Return Determinants of Option Strategies: Evidence from Protective Put and Covered Call

Abstract

This study compares the performance of protective put and covered call strategies and analyzes their return determinants. The analysis uses SET50 Index Options contracts with trading volume, covering maturities from January 2021 to December 2025. The empirical model investigates three groups of explanatory variables: market expectation variables (implied volatility and basis), option market condition variables (open interest and trading volume), and option-specific characteristics (time to maturity and moneyness). The model also incorporates fixed effects for different maturity years (with 2025 as the base year) and quarterly maturity dummies. Standard errors are clustered by monthly expiration groups, and statistical significance is further validated using the wild cluster bootstrap method to improve the reliability of p-values. Overall, the findings indicate that the covered call strategy outperforms the protective put strategy over the sample period, except in 2025. Option strategy performance is primarily driven by market expectation variables rather than contract-specific characteristics. Implied volatility and the basis are the most important determinants of returns for both protective put and covered call strategies, while option market condition variables are relevant mainly for covered call strategies. These results highlight the importance of market conditions in shaping hedging strategy outcomes in the Thai options market.

1. Introduction

Financial markets have become increasingly volatile in recent years, driven by global economic uncertainty, monetary policy shifts, and geopolitical tensions. Such heightened volatility amplifies downside risk for investors and reinforces the need for effective risk management strategies. In this environment, derivatives play a critical role by enabling investors to hedge against adverse price movements while maintaining exposure to potential returns.

In Thailand, the derivatives market has developed continuously since the establishment of the Thailand Futures Exchange (TFEX) on 17 May 2004. The market introduced SET50 Index Futures on 28 April 2006, followed by SET50 Index Options on 29 October 2007. Despite the availability of both instruments for nearly two decades, trading activity remains heavily concentrated in futures contracts. In practice, SET50 Index Options exhibit significantly lower trading volume and liquidity compared to SET50 Index Futures, with options accounting for less than 5% of total SET50 Index derivatives trading volume. This disparity is noteworthy given that options offer several distinct advantages, particularly their ability to create asymmetric payoff structures. Unlike futures, which expose investors to both gains and losses symmetrically, options allow investors to limit downside risk while preserving upside potential. Moreover, options provide flexibility in constructing strategies tailored to different market views and risk preferences.

The limited use of options in the Thai market highlights the need for greater understanding of option-based strategies and their practical benefits. Among the most commonly discussed strategies are the protective put and the covered call, both of which combine positions in the underlying asset with options but serve different investment objectives.

The protective put strategy involves holding a long position in the underlying asset while purchasing a put option to insure against downside risk. This strategy establishes a price floor, effectively limiting potential losses while allowing investors to benefit from upward price movements. In contrast, the covered call strategy involves holding the underlying asset and selling a call option to generate premium income. While this approach enhances returns in stable or moderately bullish markets, it caps the upside potential of the portfolio in exchange for the income received. These two strategies therefore reflect distinct risk-return trade-offs. The protective put prioritizes downside protection at a cost, whereas the covered call emphasizes income generation with limited upside.

Despite extensive literature on option-based strategies, existing studies have largely focused on developed markets where options are highly liquid and widely adopted (Israelov and Nielsen 2014; Basson et al. 2018; Niblock and Sinnewe 2018; Guo and Loeper 2021; El-Hassan et al. 2021; Blanc et al. 2025). These studies typically rely on data from major derivatives exchanges, such as the Chicago Board Options Exchange (CBOE) and other well-established markets, where trading activity is deep and continuous. As a result, their findings may not fully generalize to emerging markets characterized by lower liquidity and different trading environments. Empirical evidence on the performance of protective put and covered call strategies in emerging markets, particularly in Thailand, remains limited. This study therefore examines the returns of these strategies in the Thai derivatives market, with a particular focus on how market conditions and option characteristics influence their performance. The findings are expected to provide practical insights for investors and contribute to a more comprehensive understanding of the application of option-based strategies in emerging markets such as Thailand.

The remainder of this paper is organized as follows. Section 2 reviews relevant empirical studies. Section 3 describes the data and methodology used to analyze the determinants of protective put and covered call strategy returns. Section 4 reports the empirical results and discusses the findings. Section 5 concludes the paper.

