasting performance were evaluated through diagnostic tests, information criteria, rolling-window out-of-sample forecasting, the News Impact Curve, volatility half-life estimation, and structural break analysis. The empirical findings confirm that NEPSE returns exhibit the stylized characteristics of financial time series, including volatility clustering, leptokurtosis, and persistent conditional volatility. The EGARCH model provides a marginally better in-sample fit and identifies significant asymmetric volatility, with negative market shocks producing stronger increases in future volatility than positive shocks of similar magnitude. However, the GARCH(1,1) model demonstrates slightly superior out-of-sample forecasting performance, indicating that the simpler specification remains more reliable for short-term volatility prediction. The analysis also identifies a structural break in July 2022, suggesting that volatility dynamics changed during the study period and that persistence estimates should be interpreted with caution. By integrating model comparison, forecasting evaluation, News Impact Curve analysis, volatility persistence, and structural break testing within a single analytical framework, this study provides a comprehensive assessment of NEPSE volatility and offers practical insights for investors, portfolio managers, policymakers, and future researchers interested in frontier equity markets.
This study investigates the volatility dynamics of commodity import prices in Republic of Moldova and global Brent crude oil prices, employing advanced econometric models to enhance understanding of risk in small open economies and energy markets. Utilizing monthly data from 1992 to 2025 for Republic of Moldova's Commodity Import Price Index and from January 1990 to March 2025 for Brent oil, the analysis confirms that both series are integrated of order one, with no significant structural breaks in the mean, supporting constant-parameter modelling. For Republic of Moldova's Commodity Import Price Index, an AR (1)-GARCH (1,1) specification effectively captures high volatility persistence and symmetric shock responses, indicating long-memory effects without asymmetry. In contrast, Brent oil exhibits leverage effects, where negative shocks amplify volatility more than positive ones, best demonstrated via AR(1)-EGARCH(1,1). Comprehensive diagnostics, including ADF tests, Bai-Perron structural break analysis, ARCH-LM, Ljung-Box, and Nyblom stability tests, validate model adequacy, confirming residual whiteness and structural stability. These findings underscore the need for symmetric risk management in Republic of Moldova's import sector and asymmetric hedging in oil markets, with implications for macroeconomic stability and policy formulation. Research gaps highlight opportunities for multivariate spillover analysis and regime-switching models to address cross-market dependencies.
Mircea Diavor, Ion Pârțachi· Scientific Annals of Economi...· 0 citations