2. Literature Review

Option-based strategies, particularly protective put and covered call strategies, continue to receive significant attention in the finance literature because of their potential to reduce portfolio risk and improve investment performance. Prior empirical studies examine these strategies with respect to their risk–return profiles, hedging effectiveness, and performance across different market conditions.

Empirical evidence on covered call strategies generally supports their favorable risk-adjusted performance. Early studies, such as Board and Sutcliffe (1998), demonstrate that covered call strategies can reduce return variance. More recent evidence across different markets reinforces these findings. Dewobroto et al. (2010) examine the best stock hedging strategies by employing ANOVA and independent samples t-test on the stock option portfolio in Dow Jones industrial average. They propose the covered call strategy as the most effective stock hedging strategy. Israelov and Nielsen (2014) examine the CBOE S&P 500 BuyWrite Index (BXM) and suggest that equity index covered calls appeal to many investors because they have delivered returns close to those of the broader equity market, but with significantly lower volatility. Niblock and Sinnewe (2018) show that covered call strategies applied to Australian equities generate returns comparable to buy-and-hold portfolios but with lower volatility, particularly when using out-of-the-money options. Similarly, Foltice (2022) finds that covered call strategies consistently outperform the U.S. market on a risk-adjusted basis, even after accounting for transaction costs and taxes. While most existing literature focuses on covered call strategies that short a single option with a one-to-one write–buy ratio, Díaz and Kwon (2017) introduces a risk–return optimization framework that determines both the optimal strike prices and the number of call options to sell. The results show that the optimal strategy is closely linked to the call options’ risk premiums. Across all tested risk measures, the findings suggest that, from a risk–return perspective, it is often optimal to sell a mix of call options with different strike prices rather than concentrating on a single strike.

In contrast, empirical findings on protective put strategies are less favorable. Early evidence from Figlewski et al. (1993) evaluate the performance of the protective put strategy and indicate that when the market is expected to earn less than the riskless asset, it may be better to liquidate a stock position rather than protect it by buying puts. More recent studies support this view. Israelov (2019) argues that protective puts frequently provide limited practical downside protection. They may even worsen outcomes by increasing drawdowns and volatility per unit of expected return. Similarly, Kedžo and Šego (2021) evaluate return distributions using stochastic dominance criteria up to the third degree, which is appropriate for investors who prefer higher returns, are averse to downside risk and losses, and favor positive skewness. The results based on simulated returns indicate that portfolios hedged with options, such as the protective put strategy, are never dominated by unhedged portfolios. Foltice (2022) finds that protective put strategies not only yield lower returns but also significantly increase the probability of monthly losses. However, this finding contrasts with Dash and Goel (2014), who examine option strategies using stocks listed on the National Stock Exchange of India over a single expiration period from October to December 2007. Their results show that both protective put and covered call strategies outperform a simple buy-and-hold position, with protective puts generally delivering superior performance. The study further highlights that the relative performance of these strategies depends on moneyness and underlying stock characteristics, with out-of-the-money strategies performing better for stocks with higher average returns and lower volatility, while in-the-money strategies are more suitable for lower-return, higher-volatility stocks.

Beyond comparing overall performance, Misra and Dalmia (2007) emphasize that option strategy returns are strongly influenced by specific option characteristics and market conditions. Evidence from NSE Nifty options shows that both covered call and protective put portfolios can outperform unhedged portfolios in terms of risk and return, although covered calls tend to deliver higher returns while protective puts provide lower risk. Importantly, returns depend on factors such as option moneyness, time to maturity, liquidity, and open interest. Covered call performance improves with higher strike prices, shorter maturities, and more liquid contracts, whereas protective put performance is enhanced by lower strike prices, longer maturities, and higher open interest. These findings highlight that strategy outcomes are driven not only by the choice of strategy but also by how the strategy is implemented. In addition, S P et al. (2022) examine the effectiveness of hedging strategies, including covered call, covered put, collar, and synthetic long call, in the National Stock Exchange of India (NSE) over a 12-year period from 2009 to 2020. The results suggest that covered call and covered put strategies provide effective hedging in both volatile and normal market conditions. In addition, stock returns, option premiums, and market condition dummy variables are found to significantly influence strategy returns.

Overall, prior empirical studies suggest that covered call strategies can enhance income generation and improve risk-adjusted returns, while the effectiveness of protective put strategies as hedging instruments appears to vary across market conditions and trading environments. Existing studies primarily examine factors such as time to maturity, moneyness, liquidity, and volatility in explaining option strategy performance. However, most evidence is derived from developed markets characterized by relatively high liquidity and mature derivatives infrastructures. Empirical research on emerging markets remains limited, particularly regarding how market-specific conditions influence option strategy returns.

This study extends the existing literature by incorporating the basis as an additional explanatory variable, which has received limited attention in prior research on option strategies. The basis may capture pricing discrepancies between spot and futures markets, reflecting market expectations, hedging pressure, and inefficiencies that are more pronounced in emerging markets. Therefore, including this variable may provide additional insight into the determinants of option strategy returns in the Thai derivatives market.

3. Materials and Methods

This section outlines the data selection process, the construction of the hedging portfolios, and the empirical methodology used in the analysis.

3.1. Data

This study examines and compares the returns of option-based risk management strategies, specifically the protective put and covered call strategies, through the simulation of hedged portfolios using historical data on SET50 Index Options and their underlying asset. Transaction costs and other trading-related fees are excluded.

The simulation employs daily data on SET50 Index call and put option contracts with expiration months from January 2021 to December 2025, comprising a total of 60 expiration months. However, only contracts with observable trading volume are included, while those with zero trading volume are excluded. This exclusion is necessary because contracts with zero trading volume do not provide observable closing option premiums required for the empirical analysis. Including such contracts could introduce incomplete price information, thereby reducing the reliability of the dataset. The final sample comprises 28 expiration months for put options and 27 expiration months for call options, most of which are concentrated in quarterly expiration months.

3.2. Portfolio Construction, Option Selection, and Return Measurement

The hedging portfolio consists of a long position in the underlying asset combined with either the purchase of a European put option (protective put strategy) or the sale of a European call option (covered call strategy). The initiation date of each hedging portfolio is defined as any trading day (t = 0) on which the option contract records positive trading volume. To avoid portfolios with extremely short holding periods, option trades initiated on the contract’s last trading day are excluded from the sample. Each portfolio is then held until the option’s expiration date (T).

Rather than selecting a single contract on each trading day, the analysis includes all option contracts with positive trading volume that satisfy the sample criteria. Consequently, multiple hedging portfolios may be initiated on the same trading day when several eligible option contracts with different strike prices (E) and maturities are simultaneously traded. In addition, the same option contract may appear in multiple portfolio observations across different initiation dates prior to expiration, provided that the contract continues to satisfy the sample criteria. This approach avoids imposing an arbitrary contract selection rule and allows the analysis to incorporate the full cross-section of actively traded option contracts available in the market.

As a result, the sample comprises approximately 22,868 portfolio observations for the protective put strategy and 21,453 portfolio observations for the covered call strategy. This difference arises because put options exhibit non-zero trading volume on more trading days, allowing for a greater number of portfolio initiations.

The initial underlying price (S₀) is proxied by the SET50 Index closing price on the portfolio initiation date, while the terminal price (S_T) is defined as the settlement price on the contract’s final trading day. Call and put option premiums (C₀ and P₀) are measured using closing prices on the portfolio initiation dates. Portfolio returns (π) are computed based on the profit structures of the protective put and covered call strategies at expiration, as defined in Equations (1) and (2), respectively.

π = (S_T − S₀) + [Max(0, E − S_T) − P₀],

(1)

π = (S_T − S₀) − [Max(0, S_T − E) − C₀],

(2)

Because option profits are constructed on the SET50 index, strategy performance is expressed in SET50 index points and used as a proxy for portfolio returns throughout the analysis.

3.3. Empirical Methodology

To analyze the factors influencing the returns of option-based risk management strategies, this study employs an ordinary least squares (OLS) regression with year fixed effects and clustered standard errors using a repeated cross-sectional dataset. Each observation varies according to the portfolio initiation date, the option’s expiration date, the strike price, and the level of the SET50 Index. Year fixed effects are included to control for unobserved time-specific factors that may affect returns across different market conditions. Specifically, five years (2021–2025) are considered, and four year dummy variables (DY₂₀₂₁–DY₂₀₂₄) are included in the model, with 2025 serving as the reference category. Quarterly expiration dummies (DQ₁–DQ₄) are also included in the regression as control variables to account for differences across expiration cycles, with non-quarterly expiration months serving as the reference category. Explanatory variables are categorized into three groups:

• Market expectations are proxied by implied volatility (IV) and the basis (B), reflecting forward-looking information about future market conditions. Implied volatility reflects market expectations of future price uncertainty and directly affects option premiums, which are important determinants of the profitability of covered call and protective put strategies. The basis captures the relationship between spot and futures prices and may reflect market sentiment, hedging demand, and temporary pricing inefficiencies. Although the basis has received limited attention in prior option strategy literature, it may provide additional insight into strategy performance, particularly in emerging markets where deviations between spot and futures prices may be more pronounced.
• Option market conditions are measured by open interest (OI) and trading volume (TV) on the portfolio initiation date, reflecting differences in liquidity and trading activity across contracts. These variables are included because variations in contract-level market activity may influence option pricing and strategy returns through differences in investor participation, market depth, and price discovery. Differences in liquidity and trading activity across contracts may therefore contribute to variations in strategy performance. Consistent with this focus on actively traded contracts, the analysis is restricted to options with non-zero trading volume.
• Option-specific characteristics are captured by time to maturity (TM) and moneyness (M). Time to maturity affects the sensitivity of option prices to time decay and changing market conditions, while moneyness reflects the relationship between the strike price and the underlying asset price, influencing the likelihood of option exercise and the risk-return profile of the strategies. Together, these variables help explain differences in option strategy returns across contracts in the Thai derivatives market.

Table 1. Variable Categories, Definitions, and Measurements.

Category	Variable	Abbreviation	Measurement/Definition
Market Expectations	Implied Volatility	IV	Black–Scholes implied volatility (%)
	Basis	B	Percentage difference between the spot price and futures price relative to the spot price (%)
Option market conditions	Open Interest	OI	Number of outstanding option contracts at portfolio initiation (contracts)
	Trading Volume	TV	Number of option contracts traded on the portfolio initiation date (contracts)
Option-specific characteristics	Time to Maturity	TM	Number of days from portfolio initiation to option expiration, divided by 365 (years)
	Moneyness	M	Natural logarithm of the ratio of the underlying index level at initiation to the option’s strike price
Control Variables	Quarterly Expiration	DQ1–DQ4	Four quarterly dummy variables equal to 1 if the option expires in the corresponding quarter and 0 otherwise
	Year Fixed Effects	DY2021–DY2024	Four yearly dummy variables equal to 1 if the option expires in the corresponding year (2021–2024) and 0 otherwise; 2025 is the reference year

presents each variable’s definition, measurement, and category. Pairwise correlations among independent variables are also examined using a correlation matrix to assess potential multicollinearity.

The baseline empirical models are specified as follows:

$${\pi }^{PP}={\alpha }_0^{PP}+{\alpha }_1^{PP}IV+{\alpha }_2^{PP}B+{\alpha }_3^{PP}OI+{\alpha }_4^{PP}TV+{\alpha }_5^{PP}TM+{\alpha }_6^{PP}M+{\displaystyle {\displaystyle \sum _{i=1}^4}{\gamma }_i^{PP}D{Q}_i}+{\displaystyle {\displaystyle \sum _{j=2021}^{2024}}{\beta }_j^{PP}D{Y}_j}+\epsilon$$

(3)

$${\pi }^{CC}={\alpha }_0^{CC}+{\alpha }_1^{CC}IV+{\alpha }_2^{CC}B+{\alpha }_3^{CC}OI+{\alpha }_4^{CC}TV+{\alpha }_5^{CC}TM+{\alpha }_6^{CC}M+{\displaystyle {\displaystyle \sum _{i=1}^4}{\gamma }_i^{CC}D{Q}_i}+{\displaystyle {\displaystyle \sum _{j=2021}^{2024}}{\beta }_j^{CC}D{Y}_j}+\epsilon$$

(4)

Equation (3) presents the OLS regression for the protective put strategy (PP), while Equation (4) reports the corresponding specification for the covered call strategy (CC). Standard errors are clustered at the option expiration level to account for intra-cluster correlation in the repeated cross-sectional data. Given the relatively small number of clusters (28 clusters for the protective put strategy and 27 clusters for the covered call strategies), the study uses cluster-robust standard errors with the CR2 (bias-reduced linearization) adjustment to reduce finite-sample bias in variance estimation. Statistical significance is further assessed using the wild cluster bootstrap with 9999 replications, providing robust p-values despite limited clusters. Clustering inherently accounts for heteroskedasticity and within-cluster correlation, so no additional tests for these conditions are required.

4. Results and Discussion

Table 2. Descriptive Statistics.

Panel A: Protective Put Strategy (Observations = 22,868)
Variable	Mean	Std. Dev.	Min	Max
π	−4.3452	38.543	−135	218.38
IV	20.5080%	9.2950%	0.0228%	198.8147%
B	0.4911%	0.3816%	−0.4334%	2.6752%
OI	2697.72	3367.09	0	26,748
TV	257.0553	467.8357	1	10,435
TM	0.2174	0.1344	0.0027	0.5068
M	0.0547	0.0877	−0.3023	0.4113
Panel B: Covered Call Strategy (Observations = 21,453)
Variable	Mean	Std. Dev.	Min	Max
π	−2.3217	44.2132	−201.92	155.68
IV	13.4761%	8.5734%	0.0009%	254.6424%
B	0.5022%	0.3839%	−0.4334%	2.6752%
OI	2283.79	3168.55	0	20,055
TV	240.1221	446.4227	1	7054
TM	0.2223	0.1364	0.0027	0.5068
M	−0.0257	0.0766	−0.3803	0.3581

Note: This table reports summary statistics for the protective put (Panel A) and covered call (Panel B) strategies over the sample period 2021–2025. The sample consists of 22,868 observations for Panel A and 21,453 observations for Panel B.

presents summary statistics for strategy returns and explanatory variables for the protective put strategy (Panel A) and the covered call strategy (Panel B). The protective put strategy generates a mean return of −4.3452 index points with a standard deviation of 38.543 index points, while the covered call strategy yields a higher mean return of −2.3217 index points with a slightly higher dispersion of 44.2132 index points.

For the protective put strategy, implied volatility (IV) averages 20.5080%, ranging from 0.0228% to 198.8147%, indicating substantial variation in market uncertainty. Basis (B) has a mean of 0.4911%, close to zero, suggesting that futures prices are closely aligned with spot prices and indicating relatively efficient and balanced market conditions. Open interest (OI) averages 2698 contracts, while trading volume (TV) averages 257 contracts per day. Time to maturity (TM) averages 0.2174 years, indicating short-term option maturities concentrated around quarterly horizons. Moneyness (M) has a mean of 0.0547, ranging from −0.3023 (in-the-money, ITM) to 0.4113 (out-of-the-money, OTM), indicating that the sample includes in-the-money (ITM), at-the-money (ATM), and out-of-the-money (OTM) put options, with the slightly positive mean reflecting a greater proportion of OTM put options in the sample.

For the covered call strategy, implied volatility (IV) averages 13.4761%, lower than the put market, with a wide range from 0.0009% to 254.6424%. Basis (B) remains similar at 0.5022%, also close to zero. Open interest (OI) averages 2284 contracts, while trading volume (TV) averages 240 contracts per day. Time to maturity (TM) averages 0.2223 years, similar to the put strategy. Moneyness (M) averages −0.0257, ranging from −0.3803 (OTM) to 0.3581 (ITM), indicating that the sample includes OTM, ATM, and ITM call options, with the slightly negative mean reflecting a greater proportion of OTM call options in the sample.

Overall, both the protective put and covered call strategies yield negative average expiration profits over the 2021–2025 sample period. Implied volatility, trading volume, and open interest are generally higher in put options than in call options. Both hedging strategies are primarily constructed using OTM options, with maturities concentrated around quarterly horizons.

As shown in

Table 3. Comparative Performance of Buy-and-Hold, Protective Put, and Covered Call Strategies over the Sample Period (2021–2025), in Index Points.

Year	Buy & Hold	Protective Put	Covered Call
2021	28.8375	10.2005	20.85
2022	4.5814	−4.8867	6.0957
2023	−32.2519	−25.7534	−18.2252
2024	9.4380	3.3001	2.9074
2025	−26.0653	−4.9337	−15.543
Average	−4.2908	−4.3452	−2.3217

Source: Author’s results.

, the covered call strategy generally produces higher profits than the protective put strategy during 2021–2023. While the two strategies exhibit similar performance in 2024, the protective put strategy outperforms the covered call strategy during a negative market environment in 2025. This result is consistent with prior literature, including Dewobroto et al. (2010) and Foltice (2022), which suggests that covered call strategies generally outperform protective put strategies, as premium income from selling call options provides a steady return component, whereas protective puts involve higher hedging costs. The negative average profits over the full sample period are primarily driven by market conditions in 2023 and 2025, the only two years in which the buy-and-hold, protective put, and covered call strategies all record negative average profits. Although the buy-and-hold strategy generates positive profits in 2021, 2022, and 2024, the average profit over the full sample period remains approximately −4.2908 index points. Over the same period, the protective put strategy records an average profit of approximately −4.3452 index points, while the covered call strategy records a less negative average profit of approximately −2.3217 index points. These results suggest that the covered call strategy partially offsets adverse market performance through option premium income, whereas the protective put strategy performs similarly to the buy-and-hold benchmark over the full sample period. The relative performance of the two strategies reflects their asymmetric payoff structures under different market conditions. The covered call strategy generally produces less negative average profits because premium income from selling call options partially offsets portfolio losses during periods of weak or moderately positive market performance. In contrast, the protective put strategy requires recurring premium payments for downside protection, which reduce profits when market declines are insufficient to generate substantial payoff from the put overlay.

Table 4. Mean Returns by Moneyness Category for Protective Put and Covered Call Strategies.

Strategy	ITM	ATM	OTM
Protective Put	−5.0890 (6529)	−10.2960 (5)	−4.0460 (16,334)
Covered Call	−0.7444 (7831)	12.2029 (7)	−3.2364 (13,615)

Note: This table reports mean returns by moneyness category in index points. The number of observations in each category is reported in parentheses.

reports mean returns across moneyness categories for both strategies. The distribution of observations is concentrated in OTM options for both strategies, with 16,334 OTM observations compared to 6529 ITM and 5 ATM observations for protective puts, and 13,615 OTM observations compared to 7831 ITM and 7 ATM observations for covered calls. Given the very small number of ATM observations, the analysis focuses primarily on the comparison between ITM and OTM options. For the protective put strategy, ITM options generate lower average returns (−5.0890 index points) than OTM options (−4.0460 index points), indicating that higher hedging costs associated with ITM puts reduce overall performance. In contrast, for the covered call strategy, OTM options yield lower returns (−3.2364 index points) compared to ITM options (−0.7444 index points), reflecting the limited premium income received from OTM calls.

Table 5. Correlation Matrix of Explanatory Variables for the Protective Put Strategy.

	IV	B	OI	TV	TM	M
IV	1.0000	0.0415	0.0947	−0.0756	−0.2612	0.2381
B	0.0415	1.0000	−0.3104	−0.1445	0.4804	0.0440
OI	0.0947	−0.3104	1.0000	0.4844	−0.4716	0.3174
TV	−0.0756	−0.1445	0.4844	1.0000	−0.2754	0.0545
TM	−0.2612	0.4804	−0.4716	−0.2754	1.0000	0.0707
M	0.2381	0.0440	0.3174	0.0545	0.0707	1.0000

and

Table 6. Correlation Matrix of Explanatory Variables for the Covered Call Strategy.

	IV	B	OI	TV	TM	M
IV	1.0000	−0.1312	0.1470	0.0221	−0.2236	−0.2890
B	−0.1312	1.0000	−0.3080	−0.1740	0.4804	−0.0775
OI	0.1470	−0.3080	1.0000	0.5845	−0.4754	−0.2426
TV	0.0221	−0.1740	0.5845	1.0000	−0.2985	−0.0733
TM	−0.2236	0.4804	−0.4754	−0.2985	1.0000	−0.1174
M	−0.2890	−0.0775	−0.2426	−0.0733	−0.1174	1.0000

present the correlation matrices for all explanatory variables used in the regression analysis for the protective put and covered call strategies, respectively. The results show that the highest correlation coefficients do not exceed 0.5 for protective puts and 0.6 for covered calls, suggesting the absence of serious multicollinearity concerns. The strongest correlation is observed between open interest (OI) and trading volume (TV), reflecting their close relationship as measures of market activity. Overall, the correlations remain within acceptable ranges, and all variables are retained in the subsequent OLS regression with year fixed effects and clustered standard errors.

Table 7. OLS Regression Results for Protective Put and Covered Call Strategies.

Variables	Protective Put	Covered Call
Constant	−20.3522 *	−21.6621 **
	(10.4801)	(10.4582)
	[0.1233]	[0.1588]
IV	0.5026 **	1.2199 ***
	(0.1817)	(0.3931)
	[0.0183]	[0.0010]
B	14.9189 **	33.2111 **
	(7.0238)	(12.4918)
	[0.0901]	[0.0019]
OI	−0.0001	−0.0015 *
	(0.0006)	(0.0008)
	[0.8291]	[0.1350]
TV	0.0026	0.0028 *
	(0.0020)	(0.0014)
	[0.2620]	[0.0501]
TM	−5.7298	−59.0460
	(33.9067)	(43.4784)
	[0.8749]	[0.3650]
M	−4.5014	6.1789
	(23.9969)	(45.6776)
	[0.8690]	[0.8982]
Quarterly Expiration Dummies	Yes	Yes
Year Fixed Effects	Yes	Yes
R2	0.2448	0.3298
Observations	22,868	21,453

Note: This table reports OLS regression results for the protective put and covered call strategies. The dependent variable is strategy return at expiration. All regressions include year fixed effects and quarterly expiration dummy variables. Standard errors are computed using the CR2 method and reported in parentheses. Bootstrap p-values are reported in brackets. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, based on CR2 standard errors.

reports the OLS regression results for the protective put and covered call strategies. Standard errors are computed using the CR2 adjustment to account for within–option expiration dependence and are reported in parentheses. The model explains approximately 24.48% and 32.98% of the variation in returns for the protective put and covered call strategies, respectively. Option-specific characteristics, including time to maturity (TM) and moneyness (M), are not statistically significant for either strategy, suggesting that variation in returns is less driven by contract-level features. In addition, option market condition variables, namely open interest (OI) and trading volume (TV), play a role in the covered call strategy through their impact on premium income. In contrast, key market expectation variables, particularly implied volatility and the basis, have a significant effect on returns for both strategies.

Implied volatility (IV) is positively associated with returns for both strategies, significant at the 5% level for protective puts and at the 1% level for covered calls, indicating that higher expected market volatility enhances strategy performance. The positive effect of implied volatility suggests that market uncertainty plays an important role in determining the profitability of option strategies in the Thai derivatives market. Economically, implied volatility reflects investors’ expectations regarding future market risk and anticipated price fluctuations. During periods of heightened uncertainty, investors increase their demand for options for both hedging and income-generation purposes. For the protective put strategy, higher implied volatility increases the value of downside protection, which may improve returns during adverse market conditions. For the covered call strategy, higher implied volatility raises the premium received from selling call options, thereby increasing option income and overall strategy performance.

Similarly, the basis (B) is positively associated with returns for both strategies, with statistical significance at the 5% level. The positive coefficient of basis also provides important economic implications. A higher basis reflects a more negative market outlook on the SET50 index, where futures prices are lower than spot prices. Under such conditions, the value of put options increases, improving the effectiveness of hedging and enhancing returns for the protective put strategy. At the same time, a weaker market environment reduces the likelihood that call options will be exercised, allowing the covered call strategy to retain premium income while limiting losses from the underlying position. In addition, the strong explanatory power of basis may reflect the important informational role of the SET50 index futures market in Thailand. Because the SET50 futures market has substantially higher trading volume and liquidity than the options market, futures prices may incorporate market information and investor sentiment more rapidly. As a result, the basis may serve as a leading indicator of market expectations and derivatives pricing conditions, helping explain its significant relationship with option strategy profitability.

These findings may also reflect the characteristics of the Thai derivatives market, where the options market is relatively less liquid and has lower market depth compared with more developed markets. In such an environment, investors may rely more heavily on volatility expectations and information from the SET50 futures market when pricing and implementing option strategies. Consequently, implied volatility and basis may exert stronger effects on the profitability and performance of option strategies in the Thai market context.

Option market conditions variables, including open interest (OI) and trading volume (TV), exhibit significance only for the covered call strategy. OI is negatively associated with returns at the 10% significance level, while TV shows a positive effect at the 10% significance level. The negative coefficient on OI suggests that higher outstanding positions may reflect more competitive or crowded option markets, which compress option premiums and reduce profitability for covered call strategies. In contrast, higher trading volume indicates greater market activity and liquidity, particularly in actively traded SET50 Index call options with popular strike prices and expiration months. Such contracts are easier to trade, exhibit higher liquidity, and command higher option premiums, benefiting option sellers and leading to improved returns for the covered call strategy. The absence of significant effects for these variables in the protective put strategy suggests that liquidity conditions play a more limited role in determining returns for hedging-focused positions, where performance is less dependent on premium expenses and more driven by the effectiveness of downside protection rather than trading activity. While option prices (and thus premiums) still matter, returns are primarily determined by whether the hedge pays off during adverse market movements, rather than by trading activity or market liquidity.

While most results remain consistent, some coefficients exhibit weaker significance based on bootstrap p-values. In particular, the coefficient on OI for the covered call strategy, which is significant at the 10% level under CR2 standard errors, becomes statistically insignificant when using bootstrap p-values, suggesting some sensitivity in the inference.

Overall, the results suggest that market expectation factors, particularly implied volatility and the basis, play a central role in determining the performance of option-based strategies, while option-specific characteristics such as time to maturity and moneyness have limited explanatory power. Option market condition variables, including open interest and trading volume, are relevant only for the covered call strategy, reflecting the importance of premium income and trading activity for option-selling strategies. These findings are broadly consistent with prior literature, such as S P et al. (2022), which emphasizes the role of volatility and market conditions in shaping option strategy performance. However, the results differ from Misra and Dalmia (2007), who find that option returns are strongly influenced by contract-specific characteristics, including moneyness and time to maturity. One possible explanation for this difference is the exclusion of zero-trading-volume contracts from the sample. In the relatively low-liquidity Thai options market, illiquid contracts may be concentrated in certain moneyness and maturity categories. Excluding these contracts may reduce cross-sectional variation within the sample and thereby limit the ability to detect statistically significant relationships between these characteristics and strategy profitability. Although this study finds limited evidence for the effects of moneyness and time to maturity, it provides partial support for their findings by showing that open interest and trading volume significantly affect covered call strategy returns. In contrast, Misra and Dalmia (2007) report that liquidity and open interest also influence protective put portfolios in the NSE Nifty options market. A possible explanation for this difference is that the performance of protective put strategies is primarily driven by downside risk protection rather than premium-related factors. Unlike covered call strategies, which generate returns through option premium income and are therefore sensitive to market liquidity, protective puts function as insurance against adverse market movements, making their returns less dependent on trading activity and more dependent on market expectations.

5. Conclusions

Option-based hedging strategies such as covered calls and protective puts play an important role for investors in managing risk and generating returns in the Thai equity options market. This study analyzes the performance of these strategies and examines how market conditions and contract characteristics influence returns over the 2021–2025 period.

Across the sample period, covered call strategies generally outperform protective put strategies, with the exception of 2025. Within the protective put strategy, in-the-money options generate lower average returns than out-of-the-money options, reflecting the higher cost of downside protection. In contrast, for covered call strategies, out-of-the-money calls yield lower returns than in-the-money calls, indicating that lower premium income limits overall performance. The empirical results further show that option strategy performance is driven mainly by market expectation variables rather than contract-specific characteristics. Implied volatility and the basis are the most important determinants of returns, while moneyness and time to maturity have limited explanatory power across strategies. Option market condition variables, such as open interest and trading volume, matter only in specific cases, particularly for covered call strategies where returns are more closely linked to premium collection and trading activity.

Overall, the results indicate that strategy performance is driven primarily by prevailing market conditions rather than by individual contract characteristics. In particular, returns are closely associated with market expectation variables, notably implied volatility and the basis, underscoring their central role as determinants of returns in option-based strategies. The positive relationship observed suggests that higher levels of implied volatility and a stronger basis are associated with improved returns for protective put and covered call strategies. From an investment perspective, this implies that such conditions may provide a more favorable environment for their implementation, highlighting the importance of monitoring volatility and pricing signals when evaluating potential returns.

This study is subject to several limitations. First, zero-trading-volume contracts were excluded from the sample because these contracts do not provide observable closing option premiums required for the empirical analysis. However, given the relatively low liquidity of the Thai options market, this filtering criterion may introduce sample selection bias, as actively traded contracts may systematically differ from illiquid contracts in terms of moneyness, maturity, and investor demand. As a result, the findings may primarily reflect the behavior of relatively liquid option contracts rather than the overall market. Future research could explore methods for incorporating illiquid option contracts to assess whether the exclusion of zero-trading-volume contracts significantly affects the results. In addition, comparative studies across different markets or asset classes may help determine whether the observed relationships are specific to the Thai equity options market or hold more broadly. Future studies may also incorporate macroeconomic variables or regime-switching approaches to improve understanding of how changing market conditions influence option strategy performance over time.